Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space (2022.emnlp-main)

Challenge:	Existing methods for detecting dialogue contradictions are difficult due to contextualization nature of conversations.
Approach:	They propose a benchmark for Contradiction Detection in Chinese Conversations . they use automatic conversation generation to simulate common user behaviors .
Outcome:	The proposed benchmark simulated the user behaviors that trigger chatbots to make contradictions . the results show that the current state-of-the-art chatbot can be easily goaded into making contradictions.

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Mor Geva, Avi Caciularu, Kevin Wang, Yoav Goldberg

Learning to Generate Question by Asking Question: A Primal-Dual Approach with Uncommon Word Generation (2022.emnlp-main)

Challenge:	Fig. 1 shows how feed-forward network (FFN) layers are utilized to build LMs.
Approach:	They reverse-engineer the operation of feed-forward network layers to find out how they work . they show that each update can be decomposed to sub-updates corresponding to single parameter vectors .
Outcome:	The proposed model reduces the toxicity of GPT2 by almost 50% and improves computation efficiency with a simple early exit rule, saving 20% of computation on average.

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Qifan Wang, Li Yang, Xiaojun Quan, Fuli Feng, Dongfang Liu, Zenglin Xu, Sinong Wang, Hao Ma

Graph-based Model Generation for Few-Shot Relation Extraction (2022.emnlp-main)

Challenge:	Existing automatic question generation methods focus on encoding passage and answer to generate question.
Approach:	They propose an automatic question generation approach which integrates question generation with its dual problem, question answering, into a unified primal-dual framework.
Outcome:	The proposed approach outperforms existing methods on SQuAD and HotpotQA benchmarks.

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Wanli Li, Tieyun Qian

Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling (2022.emnlp-main)

Challenge:	Existing models follow a 'one-for-all' scheme where one general large model performs all individual N-way-K-shot tasks, which prevents the model from achieving the optimal point on each task.
Approach:	They propose a model generation framework that consists of one general model for all tasks and many tiny task-specific models for each individual task.
Outcome:	The proposed framework achieves state-of-the-art performance on two public datasets.

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Ki Yoon Yoo, Nojun Kwak

Generating Natural Language Proofs with Verifier-Guided Search (2022.emnlp-main)

Challenge:	Recent advances in federated learning have demonstrated its promising capability to learn on decentralized datasets.
Approach:	They propose a technique that allows adversaries to poison the global model . they propose 'model poisoning' for backdoor attacks using word embeddings of NLP models .
Outcome:	The proposed technique improves the model poisoning performance in all experimental settings.

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Kaiyu Yang, Jia Deng, Danqi Chen

Toward Unifying Text Segmentation and Long Document Summarization (2022.emnlp-main)

Challenge:	Existing stepwise methods struggle to generate valid proof steps based on the hypothesis . instead, they generate invalid steps .
Approach:	They propose a stepwise method which generates relevant steps conditioning on the hypothesis.
Outcome:	The proposed method improves correctness of predicted proofs from 27.7% to 33.3% on EntailmentBank and RuleTaker.

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Sangwoo Cho, Kaiqiang Song, Xiaoyang Wang, Fei Liu, Dong Yu

The Geometry of Multilingual Language Model Representations (2022.emnlp-main)

Challenge:	Abstractive strategies produce more condensed summaries, but they suffer from hallucinations and factual errors, which pose a more difficult generation challenge.
Approach:	They propose a method that learns robust sentence representations by performing summarization and segmentation simultaneously, which is further enhanced by an optimization-based regularizer to promote selection of diverse summary sentences.
Outcome:	The proposed model achieves state-of-the-art performance on publicly available benchmarks and better cross-genre transferability when equipped with text segmentation.

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Tyler Chang, Zhuowen Tu, Benjamin Bergen

Improving Complex Knowledge Base Question Answering via Question-to-Action and Question-to-Question Alignment (2022.emnlp-main)

Challenge:	XLM-R models encode language-sensitive information in each language, allowing them to extract features for downstream tasks and cross-lingual transfer learning.
Approach:	They evaluate how multilingual language models maintain a shared multilingual representation space while still encoding language-sensitive information in each language.
Outcome:	The proposed model can extract features for downstream tasks and cross-lingual transfer learning.

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Yechun Tang, Xiaoxia Cheng, Weiming Lu

PAIR: Prompt-Aware margIn Ranking for Counselor Reflection Scoring in Motivational Interviewing (2022.emnlp-main)

Challenge:	ALCQA addresses the semantic and structural gap between natural language and action sequences . a priori, the semantics of the question and action are not well understood .
Approach:	They propose an alignment-enhanced complex question answering framework which aligns questions and actions into sequences.
Outcome:	The proposed framework outperforms state-of-the-art methods on a CQA and WQSP dataset.

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Do June Min, Verónica Pérez-Rosas, Kenneth Resnicow, Rada Mihalcea

Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs (2022.emnlp-main)

Challenge:	Existing approaches to provide constructive feedback to counselors are limited by the time and cost involved.
Approach:	They propose a system that takes as input a client prompt and a counselor response and outputs a score indicating the level of reflection in the counselor response.
Outcome:	The proposed model outperforms baselines on different metrics and can be used to provide useful feedback to counseling trainees.

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Bowen Xing, Ivor Tsang

The Importance of Being Parameters: An Intra-Distillation Method for Serious Gains (2022.emnlp-main)

Challenge:	Existing graph-based models only model the unidirectional guidance from intent to slot, which limits the performance.
Approach:	They propose a graph-based model that leverages the correlations between intent and slot to achieve mutual guidances between the two tasks.
Outcome:	The proposed model outperforms existing models by 19.3% in overall accuracy.

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Haoran Xu, Philipp Koehn, Kenton Murray

Interpreting Language Models with Contrastive Explanations (2022.emnlp-main)

Challenge:	Recent pruning methods remove redundant parameters according to parameter sensitivity, a gradient-based measure reflecting the contribution of the parameters.
Approach:	They propose a general task-agnostic method to balance parameter sensitivity and a novel adaptive learning method to control strength of intra-distillation loss for faster convergence.
Outcome:	The proposed method can reduce redundant parameters by over 80% without obvious performance degradation.

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Kayo Yin, Graham Neubig

RankGen: Improving Text Generation with Large Ranking Models (2022.emnlp-main)

Challenge:	Existing explanation methods conflate evidence for various features to predict a token . existing explanation methods are less interpretable for human understanding .
Approach:	They propose to explain language models contrastively by looking for salient input tokens that explain why the model predicted one token instead of another.
Outcome:	The proposed explanations are better than non-contrastive explanations for language models . they show that contrastive explanations improve simulability for human observers .

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Kalpesh Krishna, Yapei Chang, John Wieting, Mohit Iyyer

Learning a Grammar Inducer from Massive Uncurated Instructional Videos (2022.emnlp-main)

Challenge:	Modern language models assign high probabilities to output sequences that are repetitive, incoherent, or irrelevant to the prefix.
Approach:	They propose a 1.2B parameter encoder model for English that scores model generations given a prefix.
Outcome:	The proposed model outperforms decoding algorithms on automatic metrics and human evaluations with English writers.

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Songyang Zhang, Linfeng Song, Lifeng Jin, Haitao Mi, Kun Xu, Dong Yu, Jiebo Luo

Normalized Contrastive Learning for Text-Video Retrieval (2022.emnlp-main)

Challenge:	aims to find more accurate syntactic grammars for accompanying text using video data.
Approach:	They build a video-aided grammar induction model that can learn video-span correlation without manual features.
Outcome:	The proposed model can learn video-span correlation without manual features adopted by previous systems.

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Yookoon Park, Mahmoud Azab, Seungwhan Moon, Bo Xiong, Florian Metze, Gourab Kundu, Kirmani Ahmed

Estimating Soft Labels for Out-of-Domain Intent Detection (2022.emnlp-main)

Challenge:	Cross-modal contrastive learning suffers from incorrect normalization of the sum retrieval probabilities of each text or video instance.
Approach:	They propose a normalized contrastive learning algorithm that normalizes the sum retrieval probabilities of each instance so that every text and video instance is fairly represented.
Outcome:	Empirical results show that NCL brings significant gains in text-video retrieval on different model architectures without any architecture engineering.

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Hao Lang, Yinhe Zheng, Jian Sun, Fei Huang, Luo Si, Yongbin Li

Multi-VQG: Generating Engaging Questions for Multiple Images (2022.emnlp-main)

Challenge:	Existing methods to detect out-of-dominance (OOD) intents are limited by the lack of OOD samples.
Approach:	They propose an adaptive soft pseudo labeling method that can estimate soft labels for pseudo OOD samples when training OOD detectors.
Outcome:	The proposed method outperforms competing methods on three benchmark datasets and consistently outperformed previous methods.

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Min-Hsuan Yeh, Vincent Chen, Ting-Hao Huang, Lun-Wei Ku

Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation (2022.emnlp-main)

Challenge:	Traditional visual question generation (VQG) focuses on single images, resulting in a limited ability to comprehend time-series information of the underlying event.
Approach:	They propose to generate engaging questions from multiple images using a visual question generation dataset and establish a series of baselines.
Outcome:	The proposed model builds stories behind the image sequence to allow for creativity and experience sharing and hence draw attention to downstream applications.

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Jannis Bulian, Christian Buck, Wojciech Gajewski, Benjamin Börschinger, Tal Schuster

Non-Parametric Domain Adaptation for End-to-End Speech Translation (2022.emnlp-main)

Challenge:	despite the importance of question answering, evaluations of QA systems are typically limited by manual annotations . despite this, little progress has been made in QA evaluations based on a single answer .
Approach:	They propose to extend over exact match (EM) with predefined rules or token-level F1 measure . they propose to use a BERT matching measure to approximate QA predictions .
Outcome:	The proposed model improves AE approximations and more accurately reflects the performance of systems.

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Yichao Du, Weizhi Wang, Zhirui Zhang, Boxing Chen, Tong Xu, Jun Xie, Enhong Chen

Prompting for Multimodal Hateful Meme Classification (2022.emnlp-main)

Challenge:	End-to-end speech translation (E2E-ST) systems have received increasing attention due to its less error propagation, lower latency and fewer parameters.
Approach:	They propose a non-parametric method that leverages in-domain text translation corpus to achieve domain adaptation for E2E-ST systems.
Outcome:	The proposed method outperforms the existing in-domain fine-tuning strategies on the Europarl-ST benchmark.

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Rui Cao, Roy Ka-Wei Lee, Wen-Haw Chong, Jing Jiang

Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking (2022.emnlp-main)

Challenge:	Hateful meme classification requires complex reasoning and contextual background knowledge.
Approach:	They propose a simple yet effective prompt-based model that prompts pre-trained language models for hateful meme classification.
Outcome:	The proposed model outperforms state-of-the-art models on hateful meme classification task.

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Minghan Li, Xinyu Zhang, Ji Xin, Hongyang Zhang, Jimmy Lin

Linearizing Transformer with Key-Value Memory (2022.emnlp-main)

Challenge:	In information retrieval, candidate set pruning is used to speed up two-stage relevance ranking but lacks accurate error control and empirical guarantees.
Approach:	They propose a method that guarantees the test error after pruning is controlled under a user-specified threshold with high probability.
Outcome:	The proposed method reduces the average set size from 1000 to 27, increasing reranking speed by about 37 times while keeping MRR@10 greater than a pre-specified value of 0.38 with about 90% empirical coverage.

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Yizhe Zhang, Deng Cai

Robustness of Fusion-based Multimodal Classifiers to Cross-Modal Content Dilutions (2022.emnlp-main)

Challenge:	Efficient transformer variants with linear time complexity have been developed to mitigate the quadratic computational overhead of the vanilla transformer.
Approach:	They propose a linear time complexity transformer variant that reduces the quadratic computational overhead of the vanilla transformer by using a recurrent-style incremental computation similar to kernel-based transformers.
Outcome:	The proposed method reduces the performance gap while achieving the same efficiency even with short generation.

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Gaurav Verma, Vishwa Vinay, Ryan Rossi, Srijan Kumar

Translation between Molecules and Natural Language (2022.emnlp-main)

Challenge:	Existing work has focused on understanding the robustness of vision-and-language models to imperceptible variations on benchmark tasks.
Approach:	They develop a model that generates additional dilution text that maintains relevance and topical coherence with the image and existing text, and when added to the original text, leads to misclassification of the multimodal input.
Outcome:	The proposed model outperforms fusion-based classifiers on Crisis Humanitarianism and Sentiment Detection tasks by 23.3% and 22.5% in presence of dilutions generated by the model.

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Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, Heng Ji

What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment (2022.emnlp-main)

Challenge:	MolT5 pretrains models on unlabeled natural language text and molecule strings . bringing a new drug to market can cost over a billion dollars and take over ten years .
Approach:	They propose a self-supervised learning framework for pretraining models on unlabeled natural language text and molecule strings.
Outcome:	The proposed framework pretrains models on unlabeled natural language text and molecule strings, and it generates high quality outputs.

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Matthew Finlayson, Kyle Richardson, Ashish Sabharwal, Peter Clark

Sentence-Incremental Neural Coreference Resolution (2022.emnlp-main)

Challenge:	Recent studies have focused on instruction learning, where a model learns to perform unseen tasks from task descriptions alone.
Approach:	They propose to use a controlled synthetic environment to characterize large transformer models as instruction learners.
Outcome:	The proposed model can interpret only 65.6% of test instructions and 11%-24% of instructions in out-of-distribution settings.

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Matt Grenander, Shay B. Cohen, Mark Steedman

SNaC: Coherence Error Detection for Narrative Summarization (2022.emnlp-main)

Challenge:	Existing systems for identifying spans as mentions are based on mention-pair formulations, but they are not generalized beyond pronouns.
Approach:	They propose a sentence-incremental neural coreference resolution system which incrementally builds clusters after marking mention boundaries in a shift-reduce method.
Outcome:	The proposed system outperforms state-of-the-art methods on OntoNotes and CODI-CRAC 2021 datasets and is comparable to state- of-the art methods.

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Tanya Goyal, Junyi Jessy Li, Greg Durrett

HydraSum: Disentangling Style Features in Text Summarization with Multi-Decoder Models (2022.emnlp-main)

Challenge:	SNaC framework is used to evaluate long summaries, but it fails to identify gaps in coherence . nallapati and colleagues have developed a framework for fine-grained annotations of long summarizations .
Approach:	They propose a narrative coherence evaluation framework for fine-grained annotations of long summaries that can be used to evaluate coherent narratives.
Outcome:	The proposed framework can support future work in document summarization and coherence evaluation, the authors show .

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Tanya Goyal, Nazneen Rajani, Wenhao Liu, Wojciech Kryscinski

A Good Neighbor, A Found Treasure: Mining Treasured Neighbors for Knowledge Graph Entity Typing (2022.emnlp-main)

Challenge:	Abstractive summarization systems implicitly encode “decisions” about summary properties, but these are not enforced.
Approach:	They propose a new summarization architecture that extends existing models to a mixture-of-experts version with multiple decoders.
Outcome:	The proposed architecture outperforms baseline models in obtaining stylistically-diverse summaries by sampling from individual decoders or their mixtures.

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Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao

Guiding Neural Entity Alignment with Compatibility (2022.emnlp-main)

Challenge:	Existing methods to infer missing types for knowledge graphs only leverage one-hop neighbor information of the central entity, ignoring multi-hop neighbors that can provide valuable clues for inference.
Approach:	They propose a method to infer missing types for knowledge graph entities by using neighbor information and co-occurrence relations between types.
Outcome:	The proposed method significantly outperforms existing state-of-the-art methods on two widely used datasets.

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Bing Liu, Harrisen Scells, Wen Hua, Guido Zuccon, Genghong Zhao, Xia Zhang

InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning (2022.emnlp-main)

Challenge:	Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs) labelled data is used to learn neural EA models, but this aspect is neglected .
Approach:	They propose a framework to integrate compatibility into neural EA models . they aim to find equivalent entities between two Knowledge Graphs (KGs)
Outcome:	The proposed framework can achieve comparable effectiveness with supervised training using 20% of labelled data.

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Prakhar Gupta, Cathy Jiao, Yi-Ting Yeh, Shikib Mehri, Maxine Eskenazi, Jeffrey Bigham

Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling (2022.emnlp-main)

Challenge:	Instruction tuning is emerging in NLP, but has not been explored for dialogue-related tasks.
Approach:	They propose an instruction tuning framework for dialogue that leverages natural language instructions with language models to induce zero-shot generalization on unseen tasks.
Outcome:	The proposed framework enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection.

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Peijie Jiang, Dingkun Long, Yanzhao Zhang, Pengjun Xie, Meishan Zhang, Min Zhang

RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder (2022.emnlp-main)

Challenge:	Experimental results show that Boundary-Aware BERT can improve Chinese sequence labeling tasks.
Approach:	They propose to encode boundary information directly into pre-trained language models . they propose to use unsupervised boundary information instead of supervised boundary info .
Outcome:	The proposed architecture improves Chinese sequence labeling tasks on ten benchmarks.

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Shitao Xiao, Zheng Liu, Yingxia Shao, Zhao Cao

Aligning Recommendation and Conversation via Dual Imitation (2022.emnlp-main)

Challenge:	Existing methods for dense retrieval are not effective, but there are still challenges.
Approach:	They propose a retrieval oriented pre-training paradigm based on Masked Auto-Encoder (MAE) where the sentence embedding is generated from the encoder’s masked input and the original sentence is recovered based upon the sentence embedded and decoded input via mangled language modeling.
Outcome:	The proposed model significantly improves the SOTA performance on a wide range of NLP benchmarks, like BEIR and MS MARCO.

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Jinfeng Zhou, Bo Wang, Minlie Huang, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance (2022.emnlp-main)

Challenge:	Existing conversational recommendation systems ignore the advantage of user interest shift in connecting recommendation and conversation, leading to an ineffective loose coupling structure.
Approach:	They propose a dual imitation to explicitly align recommendation and conversation paths . they propose to generate high-quality responses with accurate recommendations and coherent explanations .
Outcome:	The proposed model outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics.

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Xiaoqiang Wang, Bang Liu, Siliang Tang, Lingfei Wu

Abstract Visual Reasoning with Tangram Shapes (2022.emnlp-main)

Challenge:	Existing metrics for assessing question generation fail to take into account the input context of generation.
Approach:	They propose a context-aware Relevance evaluation metric for Question Generation that takes into account the context of question generation into account.
Outcome:	The proposed metric achieves higher correlation with human judgments while being much more robust to adversarial samples.

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Anya Ji, Noriyuki Kojima, Noah Rush, Alane Suhr, Wai Keen Vong, Robert Hawkins, Yoav Artzi

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)

Challenge:	We use tangrams as stimuli in cognitive science to study abstract visual reasoning . pre-trained weights demonstrate limited abstract reasoning, we observe .
Approach:	They propose a resource for studying abstract visual reasoning in humans and machines . they use tangram puzzles as stimuli to create an annotated dataset with >1k distinct stimuli .
Outcome:	The proposed resource is visually and linguistically richer than previous resources . pre-trained weights demonstrate limited abstract reasoning, the authors note .

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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu

Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models (2022.emnlp-main)

Challenge:	Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately .
Approach:	They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes .
Outcome:	The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show .

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Hannah Chen, Yangfeng Ji, David Evans

When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks (2022.emnlp-main)

Challenge:	Traditional adversarial examples involve finding a small perturbation that does not change an input’s true label but confuses the classifier into outputting a different prediction.
Approach:	They propose to use contrastive learning to increase model robustness against fickle adversarial examples by reducing the vulnerability of adversarials to fickle ones.
Outcome:	The proposed method improves model robustness against fickle and obstinate adversarial examples.

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Ankur Sikarwar, Arkil Patel, Navin Goyal

Generative Language Models for Paragraph-Level Question Generation (2022.emnlp-main)

Challenge:	Recent benchmarks like ReaSCAN use navigation tasks grounded in a grid world to assess whether neural models exhibit compositional behaviour.
Approach:	They propose a transformer-based model that outperforms specialized architectures on ReaSCAN and a modified version of gSCAN to test their performance.
Outcome:	The proposed model outperforms specialized architectures on ReaSCAN and gSCAN on a grid world and can generalize to deeper input structures.

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Asahi Ushio, Fernando Alva-Manchego, Jose Camacho-Collados

A Unified Encoder-Decoder Framework with Entity Memory (2022.emnlp-main)

Challenge:	Powerful generative models have led to recent progress in question generation.
Approach:	They propose a multilingual and multidomain benchmark for question generation that unifies existing datasets by converting them to a standard QG setting.
Outcome:	The proposed benchmark unifies existing question answering datasets to a QG setting.

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Zhihan Zhang, Wenhao Yu, Chenguang Zhu, Meng Jiang

Segmenting Numerical Substitution Ciphers (2022.emnlp-main)

Challenge:	Existing approaches to index, retrieve, and read documents as evidence suffer from large computational overheads.
Approach:	They propose an encoder-decoder framework with an entity memory that stores entity knowledge as latent representations and pre-trained on Wikipedia along with encoder parameters.
Outcome:	The proposed framework outperforms memory-based and non-memory encoder-decoder models on various entity-intensive question answering and generation tasks.

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Nada Aldarrab, Jonathan May

Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset (2022.emnlp-main)

Challenge:	Existing methods for deciphering historical substitution ciphers are difficult to crack . cipheries that are not segmented are still difficult to deciphere .
Approach:	They propose automatic methods to segment historical substitution ciphers using BPE and unigram language models.
Outcome:	The proposed methods achieve an average segmentation error of 2% on 100 monoalphabetic ciphers and 27% on 3 real historical homophonic cipheries.

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Ashish V. Thapliyal, Jordi Pont Tuset, Xi Chen, Radu Soricut

ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select (2022.emnlp-main)

Challenge:	Existing studies on multilingual image captioning have been hampered by a lack of high-quality evaluation datasets.
Approach:	They present a dataset of 3600 images annotated with human-generated captions in 36 languages.
Outcome:	The proposed dataset shows that it is feasible to build multilingual image captioning models trained on machine-translated data.

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Yuchen Zhuang, Yinghao Li, Junyang Zhang, Yue Yu, Yingjun Mou, Xiang Chen, Le Song, Chao Zhang

GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs (2022.emnlp-main)

Challenge:	Our proposed method extracts N-ary relation tuples from scientific articles.
Approach:	They propose a method that decomposes the task into two stages . they propose modal query and modal entity selection . their results show that ReSel outperforms state-of-the-art baselines significantly .
Outcome:	The proposed method outperforms state-of-the-art baselines on three scientific information extraction datasets.

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Dong Yang, Peijun Qing, Yang Li, Haonan Lu, Xiaodong Lin

Reasoning Like Program Executors (2022.emnlp-main)

Challenge:	Existing methods for embedding knowledge graphs are difficult due to complicated query structures and incomplete graph data.
Approach:	They propose a probabilistic embedding model for encoding entities and queries to answer different types of FOL queries on KGs.
Outcome:	The proposed model outperforms state-of-the-art models on public benchmarks on three large logical query datasets.

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Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Qiang Fu, Yan Gao, Jian-Guang Lou, Weizhu Chen

SEM-F1: an Automatic Way for Semantic Evaluation of Multi-Narrative Overlap Summaries at Scale (2022.emnlp-main)

Challenge:	Existing language models are inadequate in reasoning, according to studies . a new reasoning pre-training paradigm is based on pretraining language models with programs .
Approach:	They propose a reasoning pre-training paradigm that empowers language models to harvest reasoning knowledge possessed by program executors.
Outcome:	The proposed reasoning pre-training paradigm can boost models' reasoning skills . it can be instantiated by different kinds of program executors and run on a single database .

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Naman Bansal, Mousumi Akter, Shubhra Kanti Karmaker Santu

Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning (2022.emnlp-main)

Challenge:	Recent work has introduced an important yet relatively under-explored NLP task called Semantic Overlap Summarization (SOS) that entails generating a summary from multiple alternative narratives which conveys the common information provided by those narratives.
Approach:	They propose to use a sentence-level precision-recall style automated evaluation metric to evaluate a new NLP task called Semantic Overlap Summarization (SOS) they propose to employ the popular ROUGE metric and use it to compare the two tasks.
Outcome:	The proposed metric yields higher correlation with human judgment and higher inter-rater agreement compared to the existing metric.

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Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu, Di Jin, Dilek Hakkani-Tur

DocInfer: Document-level Natural Language Inference using Optimal Evidence Selection (2022.emnlp-main)

Challenge:	Prefix-tuning is an essential paradigm of parameter-efficient transfer learning . fine-tuned models require separate copies of model parameters for each task .
Approach:	They propose to understand and further develop prefix-tuning through the kernel lens . they propose a new variant of prefix tuning that shares the exact mechanism as prefix tun .
Outcome:	The proposed method improves prefix-tuning performance by training only a small portion of parameters.

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Puneet Mathur, Gautam Kunapuli, Riyaz Bhat, Manish Shrivastava, Dinesh Manocha, Maneesh Singh

LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation (2022.emnlp-main)

Challenge:	Documentlevel NLI is an important problem for many tasks including verification of factual correctness of documents.
Approach:	They propose a document-level natural language inference model that builds a hierarchical document graph enriched through inter-sentence relations and performs paragraph pruning using the novel SubGraph Pooling layer.
Outcome:	The proposed model performs on a legal judicial reasoning task with a dataset enriched with document graphs and a proposed evidence selection algorithm.

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Xin Mao, Wenting Wang, Yuanbin Wu, Man Lan

Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning (2022.emnlp-main)

Challenge:	Existing EA methods inherit the inborn defects from their neural network lineage: poor interpretability and weak scalability.
Approach:	They propose a neural-free EA framework that can find equivalent entity pairs between KGs.
Outcome:	The proposed framework has impressive scalability, robustness, and interpretability.

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Xingwei He, Yeyun Gong, A-Long Jin, Weizhen Qi, Hang Zhang, Jian Jiao, Bartuer Zhou, Biao Cheng, Sm Yiu, Nan Duan

Efficient Document Retrieval by End-to-End Refining and Quantizing BERT Embedding with Contrastive Product Quantization (2022.emnlp-main)

Challenge:	Existing work on commonsense generation requires models to have relational reasoning and compositional generalization capabilities.
Approach:	They propose a metric distillation rule to distill knowledge from a standard metric to a ranker and transfer it to re-ranking a retriever.
Outcome:	The proposed method surpasses the previous SOTA.

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Zexuan Qiu, Qinliang Su, Jianxing Yu, Shijing Si

Curriculum Knowledge Distillation for Emoji-supervised Cross-lingual Sentiment Analysis (2022.emnlp-main)

Challenge:	Existing semantic hashing methods only learn a binary code for each document and use Hamming distance to evaluate document distances.
Approach:	They propose to leverage BERT embeddings to perform efficient retrieval based on product quantization technique . they transform original BERT embedded codewords and feed it into a probabilistic product quantizer module .
Outcome:	The proposed method outperforms current state-of-the-art methods on three benchmarks.

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Jianyang Zhang, Tao Liang, Mingyang Wan, Guowu Yang, Fengmao Lv

Correctable-DST: Mitigating Historical Context Mismatch between Training and Inference for Improved Dialogue State Tracking (2022.emnlp-main)

Challenge:	Existing sentiment analysis models do not have sufficient sentiment corpus to detect sentiment in low-resource languages.
Approach:	They propose a cross-lingual sentiment analysis approach to transfer sentiment knowledge across languages . they use emojis to bridge the source and target languages to find the sentiment .
Outcome:	The proposed approach bridges the source and target languages using emojis . it can learn delicate sentiment knowledge, avoiding cross-lingual gaps .

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Hongyan Xie, Haoxiang Su, Shuangyong Song, Hao Huang, Bo Zou, Kun Deng, Jianghua Lin, Zhihui Zhang, Xiaodong He

DropMix: A Textual Data Augmentation Combining Dropout with Mixup (2022.emnlp-main)

Challenge:	Existing dialogue state tracking approaches predict the dialogue state of a target turn sequentially based on the ground-truth previous dialogue state.
Approach:	They propose a method that predicts dialogue state sequentially based on previous dialogue state . they propose generating a previously “predicted” dialogue state using ground-truth previous dialogue states .
Outcome:	The proposed method achieves 67.51%, 68.24%, 70.30%, 71.38%, and 81.27% joint goal accuracy on MultiWOZ 2.0-2.4 datasets.

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Fanshuang Kong, Richong Zhang, Xiaohui Guo, Samuel Mensah, Yongyi Mao

Cross-document Event Coreference Search: Task, Dataset and Modeling (2022.emnlp-main)

Challenge:	Existing methods to overcome overfitting in text learning do not consider dimensionality . dimensionalization is important for deep neural networks to overcome the problem .
Approach:	They propose a saliency map-based approach to overcome overfitting in text learning . they propose augmentation regularization methods such as Dropout and Mixup to improve regularization .
Outcome:	Empirical results show that the proposed approach overcomes overfitting in text learning . dropout and mixup methods are effective in enhancing regularization .

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Alon Eirew, Avi Caciularu, Ido Dagan

VIRT: Improving Representation-based Text Matching via Virtual Interaction (2022.emnlp-main)

Challenge:	Cross-document Event Coreference resolution is the task of identifying clusters of text mentions that refer to the same event, whether within a single document or across a document collection.
Approach:	They propose a cross-document coreference search task that searches for all coreferring mentions for a query event in a large document collection.
Outcome:	The proposed model integrates a powerful coreference scoring scheme into the DPR architecture, yielding improved performance.

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Dan Li, Yang Yang, Hongyin Tang, Jiahao Liu, Qifan Wang, Jingang Wang, Tong Xu, Wei Wu, Enhong Chen

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction (2022.emnlp-main)

Challenge:	Experimental results show that representation-based text matching methods suffer from performance degradation due to the lack of interactions between the pair of texts.
Approach:	They propose a virtual interaction mechanism that enables deep interaction between texts . they propose 'inteRacTion mechanism' that can be integrated into existing methods as plugins .
Outcome:	The proposed method outperforms state-of-the-art models on six text matching benchmarks.

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Xiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng, Zimu Wang, Yankai Lin, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou

Entity Extraction in Low Resource Domains with Selective Pre-training of Large Language Models (2022.emnlp-main)

Challenge:	Existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions.
Approach:	They construct a large-scale human-annotated ERE dataset with improved annotation schemes to address these drawbacks.
Outcome:	The proposed dataset is larger than existing datasets of all the ERE tasks by at least an order of magnitude.

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Aniruddha Mahapatra, Sharmila Reddy Nangi, Aparna Garimella, Anandhavelu N

How Large Language Models are Transforming Machine-Paraphrase Plagiarism (2022.emnlp-main)

Challenge:	Existing methods to perform named entity recognition (NER) on unlabeled data are difficult to obtain in low-resource domains.
Approach:	They propose ways to use unlabeled data for pretraining to improve performance in downstream tasks.
Outcome:	The proposed methods outperform models trained on unlabeled data on seven domains.

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Jan Philip Wahle, Terry Ruas, Frederic Kirstein, Bela Gipp

M2D2: A Massively Multi-Domain Language Modeling Dataset (2022.emnlp-main)

Challenge:	Autoregressive paraphrasing tools can be used to generate convincing plagiarized texts with minimal effort.
Approach:	They evaluate the detection performance of large autoregressive models for machine-paraphrase generation on scientific articles from arXiv, student theses, and Wikipedia.
Outcome:	The proposed models generate paraphrases indistinguishable from original work and human experts rate the quality of generated examples as high as originals.

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Machel Reid, Victor Zhong, Suchin Gururangan, Luke Zettlemoyer

“Will You Find These Shortcuts?” A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification (2022.emnlp-main)

Challenge:	M2D2 consists of 8.5B tokens and spans 145 domains extracted from Wikipedia and Semantic Scholar.
Approach:	They propose to organize 145 domains into 22 groups and use ontologies from Wikipedia and ArXiv to study domain adaptation in language models.
Outcome:	The proposed model enables the study of domain adaptation in language models (LMs) it shows that small amounts of fine-grained data can lead to larger in-domain performance gains than weakly relevant data.

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Jasmijn Bastings, Sebastian Ebert, Polina Zablotskaia, Anders Sandholm, Katja Filippova

Information-Transport-based Policy for Simultaneous Translation (2022.emnlp-main)

Challenge:	Existing work on faithfulness evaluation is not conclusive and does not provide a clear answer as to how different methods are to be compared.
Approach:	They propose a protocol for faithfulness evaluation that makes use of partially synthetic data to obtain ground truth for feature importance ranking.
Outcome:	The proposed method is based on partially synthetic data and is compared with lexical shortcuts on a range of datasets and LSTM models.

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Shaolei Zhang, Yang Feng

Learning to Adapt to Low-Resource Paraphrase Generation (2022.emnlp-main)

Challenge:	Simultaneous translation (ST) outputs translation while receiving source inputs . low latency restriction restricts ST to translating target tokens based on current received source tokens.
Approach:	They propose a system that outputs translation while receiving source inputs . it uses a read/write policy to decide whether to translate a target token or wait for the next source token .
Outcome:	The proposed model outperforms baselines and achieves state-of-the-art on text-to-text and speech-to text tasks.

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Zhigen Li, Yanmeng Wang, Rizhao Fan, Ye Wang, Jianfeng Li, Shaojun Wang

A Distributional Lens for Multi-Aspect Controllable Text Generation (2022.emnlp-main)

Challenge:	Conventional approaches to paraphrase generation often rely on a large number of parallel paraphrases, which require a lot of domain knowledge.
Approach:	They propose an adapter for paraphrase generation models optimized by meta-learning to overcome domain shifting problem when training on scarce labeled data.
Outcome:	The proposed model achieves state-of-the-art on three benchmark datasets.

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Yuxuan Gu, Xiaocheng Feng, Sicheng Ma, Lingyuan Zhang, Heng Gong, Bing Qin

ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation (2022.emnlp-main)

Challenge:	Existing methods for multi-aspect control suffer from attribute degeneration due to mutual interference of these controllers.
Approach:	They propose to use attribute fusion to find the intersections of multiple attributes as their combination for generation.
Outcome:	The proposed method outperforms baselines on attribute relevance and text quality and achieves the SOTA.

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Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen

Multilingual Relation Classification via Efficient and Effective Prompting (2022.emnlp-main)

Challenge:	Existing methods for text generation use auto-regressive (AR) methods, but inefficient inference is a problem.
Approach:	They propose an efficient and effective PLM to explicitly model the token dependency during NAR text generation.
Outcome:	The proposed model outperforms existing models on three text generation tasks while achieving 10 times faster inference speedup.

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Yuxuan Chen, David Harbecke, Leonhard Hennig

Topic-Regularized Authorship Representation Learning (2022.emnlp-main)

Challenge:	Existing work on prompt-based multilingual relation classification (RC) uses pre-trained language models with limited resources.
Approach:	They propose a prompt-based multilingual relation classification method that constructs relation triples from relation triple labels and requires minimal translation for the class labels.
Outcome:	The proposed method outperforms baselines in English-task training in cross-lingual settings and in fully supervised and few-shot scenarios.

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Jitkapat Sawatphol, Nonthakit Chaiwong, Can Udomcharoenchaikit, Sarana Nutanong

Fine-grained Contrastive Learning for Relation Extraction (2022.emnlp-main)

Challenge:	Existing techniques for authorship attribution have focused on out-of-distribution in topics or authors.
Approach:	They propose a framework that creates authorship representation with reduced reliance on topic-specific information to handle a large number of unseen authors and topics.
Outcome:	The proposed framework has improved over baselines in 4 out of 6 cases.

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William Hogan, Jiacheng Li, Jingbo Shang

Curriculum Prompt Learning with Self-Training for Abstractive Dialogue Summarization (2022.emnlp-main)

Challenge:	Existing methods assume all silver labels are accurate and treat them equally, but distant supervision is noisy–some silver labels more reliable than others.
Approach:	They propose a noise-aware contrastive learning approach that leverages fine-grained information about which silver labels are and are not noisy to improve the quality of learned relationship representations.
Outcome:	The proposed approach improves relation extraction performance over state-of-the-art methods on several RE benchmarks.

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Changqun Li, Linlin Wang, Xin Lin, Gerard de Melo, Liang He

Zero-Shot Text Classification with Self-Training (2022.emnlp-main)

Challenge:	Existing methods to summarize dialogues are difficult due to insufficient training data and low information density.
Approach:	They propose a curriculum-based prompt learning method with self-training that gradually increases the degree of prompt perturbation, improving dialogue understanding and modeling capabilities.
Outcome:	The proposed model outperforms baseline models on the AMI and ICSI datasets and human evaluations show it is superior in the quality of the summary generation.

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Ariel Gera, Alon Halfon, Eyal Shnarch, Yotam Perlitz, Liat Ein-Dor, Noam Slonim

Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts (2022.emnlp-main)

Challenge:	Recent advances in large pretrained language models have increased attention to zero-shot text classification.
Approach:	They propose a plug-and-play method to bridge this gap by requiring only class names along with an unlabeled dataset.
Outcome:	The proposed model can be trained on a natural language inference dataset and performs on dozens of unseen tasks without the need for domain expertise or trial and error.

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T.y.s.s Santosh, Shanshan Xu, Oana Ichim, Matthias Grabmair

SQuALITY: Building a Long-Document Summarization Dataset the Hard Way (2022.emnlp-main)

Challenge:	Legal Judgement Prediction systems without expert-informed adjustments can be vulnerable to shallow, distracting surface signals.
Approach:	They propose to use domain expertise to identify statistically predictive but legally irrelevant information and adopt adversarial training to prevent it from relying on it.
Outcome:	The proposed model aligns better with expert rationales than baseline models . the results are compared with an existing benchmark dataset of human rights cases .

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Alex Wang, Richard Yuanzhe Pang, Angelica Chen, Jason Phang, Samuel R. Bowman

MetaASSIST: Robust Dialogue State Tracking with Meta Learning (2022.emnlp-main)

Challenge:	Existing summarization datasets often have issues that seriously limit their usability.
Approach:	They propose a faster but more straightforward approach to developing summarization benchmark data . they use a protocol that hires highly-qualified contractors to read stories and write original summaries from scratch .
Outcome:	The proposed protocol is faster but more straightforward than scraping summaries from everyday text.

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Fanghua Ye, Xi Wang, Jie Huang, Shenghui Li, Samuel Stern, Emine Yilmaz

Multilingual Machine Translation with Hyper-Adapters (2022.emnlp-main)

Challenge:	Existing dialogue datasets contain lots of noise in their state annotations.
Approach:	They propose a framework to train robust dialogue state tracking models by combining pseudo and vanilla labels by a common weighting parameter.
Outcome:	The proposed framework achieves state-of-the-art accuracy of 80.10% on multiWOZ 2.4.

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Christos Baziotis, Mikel Artetxe, James Cross, Shruti Bhosale

Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination (2022.emnlp-main)

Challenge:	Multilingual machine translation suffers from negative interference across languages.
Approach:	They propose a rescaling fix that reduces the number of parameters and enables training larger hyper-networks.
Outcome:	The proposed approach outperforms regular adapters and achieves the same performance with 12 times less parameters.

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Yue Yang, Wenlin Yao, Hongming Zhang, Xiaoyang Wang, Dong Yu, Jianshu Chen

Using Commonsense Knowledge to Answer Why-Questions (2022.emnlp-main)

Challenge:	Large-scale pretrained language models suffer from reporting bias, describing the lack of explicit commonsense knowledge in written text.
Approach:	They propose to endow language models with visual imagination capabilities by recalling existing images and synthesizing nonexistent images via text-to-image generation.
Outcome:	The proposed model improves the performance of existing language models across a diverse set of language tasks.

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Yash Kumar Lal, Niket Tandon, Tanvi Aggarwal, Horace Liu, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian

Commonsense Reasoning

Challenge:	Existing approaches to integrating commonsense knowledge into large language models are implicit and explicit.
Approach:	They analyze the effects of model size and methods of injecting knowledge into TellMeWhy datasets to determine what aspects of commonsense knowledge are available in large language models.
Outcome:	The largest models yield substantial improvements over base models, but the amount of improvement decreases with larger model size.

Affective Idiosyncratic Responses to Music (2022.emnlp-main)

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Sky CH-Wang, Evan Li, Oliver Li, Smaranda Muresan, Zhou Yu

Successive Prompting for Decomposing Complex Questions (2022.emnlp-main)

Challenge:	Affective responses to music are highly personal, but it's difficult to measure marginal effects of these variables . a study of 403M listener comments on a social music platform in china aims to address this gap .
Approach:	They propose to measure affective responses to music from 403M listener comments on a Chinese social music platform.
Outcome:	The proposed method identifies musical, lyrical, contextual, demographic, and mental health effects that drive listener affective responses from over 403M listener comments on a Chinese social music platform.

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Dheeru Dua, Shivanshu Gupta, Sameer Singh, Matt Gardner

Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations (2022.emnlp-main)

Challenge:	Recent work leverages the capabilities of large language models (LMs) to perform complex question answering in a few-shot setting by generating latent reasoning steps before answering the question.
Approach:	They propose a method to iteratively break down a complex task into a simple task, solve it, and repeat the process until we get the final solution.
Outcome:	The proposed model achieves an improvement in F1 of 5% when compared with a state-of-the-art model with synthetic augmentations and few-shot version of the DROP dataset.

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Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, Yejin Choi

DANLI: Deliberative Agent for Following Natural Language Instructions (2022.emnlp-main)

Challenge:	Pre-trained language models struggle with consistent reasoning, and prompting methods are often noisy and inconsistent.
Approach:	They propose a few-shot inference method inspired by the Socratic way of conversation that generates a tree of explanations that bear logical relations between each other and frames it as a satisfiability problem.
Outcome:	The proposed method achieves 20% better accuracy than state-of-the-art prompting methods and performs competitively with supervised models.

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Yichi Zhang, Jianing Yang, Jiayi Pan, Shane Storks, Nikhil Devraj, Ziqiao Ma, Keunwoo Yu, Yuwei Bao, Joyce Chai

Tracing Semantic Variation in Slang (2022.emnlp-main)

Challenge:	Recent work on embodied AI agents that can perform tasks by following human language instructions is limited by reactive methods, which are insufficient for long-horizon complex tasks.
Approach:	They propose a neuro-symbolic deliberative agent that, while following language instructions, proactively applies reasoning and planning based on its neural and symbolic representations acquired from past experience.
Outcome:	The proposed agent achieves greater than 70% improvement over reactive baselines on the challenging TEACh benchmark.

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Zhewei Sun, Yang Xu

Linguistics & Cognitive NLP

Challenge:	Existing approaches to slang semantic variation do not account for the semantic variation of sling among different groups of users.
Approach:	They propose to use slang semantic variation models to trace the regional identity of a new emerging sling sense given its historical meanings.
Outcome:	The proposed models can predict regional identity of emerging slang word meanings from historical sling dictionary entries.

Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning (2022.emnlp-main)

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Wenbin An, Feng Tian, Ping Chen, Siliang Tang, Qinghua Zheng, QianYing Wang

PLM-based World Models for Text-based Games (2022.emnlp-main)

Challenge:	Existing methods for novel category discovery focus on the scenario where known and novel categories are of the same granularity.
Approach:	They propose a novel scenario for fine-grained category discovery under coarse-grain supervision that allows for adapting models to categories of different granularity from known ones.
Outcome:	The proposed model can adapt models to categories of different granularity from known ones and reduce labeling cost.

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Minsoo Kim, Yeonjoon Jung, Dohyeon Lee, Seung-won Hwang

Prompt-Based Meta-Learning For Few-shot Text Classification (2022.emnlp-main)

Challenge:	a new study shows that pre-trained world models provide a strong base for world models . worldformer is a text-based game environment that can be used to learn world models in text-driven games.
Approach:	They propose to use pre-trained language models to build world models in text-based game environments.
Outcome:	The proposed model outperforms state-of-the-art model-free algorithms in Atari games while retaining sample efficiency.

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Haoxing Zhang, Xiaofeng Zhang, Haibo Huang, Lei Yu

How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions? (2022.emnlp-main)

Challenge:	Existing methods to learn text labels require large amounts of data to build many few-shot tasks.
Approach:	They propose a Prompt-Based Meta-Learning model that adds the prompting mechanism to the meta-learning method.
Outcome:	The proposed method improves on four text classification datasets with high accuracy and robustness.

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Hritik Bansal, Da Yin, Masoud Monajatipoor, Kai-Wei Chang

Geographic Citation Gaps in NLP Research (2022.emnlp-main)

Challenge:	Text-to-image generative models can generate high-quality photo-realistic images conditional on natural language text descriptions in a zero-shot fashion.
Approach:	They propose an Ethical NaTural Language Interventions in Text-to-Image GENeration benchmark dataset to evaluate the change in image generation conditional on ethical interventions across three social axes – gender, skin color, and culture.
Outcome:	The proposed model generations cover diverse social groups while preserving image quality.

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Mukund Rungta, Janvijay Singh, Saif M. Mohammad, Diyi Yang

Language Models of Code are Few-Shot Commonsense Learners (2022.emnlp-main)

Challenge:	a vast number of papers accepted at top NLP venues come from a handful of western countries and (lately) China.
Approach:	They ask researchers to examine the relationship between geographical location and publication success . they use a dataset of 70,000 papers from the ACL Anthology to examine their citation network .
Outcome:	The proposed dataset of 70,000 papers from the ACL Anthology shows that there are substantial geographical disparities in paper acceptance and citations .

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Aman Madaan, Shuyan Zhou, Uri Alon, Yiming Yang, Graham Neubig

Numerical Optimizations for Weighted Low-rank Estimation on Language Models (2022.emnlp-main)

Challenge:	Existing approaches to generate graphs using pre-trained language models hinder their ability to generate them correctly.
Approach:	They propose to frame structured commonsense reasoning tasks as code generation tasks instead of serializing the output graph as a flat list of nodes and edges.
Outcome:	The proposed approach outperforms natural-language LMs in three natural language tasks even when the downstream task does not involve source code at all.

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Ting Hua, Yen-Chang Hsu, Felicity Wang, Qian Lou, Yilin Shen, Hongxia Jin

Generative Multi-hop Retrieval (2022.emnlp-main)

Challenge:	Singular value decomposition (SVD) is one of the most popular methods for estimating a target matrix with smaller matrices.
Approach:	They propose a method that approximates a target matrix with smaller matrices by two smaller . they also propose metric to predict when the SVD may introduce a significant performance drop.
Outcome:	The proposed method can perform better than current SOTA methods in compressing Transformer-based language models.

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Hyunji Lee, Sohee Yang, Hanseok Oh, Minjoon Seo

Visual Spatial Description: Controlled Spatial-Oriented Image-to-Text Generation (2022.emnlp-main)

Challenge:	A bi-encoder approach to text retrieval has limitations in multi-hop settings; the reformulated query gets longer as the number of hops increases, which further tightens the embedding bottleneck of the query vector.
Approach:	They propose an encoder-decoder model that performs multi-hop retrieval by simply generating the entire text sequences of the retrieval targets.
Outcome:	The proposed model achieves comparable or higher performance than bi-encoder models in five datasets while demonstrating superior GPU memory and storage footprint.

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Yu Zhao, Jianguo Wei, ZhiChao Lin, Yueheng Sun, Meishan Zhang, Min Zhang

M3: A Multi-View Fusion and Multi-Decoding Network for Multi-Document Reading Comprehension (2022.emnlp-main)

Challenge:	Image-to-text tasks such as captioning and controllable image descriptions have received extensive attention for decades.
Approach:	They propose a new perspective for image-to-text to generate spatial descriptions by combining two objects in an image.
Outcome:	The proposed model is awe-inspiring and human-like, and the proposed end-to-end architecture is the better choice for their integration.

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Liang Wen, Houfeng Wang, Yingwei Luo, Xiaolin Wang

COCO-DR: Combating Distribution Shift in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning (2022.emnlp-main)

Challenge:	Existing methods for multi-document reading comprehension cannot make full of the advantages of both approaches.
Approach:	They propose a multi-view fusion and multi-decoding method that integrates multiple documents for answering questions.
Outcome:	The proposed method improves on two mainstream multi-document reading comprehension datasets.

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Yue Yu, Chenyan Xiong, Si Sun, Chao Zhang, Arnold Overwijk

Language Model Pre-Training with Sparse Latent Typing (2022.emnlp-main)

Challenge:	Using COCO-DR, we combat distribution shifts between source training tasks and target scenarios.
Approach:	They propose a method to combat distribution shifts between source training tasks and target scenarios by COtinuous COtrastive learning.
Outcome:	The proposed method outperforms existing models on BEIR and the giant GPT-3 embedding model with 500x more parameters.

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Liliang Ren, Zixuan Zhang, Han Wang, Clare Voss, ChengXiang Zhai, Heng Ji

On the Transformation of Latent Space in Fine-Tuned NLP Models (2022.emnlp-main)

Challenge:	Modern large-scale Pre-trained Language Models focus on text reconstruction, but have not sought to learn latent-level interpretable representations of sentences.
Approach:	They propose a new pre-training objective that enables the model to learn latent types . the objective allows the model a self-supervised way to extract sentence-level keywords .
Outcome:	The proposed model learns interpretable latent type categories without external knowledge and improves downstream tasks.

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Nadir Durrani, Hassan Sajjad, Fahim Dalvi, Firoj Alam

Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery (2022.emnlp-main)

Challenge:	a large body of work analyzed the knowledge learned within representations of pre-trained models.
Approach:	They use hierarchical clustering to discover latent concepts in representational space . they compare pre-trained and fine-tuned models and perform a thorough analysis .
Outcome:	The results show that the model space evolves towards task-specific concepts whereas the lower layers retain generic concepts acquired in the pre-trained model.

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Yutao Mou, Keqing He, Pei Wang, Yanan Wu, Jingang Wang, Wei Wu, Weiran Xu

Extracted BERT Model Leaks More Information than You Think! (2022.emnlp-main)

Challenge:	Existing methods for finding out-of-domain intents suffer from in-domain overfitting problem . previous methods fail to transfer prior knowledge to downstream clustering .
Approach:	They propose a unified K-nearest neighbor contrastive learning framework to discover OOD intents . they propose IND pre-training objective to learn discriminative features while maintaining intra-class diversity .
Outcome:	The proposed framework improves on three benchmark datasets.

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Xuanli He, Lingjuan Lyu, Chen Chen, Qiongkai Xu

Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies? (2022.emnlp-main)

Challenge:	Existing pre-trained language models are vulnerable to model extraction attacks . model extraction can cause severe privacy leakage even when victim models are facilitated with state-of-the-art defensive strategies.
Approach:	They propose to launch an attribute-inference attack against an extracted BERT model to prevent privacy leakage.
Outcome:	The proposed attack can cause severe privacy leakage even when victim models are facilitated with state-of-the-art defensive strategies.

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Mitja Nikolaus, Emmanuelle Salin, Stephane Ayache, Abdellah Fourtassi, Benoit Favre

A Multilingual Perspective Towards the Evaluation of Attribution Methods in Natural Language Inference (2022.emnlp-main)

Challenge:	a recent study examines whether vision-and-language models learn syntactic dependencies . a controlled evaluation of the models is crucial for a precise and rigorous test of their knowledge .
Approach:	They propose a task to evaluate understanding of predicate-noun dependencies in a controlled setup.
Outcome:	This study compares state-of-the-art models with a case study on predicate-noun dependencies.

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Kerem Zaman, Yonatan Belinkov

Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging (2022.emnlp-main)

Challenge:	Existing evaluations of attribution methods focus on the English language . plausibility and faithfulness are two main criteria for plausible and faithful attributions .
Approach:	They propose a cross-lingual strategy to measure faithfulness based on word alignments.
Outcome:	The proposed approach eliminates drawbacks of erasure-based evaluations and provides a multilingual dataset with highlights to support future studies.

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Ayyoob ImaniGooghari, Silvia Severini, Masoud Jalili Sabet, François Yvon, Hinrich Schütze

SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller (2022.emnlp-main)

Challenge:	Part-of-Speech (POS) tagging is an important component of the NLP pipeline, but many low-resource languages lack labeled training data.
Approach:	They propose a method for transferring labels from high-resource sources to low-resourced target languages using a graph-based label propagation method.
Outcome:	The proposed method achieves state-of-the-art for unsupervised POS tagging of low-resource languages.

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Zhaowei Wang, Hongming Zhang, Tianqing Fang, Yangqiu Song, Ginny Wong, Simon See

Infinite SCAN: An Infinite Model of Diachronic Semantic Change (2022.emnlp-main)

Challenge:	In this paper, we propose a new task of sub-event generation for an unseen process . we use a framework to generate coherent sub-Event sequences for unseened processes .
Approach:	They propose a task of sub-event generation for an unseen process to evaluate the understanding of the coherence of subevent actions and objects.
Outcome:	The proposed framework can generate coherent sub-event sequences for unseen processes . it can also decode more coherent subevents, demonstrating its effectiveness .

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Seiichi Inoue, Mamoru Komachi, Toshinobu Ogiso, Hiroya Takamura, Daichi Mochihashi

Topic Modeling

Challenge:	Existing methods for capturing semantic changes using word embeddings cannot account for existence of each sense and its relative importance.
Approach:	They propose a Bayesian model that can estimate the number of senses of words and their changes through time using a dynamic topic model and a logistic stick-breaking process.
Outcome:	The proposed model outperforms the baseline model and investigates the semantic changes of several well-known target words using the CCOHA corpus.

Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization (2022.emnlp-main)

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Yuxian Gu, Pei Ke, Xiaoyan Zhu, Minlie Huang

Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues (2022.emnlp-main)

Challenge:	Recent studies have shown that instruction tuning is effective in instruction learning for unseen tasks, but it relies on a large amount of human-annotated samples, which restricts its generalization.
Approach:	They propose an instruction tuning technique which fine-tunes a pre-trained language model on a massive collection of tasks described via human-craft instructions and then tests its generalization ability on unseen tasks.
Outcome:	The proposed method improves IT performance versus labeled data and training tasks by constructing pseudo-labeled data from unlabele . data is used to build a model that can learn from human instructions for zero-shot generalization on unseen tasks.

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Jiao Ou, Jinchao Zhang, Yang Feng, Jie Zhou

SQUIRE: A Sequence-to-sequence Framework for Multi-hop Knowledge Graph Reasoning (2022.emnlp-main)

Challenge:	Existing methods to augment dialogue datasets are labor-intensive and time-consuming . Currently, smoking is harmful for your health.
Approach:	They propose a data augmentation method to augment dialogue responses with different semantics by counterfactual inference.
Outcome:	The proposed method outperforms baselines on multiple downstream tasks.

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Yushi Bai, Xin Lv, Juanzi Li, Lei Hou, Yincen Qu, Zelin Dai, Feiyu Xiong

SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training (2022.emnlp-main)

Challenge:	Existing methods for multi-hop knowledge graph reasoning suffer from slow and poor convergence . a transformer model can be used to learn and predict in an end-to-end fashion, giving faster convergence compared to previous methods .
Approach:	They propose a Sequence-to-sequence based multi-hop reasoning framework . it uses an encoder-decoder transformer structure to translate the query to a path .
Outcome:	The proposed framework can learn and predict in an end-to-end fashion, which gives better and faster convergence.

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Ziqiang Zhang, Long Zhou, Junyi Ao, Shujie Liu, Lirong Dai, Jinyu Li, Furu Wei

Learning Label Modular Prompts for Text Classification in the Wild (2022.emnlp-main)

Challenge:	Existing models for pre-training text and speech are based on unlabeled audio data.
Approach:	They propose a unified-modal speech-unit-text pre-training model that connects speech encoders and text decoders with a shared unit encoder.
Outcome:	The proposed model improves on automatic speech recognition and speech translation tasks and achieves state-of-the-art performance on both the LibriSpeech ASR and MuST-C ST tasks.

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Hailin Chen, Amrita Saha, Shafiq Joty, Steven C.H. Hoi

Unbiased and Efficient Sampling of Dependency Trees (2022.emnlp-main)

Challenge:	Recent advances in parameter efficient tuning of pretrained language models have limited performance.
Approach:	They propose a label-modular prompt tuning framework for text classification tasks that emulates the transient nature of real-world.
Outcome:	The proposed framework outperforms baselines in two formidable settings and shows strong generalisation ability.

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Miloš Stanojević

Continual Learning of Neural Machine Translation within Low Forgetting Risk Regions (2022.emnlp-main)

Challenge:	linguistic constraints in dependency trees are not part of the definition of spanning trees.
Approach:	They propose to use a constraint that requires a single root to be incorporated into dependency tree sampling . they propose to reduce the asymptotic runtime of sampling k trees without replacement to O(kn3)
Outcome:	The proposed algorithms are asymptotically and practically more efficient . they reduce the runtime of the fastest algorithm for sampling with replacement to O(kn3)

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Shuhao Gu, Bojie Hu, Yang Feng

COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models (2022.emnlp-main)

Challenge:	Currently, continuous learning methods suffer from catastrophic forgetting problem, causing model to forget previous knowledge while learning new knowledge.
Approach:	They propose a two-stage continuous learning method based on local features of the real loss to avoid catastrophic forgetting problem.
Outcome:	The proposed method achieves significant improvements on domain adaptation and more challenging language adaptation tasks.

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Bowen Shen, Zheng Lin, Yuanxin Liu, Zhengxiao Liu, Lei Wang, Weiping Wang

Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction (2022.emnlp-main)

Challenge:	Existing statically compressed pre-trained language models lack spatial and temporal efficiency due to their large size and wide width.
Approach:	They propose a spatially and temporally efficient model which retains the major capacity of PLMs.
Outcome:	The proposed model retains the major capacity of pre-trained language models at high compression and acceleration rate with 1/8 parameters and 1/19 FLOPs of BERT.

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Zhen Wan, Qianying Liu, Zhuoyuan Mao, Fei Cheng, Sadao Kurohashi, Jiwei Li

StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning (2022.emnlp-main)

Challenge:	Existing RE models are incapable of handling implicit expressions and long-tail relation types due to language complexity and data sparsity.
Approach:	They propose a method to enhance relation extraction using k nearest neighbors (kNN-RE) kNN is a nearest-neighbor search tool that allows the model to consult training relations at test time .
Outcome:	The proposed model outperforms the best model to date on ACE05, SciERC, and Wiki80 datasets and outperformed the best on i2b2 and Wik80 dataset.

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Hong Chen, Duc Vo, Hiroya Takamura, Yusuke Miyao, Hideki Nakayama

Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference (2022.emnlp-main)

Challenge:	Existing automatic story evaluation methods place a premium on story lexical level coherence, deviating from human preference.
Approach:	They propose a novel Story Evaluation method that mimics human preference when judging a story . the model is based on a well-annotated dataset and a longformer-encoder-decoder .
Outcome:	The proposed method is applicable to machine-generated and human-written stories.

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Eric Mitchell, Joseph Noh, Siyan Li, Will Armstrong, Ananth Agarwal, Patrick Liu, Chelsea Finn, Christopher Manning

Robustness of Demonstration-based Learning Under Limited Data Scenario (2022.emnlp-main)

Challenge:	Existing frameworks for boosting consistency and accuracy of pre-trained NLP models without fine-tuning or re-training are lacking.
Approach:	They propose a framework for boosting the consistency and accuracy of pre-trained NLP models using pre-trainer natural language inference models without fine-tuning or re-training.
Outcome:	The proposed framework boosts consistency and accuracy of pre-trained NLP models using pre-train natural language inference models without fine-tuning or re-training.

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Hongxin Zhang, Yanzhe Zhang, Ruiyi Zhang, Diyi Yang

Modeling Information Change in Science Communication with Semantically Matched Paraphrases (2022.emnlp-main)

Challenge:	Current large pretrained language models struggle to learn NLP tasks under limited data scenarios.
Approach:	They propose to augment input with some demonstrations to improve model performance under limited data scenarios.
Outcome:	The proposed demonstrations improve performance on few-shot NER tasks and show that the length of demonstrations and relevance of random tokens are the main factors affecting the model's performance.

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Dustin Wright, Jiaxin Pei, David Jurgens, Isabelle Augenstein

Word Order Matters When You Increase Masking (2022.emnlp-main)

Challenge:	Whether the media faithfully communicate scientific information has long been a core issue to the science community.
Approach:	They propose to use the SCIENTIFIC PARAPHRASE AND INFORMATION CHANGE DATASET to identify paraphrased scientific findings annotated for degree of information change to enable large-scale tracking and analysis of information changes in science communication.
Outcome:	The proposed dataset contains 6,000 scientific finding pairs extracted from news stories, social media discussions, and full texts of original papers.

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Karim Lasri, Alessandro Lenci, Thierry Poibeau

An Empirical Analysis of Memorization in Fine-tuned Autoregressive Language Models (2022.emnlp-main)

Challenge:	Recent work shows that word order information is not always useful for pre-trained models to solve downstream tasks.
Approach:	They examine the effect of removing position encodings on the pre-training objective itself to see whether models can reconstruct position information from co-occurrences.
Outcome:	The proposed model can reconstruct position information from co-occurrences without explicit encoding, but not on some tasks.

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Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang, David Evans, Taylor Berg-Kirkpatrick

Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition (2022.emnlp-main)

Challenge:	Large language models are shown to present privacy risks through memorization of training data, but little attention has been given to the fine-tuning phase.
Approach:	They empirically study memorization of fine-tuning methods using membership inference and extraction attacks and show that fine-timing the head of the model has the highest susceptibility to attacks.
Outcome:	The proposed methods have the highest memorization risk, whereas the smaller adapters are less vulnerable to known extraction attacks.

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Shuguang Chen, Leonardo Neves, Thamar Solorio

Named Entity Recognition

Challenge:	Existing methods to increase training data in low-resource domains may not be effective due to data scarcity.
Approach:	They propose a method to transform a high-resource domain into a low-resourced domain by changing its style-related attributes to generate synthetic data for training.
Outcome:	The proposed method can significantly improve results on five domain pairs under different data regimes.

Linguistic Corpus Annotation for Automatic Text Simplification Evaluation (2022.emnlp-main)

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Rémi Cardon, Adrien Bibal, Rodrigo Wilkens, David Alfter, Magali Norré, Adeline Müller, Watrin Patrick, Thomas François

Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs (2022.emnlp-main)

Challenge:	Evaluating automatic text simplification systems is a difficult task that is performed either by automatic metrics or user-based evaluations.
Approach:	They propose to use annotations of the ASSET corpus to analyze SARI’s behavior and to re-evaluate existing ATS systems.
Outcome:	The proposed methods can be used to analyze SARI’s behavior and to re-evaluate existing ATS systems.

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Wentao Ding, Hao Chen, Huayu Li, Yuzhong Qu

There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning (2022.emnlp-main)

Challenge:	Existing methods for temporal question answering ignore intrinsic connections between events that can make them temporally related.
Approach:	They propose a temporal question answering method that generates query graphs by exploring relevant facts of mentioned entities.
Outcome:	The proposed method outperforms existing methods on two benchmarks over different knowledge graphs.

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Xueliang Zhao, Tingchen Fu, Chongyang Tao, Rui Yan

Stop Measuring Calibration When Humans Disagree (2022.emnlp-main)

Challenge:	Existing methods emphasize selecting one golden knowledge given a particular dialogue context, overlooking the one-to-many phenomenon in dialogue.
Approach:	They propose to use a multi-reference dataset to assess the one-to-many efficacy of existing KGC models.
Outcome:	The proposed model improves the mapping relationship between multiple knowledge and multiple responses by optimizing the model in a wake-sleep style.

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Joris Baan, Wilker Aziz, Barbara Plank, Raquel Fernandez

Improving compositional generalization for multi-step quantitative reasoning in question answering (2022.emnlp-main)

Challenge:	Calibration is a popular framework to evaluate whether a classifier knows when it does not know - i.e., predictive probabilities are a good indication of how likely a prediction is to be correct.
Approach:	They propose to measure calibration to human majority given inherent disagreements on tasks where humans inherently disagree about which class applies.
Outcome:	The proposed measures capture key statistical properties of human judgements including class frequency, ranking and entropy.

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Armineh Nourbakhsh, Cathy Jiao, Sameena Shah, Carolyn Rosé

A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection (2022.emnlp-main)

Challenge:	Quantitative reasoning is an important aspect of question answering when numeric and verbal cues interact to indicate sophisticated, multi-step programs.
Approach:	They propose a method that encourages QA models to adjust attention patterns and capture input/output alignments that are meaningful to the reasoning task.
Outcome:	The proposed approach improves program accuracy and renders models more robust against overfitting as the number of reasoning steps grows.

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Adam Wiemerslage, Shiran Dudy, Katharina Kann

Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent? (2022.emnlp-main)

Challenge:	Neural networks are at the center of a debate about human behavior in inflectional morphology.
Approach:	They measure correlation between human judgments and neural network probabilities for unknown word inflections.
Outcome:	The proposed model for morphological inflections correlates best with human wug ratings, but not with humans.

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Pradip Pramanick, Chayan Sarkar

AfroLID: A Neural Language Identification Tool for African Languages (2022.emnlp-main)

Challenge:	ASR systems are often unable to recognize speech due to generic datasets and open-vocabulary modeling.
Approach:	They propose to incorporate a robot’s visual information into an ASR system and improve the recognition of a spoken utterance containing a visible entity.
Outcome:	The proposed method achieves a 59% relative reduction in WER from an unmodified ASR system.

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Ife Adebara, AbdelRahim Elmadany, Muhammad Abdul-Mageed, Alcides Inciarte

EvEntS ReaLM: Event Reasoning of Entity States via Language Models (2022.emnlp-main)

Challenge:	AfroLID is a neural LID toolkit for 517 African languages and varieties.
Approach:	They propose to exploit a multi-domain web dataset manually curated from across 14 language families utilizing five orthographic systems to exploit AfroLID.
Outcome:	The proposed tool outperforms existing tools on the acutely under-served Twitter domain.

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Evangelia Spiliopoulou, Artidoro Pagnoni, Yonatan Bisk, Eduard Hovy

Large language models are few-shot clinical information extractors (2022.emnlp-main)

Challenge:	Existing approaches to model event implications fail to reason about the world, despite their knowledge of physical attributes.
Approach:	They propose to use a model prompting technique to prompt models of event implications by targeting their understanding of physical attributes.
Outcome:	The proposed model prompting technique is especially useful for unseen attributes or when only limited data is available.

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Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, David Sontag

Towards a Unified Multi-Dimensional Evaluator for Text Generation (2022.emnlp-main)

Challenge:	a long-running goal of clinical NLP is the extraction of important variables trapped in clinical notes.
Approach:	They propose to use large language models to tackle diverse clinical extraction tasks . they propose to reannote existing CASI datasets to compare their models with clinical text.
Outcome:	The proposed models outperform existing models on few-shot clinical information extraction tasks.

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Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, Pengfei Liu, Chenguang Zhu, Heng Ji, Jiawei Han

GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models (2022.emnlp-main)

Challenge:	Existing evaluation frameworks for natural language generation are dominated by similarity-based metrics.
Approach:	They propose a multi-dimensional evaluator for natural language generation that integrates multiple dimensions into one evaluer.
Outcome:	The proposed evaluator improves on three typical NLG tasks and improves with external knowledge.

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Da Yin, Hritik Bansal, Masoud Monajatipoor, Liunian Harold Li, Kai-Wei Chang

The (Undesired) Attenuation of Human Biases by Multilinguality (2022.emnlp-main)

Challenge:	Recent work shows that Pre-trained Language Models store relational knowledge and utilize it for performing downstream tasks.
Approach:	They propose a benchmark dataset for probing the diversity of relational knowledge in multilingual PLMs.
Outcome:	The proposed dataset contains 3125 prompts in English, Chinese, Hindi, Persian, and Swahili . larger multilingual PLMs variants do not store geo-diverse concepts better than its smaller variant .

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Cristina España-Bonet, Alberto Barrón-Cedeño

Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning (2022.emnlp-main)

Challenge:	odor pleasantness perception is universal, but cultural biases are not always present in embedding models . et al., 2018: a new study shows that cultural bias is not always the case in embedded models based on human texts .
Approach:	They propose multilingual cultural aware tests to quantify biases in embedding models . they find that biased models are more likely to be multilingual than monolingual ones .
Outcome:	The results show that human preferences are not always universal . they also show that multilinguality reverses biases, despite differences in training corpus .

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Oyvind Tafjord, Bhavana Dalvi Mishra, Peter Clark

Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets (2022.emnlp-main)

Challenge:	a system that can show how its answers are implied by its own internal beliefs via a systematic chain of reasoning would allow better understanding of why a model produced the answer it did.
Approach:	They propose to combine a backward-chaining model with a verifier that checks that the model itself believes those premises through self-querying to generate multistep chains that are both faithful (the answer follows from the reasoning)
Outcome:	The proposed model generates chains that are faithful and truthful while maintaining answer accuracy.

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Philippe Laban, Chien-Sheng Wu, Wenhao Liu, Caiming Xiong

ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection (2022.emnlp-main)

Challenge:	Existing methods for evaluating progress in natural language generation tasks are expensive, difficult to reproduce, and non-reusable.
Approach:	They propose a new automatic evaluation method for NLG called Near-Negative Distinction that repurposes prior human annotations into NND tests.
Outcome:	The proposed method achieves higher correlation with human judgments than standard NLG evaluation metrics.

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Badr AlKhamissi, Faisal Ladhak, Srinivasan Iyer, Veselin Stoyanov, Zornitsa Kozareva, Xian Li, Pascale Fung, Lambert Mathias, Asli Celikyilmaz, Mona Diab

Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations (2022.emnlp-main)

Challenge:	Hate speech detection is complex and requires commonsense reasoning and social nuance . prior work has shown that even humans cannot achieve a high agreement on whether a post constitutes HS .
Approach:	They frame a few-shot learning task to decompose a hate speech detection task into its "constituent" parts. they show that infusing commonsense knowledge from reasoning datasets improves the performance even further.
Outcome:	The proposed method outperforms baseline methods in the 16-shot case.

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Swarnadeep Saha, Peter Hase, Nazneen Rajani, Mohit Bansal

Stanceosaurus: Classifying Stance Towards Multicultural Misinformation (2022.emnlp-main)

Challenge:	Recent work on explainable NLP has shown that few-shot prompting can enable large pre-trained language models (LLMs) to generate grammatical and factual explanations for data labels.
Approach:	They propose to use few-shot prompting to generate grammatical and factual explanations for data labels by varying the hardness of the test samples and in-context samples to investigate the link between explainability and sample hardness.
Outcome:	The proposed model can generate grammatical and factual explanations for data labels with few-shot prompting.

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Jonathan Zheng, Ashutosh Baheti, Tarek Naous, Wei Xu, Alan Ritter

Gendered Mental Health Stigma in Masked Language Models (2022.emnlp-main)

Challenge:	Existing corpora focus on misinformation spreading within western countries.
Approach:	They present a new corpus of tweets annotated with stance towards 250 misinformation claims.
Outcome:	The proposed method achieves 53.1 F1 on Hindi and 50.4 F1 in Arabic without any target-language fine-tuning.

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Inna Lin, Lucille Njoo, Anjalie Field, Ashish Sharma, Katharina Reinecke, Tim Althoff, Yulia Tsvetkov

Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization (2022.emnlp-main)

Challenge:	Mental health stigma prevents many individuals from receiving appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men.
Approach:	They propose to use clinical psychology literature to curate prompts, then evaluate models’ propensity to generate gendered words.
Outcome:	The proposed framework captures stigma about gender in mental health and is more likely to predict female subjects than male in sentences about mental health conditions (32% vs. 19%), and this disparity is exacerbated for sentences that indicate treatment-seeking behavior.

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Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum

Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models (2022.emnlp-main)

Challenge:	Efficient k-nearest neighbor search is a fundamental task, foundational for many problems in NLP.
Approach:	They propose an approach that avoids the use of a dual-encoder for retrieval, relying solely on the cross-encoding model.
Outcome:	Empirically, for k > 10, our approach provides test-time recall-vs-computational cost trade-offs superior to the current widely-used methods that re-rank items retrieved using a dual-encoder or TF-IDF.

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Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky

Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts (2022.emnlp-main)

Challenge:	a new method for textual style transfer is proposed for text with a limited set of style choices . textual styles are a complex task that requires specialized models to perform .
Approach:	They propose a method for arbitrary textual style transfer using pre-trained language models . they use a mathematical formulation of the TST task, decomposing it into three components .
Outcome:	The proposed method performs on par with state-of-the-art large-scale models while using less compute and memory.

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Ben Zhou, Kyle Richardson, Xiaodong Yu, Dan Roth

Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality (2022.emnlp-main)

Challenge:	Existing approaches to end-to-end questionanswering assume that pre-trained language can decompose complex tasks into more straightforward sub-tasks.
Approach:	They propose to use distant supervision to train decomposition-based transformers for large-scale parallel news.
Outcome:	The proposed model improves on semantic parsing and on hotpotQA and strategyQA datasets by 20% to 30%.

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Anuj Diwan, Layne Berry, Eunsol Choi, David Harwath, Kyle Mahowald

Gradient-based Constrained Sampling from Language Models (2022.emnlp-main)

Challenge:	Recent visuolinguistic pre-trained models fail miserably on the Winoground dataset, which challenges models to match paired images and English captions.
Approach:	They propose to annotate a Winoground dataset that challenges visuolinguistic models to match paired images and English captions with items constructed to overlap lexically but differ in meaning.
Outcome:	The proposed dataset challenges models to match paired images and English captions with items constructed to overlap lexically but differ in meaning.

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Sachin Kumar, Biswajit Paria, Yulia Tsvetkov

TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data (2022.emnlp-main)

Challenge:	Large pretrained language models are successful at generating fluent text but are notoriously hard to controllably sample from.
Approach:	They propose a sampling procedure that combines the log-likelihood of the language model with arbitrary constraints in a single energy function and then generates samples in . non-autoregressive manner.
Outcome:	The proposed method improves on text generation with soft and hard constraints and keyword-guided generation.

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Fan Zhou, Mengkang Hu, Haoyu Dong, Zhoujun Cheng, Fan Cheng, Shi Han, Dongmei Zhang

Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence (2022.emnlp-main)

Challenge:	Existing auto-regressive pre-trained language models are challenged by recent emerging numerical reasoning datasets due to the error-prone implicit calculation.
Approach:	They propose a pre-computation tool to pre-compute aggregation/arithmetic results for the table in advance, so they are handy and readily available for PLMs to answer numerical reasoning questions.
Outcome:	The proposed model improves on TAT-QA and T5 and BART-large on multiple benchmarks.

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Hung-Ting Chen, Michael Zhang, Eunsol Choi

QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation (2022.emnlp-main)

Challenge:	Existing work on question answering models relies on retrieved documents for provenance, but recent studies show that models can retain vast amounts of factual knowledge . retrieval-based generation approaches combine parametric knowledge sources with a large number of retrieved evidence documents, achieving state-of-the-art performance on open retrieval datasets.
Approach:	They propose to use parametric and parametric knowledge to generate free-form questions from retrieved evidence documents.
Outcome:	The proposed model can use parametric and parametric knowledge to generate free-form answers from retrieved evidence documents.

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Zhenrui Yue, Huimin Zeng, Bernhard Kratzwald, Stefan Feuerriegel, Dong Wang

When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain (2022.emnlp-main)

Challenge:	Question answering models often suffer from performance deterioration upon deployment .
Approach:	They propose a self-supervised framework called QADA for QA domain adaptation . they propose to augment training QA samples with hidden space augmentation .
Outcome:	The proposed framework improves on multiple target datasets over state-of-the-art methods.

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Raj Shah, Kunal Chawla, Dheeraj Eidnani, Agam Shah, Wendi Du, Sudheer Chava, Natraj Raman, Charese Smiley, Jiaao Chen, Diyi Yang

Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer (2022.emnlp-main)

Challenge:	Pre-trained language models have shown impressive performance on a variety of tasks and domains.
Approach:	They propose a domain specific financial LANGuage model which uses financial keywords and phrases for better masking.
Outcome:	The proposed model outperforms existing models on a variety of tasks and domains.

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Zhengbao Jiang, Luyu Gao, Zhiruo Wang, Jun Araki, Haibo Ding, Jamie Callan, Graham Neubig

Reproducibility in Computational Linguistics: Is Source Code Enough? (2022.emnlp-main)

Challenge:	eschewing separate architecture and training for knowledge-intensive tasks is cumbersome . end-to-end training only based on supervision from the end task is awkward .
Approach:	They propose a single Transformer that performs retrieval as attention and end-to-end training solely based on supervision from the end QA task.
Outcome:	The proposed model outperforms state-of-the-art retrievers and readers on in-domain datasets.

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Mohammad Arvan, Luís Pina, Natalie Parde

Generating Information-Seeking Conversations from Unlabeled Documents (2022.emnlp-main)

Challenge:	a recent study finds that source code releases leave much to be desired . authors can include small-scale experiments and explicit scripts to generate each result .
Approach:	They propose that conferences require self-contained artifacts to evaluate their reproducibility . authors suggest authors include small-scale experiments and explicit scripts to generate results .
Outcome:	The authors find that source code releases leave much to be desired . authors can include small-scale experiments and explicit scripts to generate each result .

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Gangwoo Kim, Sungdong Kim, Kang Min Yoo, Jaewoo Kang

Distill The Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation (2022.emnlp-main)

Challenge:	a novel framework for conversational question answering from unlabeled documents has been proposed . a large-scale dataset of synthetic conversations is available for use in real-world applications .
Approach:	They propose a framework for conversational question answering from unlabeled documents . they propose 'SimSeek' framework that simulates conversation from unlabelled documents based on two scenarios .
Outcome:	The proposed framework achieves state-of-the-art performance on a recent CQA benchmark, QuAC.

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Ru Peng, Yawen Zeng, Jake Zhao

A Multifaceted Framework to Evaluate Evasion, Content Preservation, and Misattribution in Authorship Obfuscation Techniques (2022.emnlp-main)

Challenge:	Existing studies on multimodal machine translation (MMT) have focused on the fusion and alignment of images and texts to improve MMT.
Approach:	They propose an image-free inference framework that supports image-based inference via an inversion knowledge distillation scheme.
Outcome:	The proposed framework is the first to rival or surpass image-must frameworks on the multimodal translation benchmark.

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Malik Altakrori, Thomas Scialom, Benjamin C. M. Fung, Jackie Chi Kit Cheung

SafeText: A Benchmark for Exploring Physical Safety in Language Models (2022.emnlp-main)

Challenge:	Authorship obfuscation techniques are often evaluated based on their ability to hide the author’s identity (evasion) while preserving the content of the original text.
Approach:	They propose to evaluate authorship obfuscation techniques on detection evasion and content preservation using competitive identification techniques in real-life scenarios.
Outcome:	The proposed method reveals key weaknesses in state-of-the-art obfuscation techniques and surprisingly competitive effectiveness from a back-translation baseline in all evaluation aspects.

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Sharon Levy, Emily Allaway, Melanie Subbiah, Lydia Chilton, Desmond Patton, Kathleen McKeown, William Yang Wang

Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations (2022.emnlp-main)

Challenge:	Existing models that generate unsafe text are susceptible to the dangers of unsafe text generation and are deemed unsafe.
Approach:	They use a dataset to empirically study commonsense physical safety across various models for text generation and reasoning tasks.
Outcome:	The proposed model can generate unsafe text and reject it, but the different harms that can occur do not receive equal attention, which may consequently downplay certain harms.

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Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, Hyunsoo Cho, Hwiyeol Jo, Sang-Woo Lee, Sang-goo Lee, Taeuk Kim

D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat (2022.emnlp-main)

Challenge:	Intuitively, ground-truth labels should have as much impact in in-context learning as supervised learning, but the impact of the quality of demonstrations remains elusive.
Approach:	They propose to measure input-label correspondence and ground-truth label effect ratio . they propose to use verbosity of prompt templates and language model size as controlling factors .
Outcome:	The proposed metrics show that ground-truth labels have less impact than previously thought . the authors identify key components as controlling factors to achieve noise-resilient ICL .

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Binwei Yao, Chao Shi, Likai Zou, Lingfeng Dai, Mengyue Wu, Lu Chen, Zhen Wang, Kai Yu

Exploiting domain-slot related keywords description for Few-Shot Cross-Domain Dialogue State Tracking (2022.emnlp-main)

Challenge:	Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness.
Approach:	They propose to construct a Chinese Dialogue Dataset for depression-diagnosis-oriented chat based on clinical depression diagnostic criteria.
Outcome:	The proposed system can be used to diagnose depression using a Chinese Dialogue Dataset.

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Gao Qixiang, Guanting Dong, Yutao Mou, Liwen Wang, Chen Zeng, Daichi Guo, Mingyang Sun, Weiran Xu

CoCoa: An Encoder-Decoder Model for Controllable Code-switched Generation (2022.emnlp-main)

Challenge:	Existing frameworks for dialogue state tracking with domain-slot-value labels are expensive . current models are limited due to high cost of data annotation and lack of data in some domains .
Approach:	They propose a framework based on domain-slot related description to tackle the challenge of few-shot cross-domain DST.
Outcome:	The proposed framework outperforms existing methods on MultiWOZ and gains strong slot accuracy compared to existing models.

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Sneha Mondal, null Ritika, Shreya Pathak, Preethi Jyothi, Aravindan Raghuveer

Towards Climate Awareness in NLP Research (2022.emnlp-main)

Challenge:	Generating code-switched text with fine-grained control on the degree of code-witching and the lexical choices used to convey formality has been well-explored.
Approach:	They propose to generate code-switched text with fine-grained control on the degree of code-changing and lexical choices used to convey formality.
Outcome:	The proposed model can be invoked at test-time to synthesize code-switched text faithful to syntactic and lexical attributes relevant to code-witching.

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Daniel Hershcovich, Nicolas Webersinke, Mathias Kraus, Julia Bingler, Markus Leippold

Navigating Connected Memories with a Task-oriented Dialog System (2022.emnlp-main)

Challenge:	Increasing focus on efficient AI and NLP research lacks systematic climate reporting guidelines . a proposed model card would be practical with limited information about experiments and the underlying computer hardware.
Approach:	They propose a model card that is practically usable with limited information about experiments and the underlying computer hardware.
Outcome:	The proposed model card would be usable with limited information about experiments and the underlying computer hardware.

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Satwik Kottur, Seungwhan Moon, Alborz Geramifard, Babak Damavandi

Language Model Decomposition: Quantifying the Dependency and Correlation of Language Models (2022.emnlp-main)

Challenge:	Recent years have seen an increasing trend in the volume of personal media captured by users thanks to smartphones and smart glasses.
Approach:	They propose to use dialogs for connected memories to query media collection . they use a multimodal dialog simulator and manual paraphrasing to obtain natural language utterances.
Outcome:	The proposed dataset contains 11.5k userassistant dialogs grounded in simulated personal memory graphs.

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Hao Zhang

SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser (2022.emnlp-main)

Challenge:	Pre-trained language models (LMs) have led to significant improvements on various NLP tasks in past years, but a theoretical framework for studying their relationships is still missing.
Approach:	They propose to use language model decomposition to represent a set of pre-trained LMs and derive a closed-form solution.
Outcome:	The proposed model is based on a language model decomposition (LMD) and its variants.

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Yue Zhang, Bo Zhang, Zhenghua Li, Zuyi Bao, Chen Li, Min Zhang

Varifocal Question Generation for Fact-checking (2022.emnlp-main)

Challenge:	Existing approaches to grammatical error correction are unreliable when processing ungrammatically . a new approach is proposed that incorporates dependency syntactic information into the encoder part of GEC models.
Approach:	They propose a syntax-enhanced grammatical error correction approach called SynGEC that incorporates dependency syntactic information into the encoder part of GEC models.
Outcome:	The proposed approach outperforms strong baselines and achieves competitive performance on mainstream English and Chinese GEC datasets.

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Nedjma Ousidhoum, Zhangdie Yuan, Andreas Vlachos

Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport (2022.emnlp-main)

Challenge:	Recent question generation approaches assume that the answer is known . however, such passages are what is being sought when verifying a claim.
Approach:	They propose a method that generates questions based on different focal points within a claim . they demonstrate that the method generates more relevant and informative questions .
Outcome:	The proposed method outperforms previous work on a fact-checking question generation dataset on measurable evaluation metrics.

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Kelly Marchisio, Ali Saad-Eldin, Kevin Duh, Carey Priebe, Philipp Koehn

Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection (2022.emnlp-main)

Challenge:	Existing literature on bilingual lexicon induction fails in low-resource scenarios . a language dataset is considered low- resource based on its own embedding space .
Approach:	They propose a graph-matching method that improves bilingual lexicon induction performance across 40 language pairs using optimal transport.
Outcome:	The proposed method is especially strong with low amounts of supervision.

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Suchin Gururangan, Dallas Card, Sarah Dreier, Emily Gade, Leroy Wang, Zeyu Wang, Luke Zettlemoyer, Noah A. Smith

ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction (2022.emnlp-main)

Challenge:	Language models rely on massive web crawls for diverse text data, but are rife with undesirable content.
Approach:	They analyze newspaper articles written by students from across the country to determine whose language is preferred by a quality filter.
Outcome:	The results show that newspapers from wealthier, educated, and urban zones are more likely to be classified as high quality.

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Weiwen Xu, Yang Deng, Wenqiang Lei, Wenlong Zhao, Tat-Seng Chua, Wai Lam

Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU (2022.emnlp-main)

Challenge:	Existing CCE methods treat contracts as plain text, creating a barrier to understanding complex contracts.
Approach:	They propose a framework to model implicit relations in legal contracts to improve contract understanding . they propose Term-Definition Relation captures the relation between important terms and their definitions .
Outcome:	The proposed framework improves on two CCE tasks in conventional and zero-shot settings.

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Fenia Christopoulou, Gerasimos Lampouras, Ignacio Iacobacci

Revisiting Parameter-Efficient Tuning: Are We Really There Yet? (2022.emnlp-main)

Challenge:	Current approaches for NLU use CL to improve in-distribution data performance via heuristic-oriented or task-agnostic difficulties.
Approach:	They propose to use CL to improve in-distribution data performance by taking advantage of training dynamics as difficulty metrics instead of heuristic-oriented or task-agnostic difficulties.
Outcome:	The proposed model schedulers improve on in-distribution, out-of-distortion and zero-shot cross-lingual transfer datasets while being 20% faster on average.

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Guanzheng Chen, Fangyu Liu, Zaiqiao Meng, Shangsong Liang

Transfer Learning from Semantic Role Labeling to Event Argument Extraction with Template-based Slot Querying (2022.emnlp-main)

Challenge:	Pretrained language models (PLMs) are used as backbones to be combined with additional parameters and finetuned on downstream tasks in an end-to-end manner.
Approach:	They propose to use a fraction of parameters to tune pretrained language models (PLMs) this is the first comprehensive investigation into the training and evaluation of PETuning methods.
Outcome:	The proposed methods have been validated and tested with a rigorous evaluation protocol and have shown that they are unstable and inconsistent.

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Zhisong Zhang, Emma Strubell, Eduard Hovy

Calibrating Zero-shot Cross-lingual (Un-)structured Predictions (2022.emnlp-main)

Challenge:	Existing methods for event argument extraction are limited by the limited amount of annotations available.
Approach:	They propose to use SRL annotations for event argument extraction . they propose to specify natural language-like queries to tackle label mismatch problem .
Outcome:	The proposed model achieves impressive zero-shot results on English benchmarks . it also provides benefits in low-resource cases, where few annotations are available .

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Zhengping Jiang, Anqi Liu, Benjamin Van Durme

PRINCE: Prefix-Masked Decoding for Knowledge Enhanced Sequence-to-Sequence Pre-Training (2022.emnlp-main)

Challenge:	Existing need for model calibration when natural language models are deployed in critical tasks.
Approach:	They compare model calibration methods in a context of zero-shot cross-lingual transfer with pre-trained language models.
Outcome:	The proposed method fails to calibrate more complex confidence estimations in structured predictions compared to expressive alternatives like Gaussian Process Calibration.

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Song Xu, Haoran Li, Peng Yuan, Youzheng Wu, Xiaodong He

How Far are We from Robust Long Abstractive Summarization? (2022.emnlp-main)

Challenge:	Existing studies focus on injecting noises into the input sequence, but feasibility of injecting them into the decoding sequence remains an open question.
Approach:	They propose a pre-training paradigm that integrates knowledge-enhanced decoding with noises in the prefix to strengthen the representation learning of entities that span over multiple input tokens.
Outcome:	The proposed model achieves state-of-the-art results on two knowledge-driven data-to-text generation tasks with up to 2% BLEU gains.

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Huan Yee Koh, Jiaxin Ju, He Zhang, Ming Liu, Shirui Pan

Measuring Context-Word Biases in Lexical Semantic Datasets (2022.emnlp-main)

Challenge:	Abstractive summarization has made tremendous progress in recent years . however, even under a short document setting, abstractive models often generate summaries that are repetitive, ungrammatical, and factually inconsistent with the source.
Approach:	They perform fine-grained human annotations to evaluate long document abstractive summarization systems and develop factual consistency metrics.
Outcome:	The proposed model can generate more relevant summaries but not factual ones.

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Qianchu Liu, Diana McCarthy, Anna Korhonen

Iteratively Prompt Pre-trained Language Models for Chain of Thought (2022.emnlp-main)

Challenge:	Existing pretrained contextualized models have been used to evaluate word-in-context representations in many lexical semantic tasks.
Approach:	They propose to quantify the degree of context or word biases in existing datasets by probing masked input.
Outcome:	The proposed model performs better when both word and context are available than with masked input.

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Boshi Wang, Xiang Deng, Huan Sun

Unobserved Local Structures Make Compositional Generalization Hard (2022.emnlp-main)

Challenge:	Pre-trained language models (PLMs) internalize a great amount of knowledge, but have been shown incapable of recalling this knowledge to solve complex & multi-step reasoning tasks.
Approach:	They propose an iterative prompting framework which progressively elicits relevant knowledge from PLMs for multi-step inference.
Outcome:	The proposed prompting framework outperforms existing prompting methods on three datasets involving multi-step reasoning.

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Ben Bogin, Shivanshu Gupta, Jonathan Berant

Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning (2022.emnlp-main)

Challenge:	Recent studies show sequence-to-sequence models struggle to generalize to new compositions . little is known on what makes generalization hard on a particular test instance .
Approach:	They propose a criterion for the difficulty of an example that is hard if it contains a local structure that was not observed at training time.
Outcome:	The proposed rule predicts instance-level generalization well across 5 different datasets.

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Xiaobao Wu, Anh Tuan Luu, Xinshuai Dong

Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling (2022.emnlp-main)

Challenge:	Existing topic models do not make full use of word co-occurrence information to model latent topics.
Approach:	They propose a novel short text topic modeling framework, Topic-Semantic Contrastive Topic Model, which uses augmented data and the data characteristic to learn the relations among samples.
Outcome:	The proposed framework outperforms state-of-the-art baselines regardless of the data augmentation availability, producing high-quality topics and topic distributions.

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Yiyang Li, Hai Zhao, Zhuosheng Zhang

Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimates (2022.emnlp-main)

Challenge:	Existing studies on dialogue modeling use pre-trained language models to encode dialogue history as successive tokens, which is insufficient in capturing the temporal characteristics of dialogues.
Approach:	They propose a bidirectional information decoupling network as a universal dialogue encoder which explicitly incorporates both the past and future contexts.
Outcome:	The proposed model incorporates past and future contexts and can be generalized to a wide range of dialogue-related tasks.

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Dongfang Li, Baotian Hu, Qingcai Chen

Non-Autoregressive Neural Machine Translation: A Call for Clarity (2022.emnlp-main)

Challenge:	Existing methods to improve confidence calibration of pre-trained language models are still a mystery.
Approach:	They propose a method that leverages model explanations to make models less confident with non-inductive attributions.
Outcome:	The proposed method improves confidence calibration in all settings and reduces calibration errors when combined with temperature scaling.

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Robin Schmidt, Telmo Pires, Stephan Peitz, Jonas Lööf

RED-ACE: Robust Error Detection for ASR using Confidence Embeddings (2022.emnlp-main)

Challenge:	Non-autoregressive translation models require a single forward pass to generate the output sequence instead of iteratively producing each predicted token.
Approach:	They propose to use a single forward pass to generate the output sequence instead of iteratively producing each predicted token.
Outcome:	The proposed models improve translation quality and speed under third-party testing environments.

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Zorik Gekhman, Dina Zverinski, Jonathan Mallinson, Genady Beryozkin

Fast-R2D2: A Pretrained Recursive Neural Network based on Pruned CKY for Grammar Induction and Text Representation (2022.emnlp-main)

Challenge:	ASR Error Detection (AED) models post-process the output of Automatic Speech Recognition systems, in order to detect transcription errors.
Approach:	They propose to use ASR model's word-level confidence scores to combine ASR models with transcribed text to improve AED performance.
Outcome:	The proposed models combine the confidence scores and transcribed text into a contextualized representation.

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Xiang Hu, Haitao Mi, Liang Li, Gerard de Melo

A Localized Geometric Method to Match Knowledge in Low-dimensional Hyperbolic Space (2022.emnlp-main)

Challenge:	Chart-based models have shown great potential in unsupervised grammar induction, running recursively and hierarchically, but requiring O(n3) time-complexity.
Approach:	They propose a model-guided pruning method that scales to large language model pretraining by introducing a heuristic pruning method.
Outcome:	The proposed method significantly improves grammar induction quality and achieves competitive results in downstream tasks.

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Bo Hui, Tian Xia, Wei-Shinn Ku

Memory-assisted prompt editing to improve GPT-3 after deployment (2022.emnlp-main)

Challenge:	Existing methods for entity alignment are limited to Euclidean space and hyperbolic embedding can represent hierarchical structure in knowledge graphs.
Approach:	They propose a localized geometric method to find equivalent entities in hyperbolic space using a hyperbolical neural network.
Outcome:	The proposed method outperforms the state-of-the-art by a large margin.

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Aman Madaan, Niket Tandon, Peter Clark, Yiming Yang

LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation (2022.emnlp-main)

Challenge:	Large LMs such as GPT-3 can commit mistakes that are obvious to humans, such as interpreting “What word is similar to good?” to mean a homophone, while the user intended a synonym.
Approach:	They pair GPT-3 with a growing memory of cases where the model misunderstood the user’s intents, along with user feedback for clarification.
Outcome:	The proposed model can correct misunderstandings on four lexical tasks and two advanced ethical reasoning tasks without retraining.

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Hongcheng Guo, Jiaheng Liu, Haoyang Huang, Jian Yang, Zhoujun Li, Dongdong Zhang, Zheng Cui

PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning (2022.emnlp-main)

Challenge:	Recent advances struggle to train a separate model for each language pair, which is costly and unaffordable when the number of languages increases in the real world.
Approach:	They propose to train different MMT models to support translations between different languages.
Outcome:	The proposed model is able to handle the above issues by providing a shared semantic space for multiple languages.

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Zifeng Wang, Jimeng Sun

ROSE: Robust Selective Fine-tuning for Pre-trained Language Models (2022.emnlp-main)

Challenge:	Existing methods for generating longitudinal multimodal EHRs are limited due to privacy concerns.
Approach:	They propose to generate longitudinal multimodal EHRs by unconditional generation or longitudinal inference . existing methods generate single-modal E HRs by conditional generation or by longitudinal inferment .
Outcome:	The proposed method is more flexible and controllable than existing methods and is more cost-effective than existing ones.

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Lan Jiang, Hao Zhou, Yankai Lin, Peng Li, Jie Zhou, Rui Jiang

CodeRetriever: A Large Scale Contrastive Pre-Training Method for Code Search (2022.emnlp-main)

Challenge:	Recent studies have highlighted the lack of adversarial robustness in pre-trained models.
Approach:	They propose a fine-tuning approach that conducts selective updates when adapting pre-trained models to downstream tasks.
Outcome:	The proposed approach improves adversarial robustness on downstream tasks . it eliminates spurious updates, leading to flatter and wider optima than the conventional method .

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Xiaonan Li, Yeyun Gong, Yelong Shen, Xipeng Qiu, Hang Zhang, Bolun Yao, Weizhen Qi, Daxin Jiang, Weizhu Chen, Nan Duan

Open-Topic False Information Detection on Social Networks with Contrastive Adversarial Learning (2022.emnlp-main)

Challenge:	Existing code pre-training approaches often adopt (masked) language modeling as the training objective which targets on learning to predict (macked) tokens in a given code context.
Approach:	They propose a code-text contrastive learning model which learns function-level code semantic representations through large-scale code corpus.
Outcome:	The proposed model achieves new state-of-the-art with significant improvement over existing pre-trained models on eleven domain/language-specific code search tasks with six programming languages in different code granularity.

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Guanghui Ma, Chunming Hu, Ling Ge, Hong Zhang

Mitigating Inconsistencies in Multimodal Sentiment Analysis under Uncertain Missing Modalities (2022.emnlp-main)

Challenge:	Existing models for false information detection on social networks are too harsh for actual social networks that contain both seen and unseen topics simultaneously.
Approach:	They propose an open-topic scenario that assumes that all test data topics are seen or unseen by the model, but which is too harsh for actual social networks that contain both seen and unseened topics simultaneously.
Outcome:	The proposed model improves on two benchmark datasets and a variety of graph neural networks on two social networks and shows that it is more accurate than existing models.

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Jiandian Zeng, Jiantao Zhou, Tianyi Liu

ConvTrans: Transforming Web Search Sessions for Conversational Dense Retrieval (2022.emnlp-main)

Challenge:	Existing studies ignore the inconsistency phenomenon of missing modality in multimodal sentiment analysis . neglect of missing modalities may lead to incorrect semantic results .
Approach:	They propose an ensemble-based Missing Modality Reconstruction network to detect and recover missing modality features.
Outcome:	The proposed method is superior to existing methods on CMU-MOSI and IEMOCAP datasets.

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Kelong Mao, Zhicheng Dou, Hongjin Qian, Fengran Mo, Xiaohua Cheng, Zhao Cao

MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts (2022.emnlp-main)

Challenge:	Recent studies show that conversational dense retrieval is a promising technique for realizing conversational search, but its implementation is severely hindered by the lack of data.
Approach:	They propose a method that transforms easily-accessible web search sessions into conversational search sessions to alleviate the data scarcity problem.
Outcome:	The proposed method can transform easily-accessible web search sessions into conversational search sessions.

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Xiangyu Xi, Jianwei Lv, Shuaipeng Liu, Wei Ye, Fan Yang, Guanglu Wan

Reproducibility Issues for BERT-based Evaluation Metrics (2022.emnlp-main)

Challenge:	Recent efforts to classify unstructured texts into specific types have been limited in practical scenarios.
Approach:	They propose to use Chinese text conversations and phone conversations to expand event detection to the scenarios involving informal and heterogeneous texts.
Outcome:	The proposed dataset is based on user reviews, text conversations, and phone conversations in a leading e-commerce platform for food service.

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Yanran Chen, Jonas Belouadi, Steffen Eger

Improving Multi-task Stance Detection with Multi-task Interaction Network (2022.emnlp-main)

Challenge:	Reproducibility is of utmost concern in machine learning and natural language processing . lexical-overlap metrics are still the dominant metric in natural language generation .
Approach:	They ask whether results and claims from four recent BERT-based evaluation metrics can be reproduced.
Outcome:	The proposed metrics outperform the dominant metric, BLEU, and show that they can be reproduced.

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Heyan Chai, Siyu Tang, Jinhao Cui, Ye Ding, Binxing Fang, Qing Liao

Neural-based Mixture Probabilistic Query Embedding for Answering FOL queries on Knowledge Graphs (2022.emnlp-main)

Challenge:	Recent studies have proposed multi-task learning models that introduce sentiment information to boost stance detection but neglect to capture the fine-grained task-specific interaction between stance and sentiment tasks, thus degrading performance.
Approach:	They propose a novel multi-task interaction network (MTIN) that captures the word-level interaction between tasks, so as to obtain richer task representations.
Outcome:	The proposed approach outperforms state-of-the-art methods on two real-world datasets.

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Xiao Long, Liansheng Zhuang, Li Aodi, Shafei Wang, Houqiang Li

Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning (2022.emnlp-main)

Challenge:	Existing methods to embed entities and first-order logical queries in a vector space are often violated in real applications and limit their performance.
Approach:	They propose a Neural-based Mixture Probabilistic Query Embedding Model that embeds entities and first-order logical queries in a vector space.
Outcome:	The proposed model outperforms state-of-the-art methods on benchmark datasets.

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Yi Cheng, Wenge Liu, Wenjie Li, Jiashuo Wang, Ruihui Zhao, Bang Liu, Xiaodan Liang, Yefeng Zheng

Conformal Predictor for Improving Zero-Shot Text Classification Efficiency (2022.emnlp-main)

Challenge:	Existing research on building ES conversation systems only considered single-turn interactions with users, which is over-simplified and has limited support for multi-turn systems.
Approach:	They propose a multi-turn ES conversation system that uses lookahead heuristics to estimate future user feedback after using particular strategies.
Outcome:	The proposed system significantly outperforms baselines in both dialogue generation and strategy planning.

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Prafulla Kumar Choubey, Yu Bai, Chien-Sheng Wu, Wenhao Liu, Nazneen Rajani

Effective and Efficient Query-aware Snippet Extraction for Web Search (2022.emnlp-main)

Challenge:	Pre-trained language models (PLMs) have been shown effective for zero-shot (0shot) text classification.
Approach:	They propose to limit the number of likely labels using a fast base classifier-based conformal predictor calibrated on samples labeled by the 0shot model.
Outcome:	The proposed models reduce the average inference time for NLI- and NSP-based models by 25.6% and 22.2% without dropping performance below the predefined error rate of 1%.

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Jingwei Yi, Fangzhao Wu, Chuhan Wu, Xiaolong Huang, Binxing Jiao, Guangzhong Sun, Xing Xie

You Only Need One Model for Open-domain Question Answering (2022.emnlp-main)

Challenge:	Existing methods to extract webpage snippets ignore contextual information of webpages, which may be sub-optimal.
Approach:	They propose a query-aware webpage snippet extraction method called DeepQSE that captures contextual information of webpages.
Outcome:	The proposed method can significantly improve the performance of DeepQSE without affecting its performance.

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Haejun Lee, Akhil Kedia, Jongwon Lee, Ashwin Paranjape, Christopher Manning, Kyoung-Gu Woo

Generative Entity Typing with Curriculum Learning (2022.emnlp-main)

Challenge:	Recent approaches to Open-domain Question Answering use external knowledge bases, but have separate parameters and are weakly-coupled during training.
Approach:	They propose to use a single question answering model trained end-to-end to retrieve external knowledge and rerank passages with a separate reranked model.
Outcome:	The proposed model outperforms the previous state-of-the-art model by 1.0 and 0.7 exact match scores on the Natural Questions and TriviaQA open datasets.

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Siyu Yuan, Deqing Yang, Jiaqing Liang, Zhixu Li, Jinxi Liu, Jingyue Huang, Yanghua Xiao

SetGNER: General Named Entity Recognition as Entity Set Generation (2022.emnlp-main)

Challenge:	Entity typing fails to assign an entity to the types beyond the predefined type set.
Approach:	They propose a generative entity typing paradigm that assigns types to entities . traditional classification-based approaches fail to assign entities to the types beyond the predefined set . they employ curriculum learning to train the model on heterogeneous data .
Outcome:	The proposed model outperforms the state-of-the-art model on heterogeneous training data.

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Yuxin He, Buzhou Tang

Opinion Summarization by Weak-Supervision from Mix-structured Data (2022.emnlp-main)

Challenge:	Named entity recognition (NER) is a fundamental task in the field of information extraction and has played an important role in the development of natural language processing.
Approach:	They propose a method that treats each entity as a sequence and is capable of recognizing discontinuous mentions.
Outcome:	The proposed model outperforms state-of-the-art generative NER models on two discontinuous NER datasets, two nested NER and one flat NER.

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Yizhu Liu, Qi Jia, Kenny Zhu

Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model (2022.emnlp-main)

Challenge:	Existing methods for opinion summarization of multiple reviews lack reference summaries . OAs and ISs are often mismatched between review input and summary .
Approach:	They propose a method to generate mixed-structured synthetic training data for opinion summarization.
Outcome:	The proposed method outperforms existing methods on Yelp, Amazon and RottenTomatos datasets.

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Mingqi Li, Fei Ding, Dan Zhang, Long Cheng, Hongxin Hu, Feng Luo

Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval (2022.emnlp-main)

Challenge:	Existing methods for improving multilingual models did not focus on learning the semantic structure of representation.
Approach:	They propose a method to improve multilingual language models by aligning parallel sentences . they propose token-, word-, sentence- and structure-level alignment objectives .
Outcome:	The proposed method outperforms baseline models on XNLI, PAWS-X, and XQuAD . it obtains comparable performance on low-resource languages, the authors show .

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Houxing Ren, Linjun Shou, Ning Wu, Ming Gong, Daxin Jiang

R2F: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference (2022.emnlp-main)

Challenge:	Existing methods to distill knowledge from cross-encoder re-ranker to dual-encoding retriever are lacking in the cross-lingual setting.
Approach:	They propose to use a query generator as the teacher in the cross-lingual setting to distill knowledge to a dual-encoder retrieval model.
Outcome:	The proposed method outperforms state-of-the-art methods on two benchmark datasets.

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Hao Wang, Yixin Cao, Yangguang Li, Zhen Huang, Kun Wang, Jing Shao

Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Processing (2022.emnlp-main)

Challenge:	Document-level natural language inference (DOCNLI) is a new task in natural language processing.
Approach:	They propose a document-level natural language inference framework that fuses sentence-level tasks into a set of sentence-based tasks.
Outcome:	The proposed framework improves interpretability and performance with evidence.

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Abbas Ghaddar, Yimeng Wu, Sunyam Bagga, Ahmad Rashid, Khalil Bibi, Mehdi Rezagholizadeh, Chao Xing, Yasheng Wang, Xinyu Duan, Zhefeng Wang, Baoxing Huai, Xin Jiang, Qun Liu, Phillippe Langlais

KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering (2022.emnlp-main)

Challenge:	Existing pre-trained language models are not well-explored and are not reproducible in the literature.
Approach:	They propose to improve existing Arabic language pre-trained language models using a more methodical approach.
Outcome:	The proposed models outperform existing models on ALUE, a leaderboard-powered benchmark for Arabic NLU and NLG tasks.

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Jianing Wang, Chengyu Wang, Minghui Qiu, Qiuhui Shi, Hongbin Wang, Jun Huang, Ming Gao

Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding (2022.emnlp-main)

Challenge:	Extractive Question Answering (EQA) is one of the most essential tasks in Machine Reading Comprehension (MRC).
Approach:	They propose a framework that transforms extractive question answering into a non-autoregressive Masked Language Modeling (MLM) generation problem.
Outcome:	The proposed framework outperforms state-of-the-art approaches in few-shot learning scenarios by a large margin.

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Jianing Wang, Wenkang Huang, Minghui Qiu, Qiuhui Shi, Hongbin Wang, Xiang Li, Ming Gao

On the Evaluation Metrics for Paraphrase Generation (2022.emnlp-main)

Challenge:	Existing knowledge-enhanced pre-trained language models (PLMs) introduce redundant factual knowledge from knowledge bases and require complex modules.
Approach:	They propose a knowledge prompting-based PLM framework that incorporates factual knowledge into PLMs.
Outcome:	The proposed framework can be flexibly combined with existing mainstream PLMs.

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Lingfeng Shen, Lemao Liu, Haiyun Jiang, Shuming Shi

Curriculum Learning Meets Weakly Supervised Multimodal Correlation Learning (2022.emnlp-main)

Challenge:	Existing evaluation metrics for paraphrase generation are not designed for the task, but adopted from other evaluation tasks.
Approach:	They propose a new evaluation metric for paraphrase generation that uses reference-based and reference-free metrics.
Outcome:	The proposed evaluation metric outperforms existing metrics and is more reliable than reference-based metrics.

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Sijie Mai, Ya Sun, Haifeng Hu

Rethinking Positional Encoding in Tree Transformer for Code Representation (2022.emnlp-main)

Challenge:	Existing studies have used the correlation information stored in samples for self-supervised learning, but they feed the training pairs in a random order without consideration of difficulty.
Approach:	They propose to inject curriculum learning into weakly supervised multimodal correlation learning by scoring and feeding pairs according to difficulty.
Outcome:	The proposed model achieves state-of-the-art on multimodal sentiment analysis without human annotation.

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Han Peng, Ge Li, Yunfei Zhao, Zhi Jin

RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL (2022.emnlp-main)

Challenge:	Recent works have proposed novel tree Transformers to capture the syntactic structure in source code.
Approach:	They propose a novel tree Transformer encoding node positions based on a description method for tree structures to incorporate inductive bias into Transformer.
Outcome:	The proposed model outperforms baselines on code summarization and completion tasks across two languages, and it is able to perform better on both local and global paradigms.

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Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, Zhouhan Lin

Programming Languages in NLP

Challenge:	Experimental results show RASAT can leverage a variety of relational structures while inheriting the pretrained parameters from the T5 model.
Approach:	They propose a Transformer seq2seq architecture augmented with relation-aware self-attention that leverages relational structures while inheriting pretrained parameters from the T5 model.
Outcome:	The proposed model can leverage relational structures while inheriting pretrained parameters from the T5 model effectively.

COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction (2022.emnlp-main)

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Zepeng Zhai, Hao Chen, Fangxiang Feng, Ruifan Li, Xiaojie Wang

CEM: Machine-Human Chatting Handoff via Causal-Enhance Module (2022.emnlp-main)

Challenge:	Aspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from sentences, but when faced with multiple aspect terms, the MRC-based methods could fail due to the interference from other aspect terms.
Approach:	They propose a COntext-Masked MRC framework for Aspect Sentiment Triplet Extraction (ASTE) which aims to extract sentiment triplets from sentences .
Outcome:	The proposed framework outperforms state-of-the-art methods on benchmark datasets and shows that it can extract sentiment triplets from multiple aspect terms.

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Shanshan Zhong, Jinghui Qin, Zhongzhan Huang, Daifeng Li

Nearest Neighbor Zero-Shot Inference (2022.emnlp-main)

Challenge:	Existing methods to predict chatbot failure ignore causal variables, resulting in cost increasement and prediction bias.
Approach:	They propose a machine-human chatting handoff module that predicts chatbot failure . they use user state and labor cost to correct the prediction bias .
Outcome:	The proposed method improves the performance of existing methods without any elaborate model crafting.

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Weijia Shi, Julian Michael, Suchin Gururangan, Luke Zettlemoyer

Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems (2022.emnlp-main)

Challenge:	Using non-parametric memory for retrieval-augmented language models yields significant performance boosts over strong zeroshot baselines.
Approach:	They propose a retrieval-augmented language model with fuzzy verbalizers that expands the verbalizes that define different end-task class labels.
Outcome:	The proposed model outperforms non-retrieval-augmented language models on perplexity-based evaluations but gains transfer marginally . the main challenge is to achieve coverage of the verbalizer tokens that define the different end-task class labels.

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David Gros, Yu Li, Zhou Yu

A Joint Learning Framework for Restaurant Survival Prediction and Explanation (2022.emnlp-main)

Challenge:	Dialog systems often output human-like responses, but some are impossible for a machine to say.
Approach:	They collect ratings on the feasibility of 900 two-turn dialogs from 9 data sources . they build classifiers and explore how modeling configuration might affect output permissibly .
Outcome:	The proposed model can be used to train human-like dialogs, but it is not anthropomorphic.

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Xin Li, Xiaojie Zhang, Peng JiaHao, Rui Mao, Mingyang Zhou, Xing Xie, Hao Liao

Making Pretrained Language Models Good Long-tailed Learners (2022.emnlp-main)

Challenge:	Recent advances in deep learning have various models that research reviews and interactions for different kinds of tasks, such as predicting restaurant survival.
Approach:	They propose a joint learning framework for explainable restaurant survival prediction based on multi-modal data of user-restaurant interactions and users’ textual reviews.
Outcome:	The proposed framework improves on two datasets showing that it can model restaurant interactions and users’ textual reviews.

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Chen Zhang, Lei Ren, Jingang Wang, Wei Wu, Dawei Song

UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression (2022.emnlp-main)

Challenge:	Prompt-tuning has shown appealing performance in few-shot classification . however, it is less promising in long-tailed classification due to long tail .
Approach:	They propose to use prompt-tuning to make pretrained language models at least good long-tailed learners by bridging the gap between prompt- and commonly used finetun.
Outcome:	The proposed method makes pretrained language models at least good long-tailed learners, bridging the gap between prompt-tuning and finetunation.

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Jiaqi Chen, Tong Li, Jinghui Qin, Pan Lu, Liang Lin, Chongyu Chen, Xiaodan Liang

Face-Sensitive Image-to-Emotional-Text Cross-modal Translation for Multimodal Aspect-based Sentiment Analysis (2022.emnlp-main)

Challenge:	Existing work on geometry problem solving treats calculation and proving as two specific tasks hindering a deep model to unify reasoning ability on multiple math tasks.
Approach:	They propose a large-scale Unified Geometry problem benchmark to unify geometry on multiple math tasks.
Outcome:	The proposed framework outperforms the existing model with 5.6% and 3.2% accuracies on calculation and proving problems.

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Hao Yang, Yanyan Zhao, Bing Qin

FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluation (2022.emnlp-main)

Challenge:	Existing models focus on utilizing semantic information in the image but ignore using visual emotional cues.
Approach:	They propose a face-sensitive image-to-emotional-text translation method that captures visual emotional cues through facial expressions and selectively matches and fuses with the textual content.
Outcome:	The proposed method achieves state-of-the-art results on the Twitter-2015 and Twitter-2017 datasets.

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Chen Zhang, Luis Fernando D’Haro, Qiquan Zhang, Thomas Friedrichs, Haizhou Li

Sentence Representation Learning with Generative Objective rather than Contrastive Objective (2022.emnlp-main)

Challenge:	Recent model-based reference-free metrics for open-domain dialogue evaluation lack correlations with human judgment and poor interpretability.
Approach:	They propose a multi-dimensional dialogue-level metric with three sub-metrics targeting a specific dimension.
Outcome:	The proposed metric outperforms existing models and sub-metrics in three high-quality dialogue evaluation benchmarks.

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Bohong Wu, Hai Zhao

RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning (2022.emnlp-main)

Challenge:	Existing sentences-level training objectives focus on acquiring sentence-level representations, but they lack effective self-supervised objectives.
Approach:	They propose a generative self-supervised learning objective based on phrase reconstruction to improve sentence representation.
Outcome:	Empirical results show that the proposed objective outperforms current methods on STS benchmarks and retrieval and reranking tasks.

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Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric Xing, Zhiting Hu

DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation (2022.emnlp-main)

Challenge:	Existing methods for finding the optimal prompt for a task are difficult to optimize.
Approach:	They propose an efficient discrete prompt optimization approach with reinforcement learning that generates the optimal discrete stimulus after training with reward.
Outcome:	The proposed approach is based on a parameter-efficient policy network that generates the optimal discrete prompt after training with reward.

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Hanqing Zhang, Dawei Song

CPL: Counterfactual Prompt Learning for Vision and Language Models (2022.emnlp-main)

Challenge:	Existing prompt tuning approaches for attribute-controllable text generation are difficult to implement due to the lack of interpretability of deep neural networks.
Approach:	They propose a new approach that incorporates attribute knowledge of discriminator to optimize prompt tuning by steering a frozen CLM to produce attribute-specific texts.
Outcome:	The proposed approach can achieve state-of-the-art control performance while maintaining high-quality text generation.

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Xuehai He, Diji Yang, Weixi Feng, Tsu-Jui Fu, Arjun Akula, Varun Jampani, Pradyumna Narayana, Sugato Basu, William Yang Wang, Xin Wang

Red Teaming Language Models with Language Models (2022.emnlp-main)

Challenge:	Existing prompt tuning methods tend to learn spurious or entangled representations, leading to poor generalization to unseen concepts.
Approach:	They propose a prompt tuning technique that tunes the learnable prompt for pre-trained vision and language models.
Outcome:	The proposed method improves few-shot performance on vision and language tasks over existing prompt tuning methods.

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Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, Geoffrey Irving

CapOnImage: Context-driven Dense-Captioning on Image (2022.emnlp-main)

Challenge:	Prior work has found that language models (LMs) can harm users in hard-to-predict ways, and human annotation is expensive, limiting the number and diversity of test cases.
Approach:	They propose to generate test inputs using an LM itself, and use a classifier to detect harmful behavior on test input.
Outcome:	The proposed approach detects tens of thousands of offensive responses in a 280B parameter LM chatbot.

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Yiqi Gao, Xinglin Hou, Yuanmeng Zhang, Tiezheng Ge, Yuning Jiang, Peng Wang

SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition (2022.emnlp-main)

Challenge:	Existing image captioning systems generate narrative captions for images, which are spatially detached from the image in presentation.
Approach:	They propose a task called captioning on image which generatesense captions at different locations of the image based on contextual information.
Outcome:	The proposed model achieves the best results in both captioning accuracy and diversity aspects.

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Jianing Wang, Chengyu Wang, Chuanqi Tan, Minghui Qiu, Songfang Huang, Jun Huang, Ming Gao

Discovering Differences in the Representation of People using Contextualized Semantic Axes (2022.emnlp-main)

Challenge:	Existing methods for few-shot Named Entity Recognition ignore entity boundaries and are time-consuming . a seminal span-based prototypical network solves the problem using two stages: span extraction and mention classification.
Approach:	They propose a seminal span-based prototypical network that tackles few-shot NER . they transform sequential tags into a global boundary matrix and use prototypical learning .
Outcome:	The proposed model outperforms strong baselines over multiple benchmarks.

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Li Lucy, Divya Tadimeti, David Bamman

Generating Literal and Implied Subquestions to Fact-check Complex Claims (2022.emnlp-main)

Challenge:	Past work has compared embeddings against “semantic axes” that represent two opposing concepts.
Approach:	They extend this paradigm to BERT embeddings and construct contextualized axes that mitigate pitfall where antonyms have neighboring representations.
Outcome:	The proposed axes can characterize differences among instances of the same word type on two people-centric datasets.

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Jifan Chen, Aniruddh Sriram, Eunsol Choi, Greg Durrett

Machine Translation Robustness to Natural Asemantic Variation (2022.emnlp-main)

Challenge:	Existing fact-checking systems are not reliable because it is unclear which parts of a claim are true and which are not.
Approach:	They propose to decompose a political claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim.
Outcome:	The proposed models can decompose a complex claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim.

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Jacob Bremerman, Xiang Ren, Jonathan May

Natural Language to Code Translation with Execution (2022.emnlp-main)

Challenge:	Existing machine translation models struggle with noisy data and tail-end words and phrases.
Approach:	They introduce and formalize a class of noise and variation that preserves meaning in the target language.
Outcome:	The proposed model can perform better on natural asemantic variation (NAV) the proposed model is robust to a variety of perturbations, but not all of them are achieved with organic variations.

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Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, Sida I. Wang

Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processes (2022.emnlp-main)

Challenge:	Generative code models do not explicitly incorporate program semantics during training, but they are able to generate correct solutions for many problems.
Approach:	They introduce execution result-based minimum Bayes risk decoding for program selection . they select output programs from a generated candidate set by marginalizing over implementations that share the same semantics .
Outcome:	The proposed model outperforms all other methods on natural language-to-code translation.

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Oren Sultan, Dafna Shahaf

Language Contamination Helps Explains the Cross-lingual Capabilities of English Pretrained Models (2022.emnlp-main)

Challenge:	Analogy-making gives rise to reasoning, abstraction, flexible categorization and counterfactual inference – abilities that current AI systems lack.
Approach:	They propose an interpretable, scalable algorithm that extracts analogies from a pair of natural language procedural texts and finds a mapping between the different domains based on relational similarity.
Outcome:	The proposed algorithm can extract analogies from a large dataset and achieve 79% precision.

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Terra Blevins, Luke Zettlemoyer

Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models (2022.emnlp-main)

Challenge:	a large number of pretraining corpora are not publicly available, and it is unclear how much foreign language data exists in monolingual models.
Approach:	They propose to use English pretraining corpora to analyze their language composition . they find that even when less than 1% of data is not English, it facilitates cross-lingual transfer .
Outcome:	The proposed model is not truly monolingual when pretrained at scale, the authors show . they show that even when less than 1% of data is not English, it facilitates cross-lingual transfer .

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Terra Blevins, Hila Gonen, Luke Zettlemoyer

Neural Machine Translation with Contrastive Translation Memories (2022.emnlp-main)

Challenge:	Existing studies on multilingual models have focused on their cross-lingual transfer behavior . a recent study examined multilingual model learning from the multilingual pretraining signal .
Approach:	They analyze checkpoints during multilingual pretraining to identify when models acquire in-language and cross-lingual abilities.
Outcome:	The proposed model achieves high in-language performance early on, with lower-level linguistic skills acquired before more complex ones.

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Xin Cheng, Shen Gao, Lemao Liu, Dongyan Zhao, Rui Yan

Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition (2022.emnlp-main)

Challenge:	Experimental results show that retrieval-augmented NMT model obtains substantial improvements over strong baselines in the benchmark dataset.
Approach:	They propose a retrieval-augmented NMT model that is holistically similar to the source sentence while individually contrastive to each other.
Outcome:	The proposed model improves on baselines in the translation task.

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Junhao Zheng, Zhanxian Liang, Haibin Chen, Qianli Ma

Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing (2022.emnlp-main)

Challenge:	Existing methods for Named Entity Recognition (NER) are not able to learn Other-Class in the same way as new entity types.
Approach:	They propose a unified causal framework to retrieve causality from new entity types and Other-Class.
Outcome:	The proposed method outperforms the state-of-the-art method on three benchmark datasets.

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Zhenwen Li, Jiaqi Guo, Qian Liu, Jian-Guang Lou, Tao Xie

That’s the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data (2022.emnlp-main)

Challenge:	Existing studies show that the design of Meaning Representation (MR) greatly influences the final model performance of a neural semantic parser.
Approach:	They propose a data-aware metric called ISS to measure the final performance of MRs.
Outcome:	The proposed metric denoting incremental structural stability (ISS) of MRs can be used as an indicator for MR design to avoid the costly training-testing process.

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Jered McInerney, Geoffrey Young, Jan-Willem van de Meent, Byron Wallace

Unsupervised Tokenization Learning (2022.emnlp-main)

Challenge:	Recent multimodal models induce soft local alignments between image regions and sentences.
Approach:	They compare alignments from a state-of-the-art multimodal model for EHR with human annotations that link image regions to sentences.
Outcome:	The proposed models induce soft local alignments between image regions and sentences . the text has an often weak or unintuitive influence on attention, the authors found .

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Anton Kolonin, Vignav Ramesh

A Template-based Method for Constrained Neural Machine Translation (2022.emnlp-main)

Challenge:	Unsupervised language learning has attracted great attention in recent years . Glushchenko et al. (2015) suggested using "deep patterns" with hierarchical "symbolic" grammatical pattern structures learned from texts as a way to model grammars and domain ontologies for natural languages.
Approach:	They propose to use a "transition freedom" metric to measure unsupervised tokenization . they find that different languages require different offshoots of that metric for tokenization.
Outcome:	The proposed method provides better tokenization quality than or comparable to lexicon-based ones, depending on the language.

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Shuo Wang, Peng Li, Zhixing Tan, Zhaopeng Tu, Maosong Sun, Yang Liu

PATS: Sensitivity-aware Noisy Learning for Pretrained Language Models (2022.emnlp-main)

Challenge:	Existing methods to solve this problem can not satisfy the following three desiderata: (1) high translation quality, (2) high match accuracy, and (3) low latency.
Approach:	They propose a template-based method that can provide high translation quality and match accuracy and a low latency inference.
Outcome:	The proposed method outperforms baselines in lexically and structurally constrained translation tasks and can be used in a variety of applications.

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Yupeng Zhang, Hongzhi Zhang, Sirui Wang, Wei Wu, Zhoujun Li

Towards Reinterpreting Neural Topic Models via Composite Activations (2022.emnlp-main)

Challenge:	A wide range of NLP tasks benefit from fine-tuning of pretrained language models (PLMs), however, a number of redundant parameters which contribute less to the downstream task are observed in a directly fine- tuned model.
Approach:	They propose a noisy training mechanism which considers each parameter’s importance in the downstream task to help fine-tune pretrained language models.
Outcome:	The proposed method can be used to fine-tune pretrained language models on a wide range of tasks and consistently achieve higher performance.

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Jia Peng Lim, Hady Lauw

Few-shot Query-Focused Summarization with Prefix-Merging (2022.emnlp-main)

Challenge:	Most Neural Topic Models (NTMs) use a variational auto-encoder framework producing K topics limited to the size of the encoder’s output.
Approach:	They propose a model-free two-stage process to reinterpret NTM and derive further insights on the state of the trained model.
Outcome:	The proposed model-free process decouples the strict interpretation of topics from the original NTM and evaluates them on a large external corpus.

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Ruifeng Yuan, Zili Wang, Ziqiang Cao, Wenjie Li

Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment (2022.emnlp-main)

Challenge:	Query-focused summarization has been considered as an important extension for text summarizing . lack of large-scale datasets hinders its development .
Approach:	They propose to integrate text summarization and question answering into a prefix-based pretraining strategy for few-shot learning in query-focused summarizing.
Outcome:	The proposed prefix-based pretraining outperforms fine-tuning on query-focused summarization.

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Siyu Lai, Zhen Yang, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation (2022.emnlp-main)

Challenge:	Existing word alignment models capture few interactions between input sentence pairs, which severely degrades the word alignment quality.
Approach:	They propose to model deep interactions between input and target sentences using a two-stage training framework to train the model.
Outcome:	The proposed model achieves the state-of-the-art (SOTA) performance on four out of five language pairs.

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Tianxiang Sun, Junliang He, Xipeng Qiu, Xuanjing Huang

HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (2022.emnlp-main)

Challenge:	Currently, pre-trained language model (PLM) based metrics are widely adopted in text generation tasks.
Approach:	They propose to use PLMs to encode stereotypical societal biases in PLM-based metrics . they show that popular metrics exhibit higher social bias than traditional metrics based on 6 attributes .
Outcome:	The proposed method shows that PLM-based metrics exhibit higher social bias than traditional metrics on 6 attributes.

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Zihan Wang, Peiyi Wang, Tianyu Liu, Binghuai Lin, Yunbo Cao, Zhifang Sui, Houfeng Wang

Not to Overfit or Underfit the Source Domains? An Empirical Study of Domain Generalization in Question Answering (2022.emnlp-main)

Challenge:	Hierarchical text classification (HTC) is a multi-label classification problem with a complex label hierarchy.
Approach:	They propose a Hierarchy-aware Prompt Tuning method to handle HTC from a multi-label perspective using a dynamic virtual template and label words that take the form of soft prompts to fuse the label hierarchy knowledge.
Outcome:	The proposed method achieves state-of-the-art performance on 3 popular HTC datasets and is adept at handling imbalance and low resource situations.

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Md Arafat Sultan, Avi Sil, Radu Florian

Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs (2022.emnlp-main)

Challenge:	Existing approaches to limit overfitting of training domains are rooted in this problem . domain generalization (DG) seeks to train models on a small number of source domains .
Approach:	They propose to use knowledge distillation to train models on a small number of source domains to maximize their zero-shot out-of-domain utility.
Outcome:	The proposed model learns its source domains better and has better out-of-domain generalization . the proposed model outperforms existing approaches that aim to limit overfitting .

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Maarten Sap, Ronan Le Bras, Daniel Fried, Yejin Choi

Improving Passage Retrieval with Zero-Shot Question Generation (2022.emnlp-main)

Challenge:	We show that one of today’s largest language models lacks this kind of social intelligence out-of-the-box, using two tasks: SocialIQa and ToMi.
Approach:	They propose to use social intelligence and Theory of Mind to examine whether modern large-scale language models lack this kind of social intelligence out-of-the-box.
Outcome:	The proposed model lacks social intelligence out-of-the-box, and has well-below human accuracies on SocialIQa and ToMi, respectively.

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Devendra Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, Luke Zettlemoyer

Summarizing Community-based Question-Answer Pairs (2022.emnlp-main)

Challenge:	Existing re-ranking methods for open-domain question answering are not domain- or task-specific.
Approach:	They propose a simple and effective re-ranking method for improving passage retrieval in open-domain question answering.
Outcome:	The proposed method outperforms strong supervised models on open-domain questions and triviaQA datasets on top-1000 passages.

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Ting-Yao Hsu, Yoshi Suhara, Xiaolan Wang

Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models (2022.emnlp-main)

Challenge:	Community-based question answering (CQA) has become an essential component of online services.
Approach:	They propose a novel task to summarize CQA pairs into a concise summary . they use a benchmark dataset and a sentence-type transfer and deduplication removal approach .
Outcome:	The proposed task aims to create a concise summary from CQA pairs . the proposed method is stronger than existing methods and is publicly available .

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Joe Stacey, Pasquale Minervini, Haim Dubossarsky, Marek Rei

How to disagree well: Investigating the dispute tactics used on Wikipedia (2022.emnlp-main)

Challenge:	Current models learn from annotation artefacts and dataset biases, but it is unclear to what extent they are learning the task of NLI.
Approach:	They propose a logical reasoning framework that allows models to learn from annotation artefacts and dataset biases.
Outcome:	The proposed model outperforms humans on in-distribution test sets without using span labels . the model is more robust in a reduced data setting, and out-of-disturbance performance is improved .

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Christine De Kock, Andreas Vlachos

Challenge:	Disagreements are often studied from the perspective of toxicity or analysing argument structure.
Approach:	They propose a dispute tactics framework which unifies both perspectives . they annotate 213 disagreements from Wikipedia Talk pages .
Outcome:	The proposed framework can be used to predict disagreements with a transformer-based model.

Chapter Ordering in Novels (2022.emnlp-main)

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Allen Kim, Steve Skiena

Open-ended Knowledge Tracing for Computer Science Education (2022.emnlp-main)

Challenge:	a major challenge in research on long-form narrative texts is the cost of annotation . authors propose a new task that reconstructs the original order of chapters in novels without the need for human annotation.
Approach:	They propose a task that reconstructs the original order of chapters in novels given a random permutation of the text.
Outcome:	The proposed task yields a Spearman correlation of 0.59 on the novel and challenging task, substantially above baseline.

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Naiming Liu, Zichao Wang, Richard Baraniuk, Andrew Lan

Logical Neural Networks for Knowledge Base Completion with Embeddings & Rules (2022.emnlp-main)

Challenge:	Knowledge tracing (KT) is a method used to estimate student mastery of concepts/skills/knowledge components from their responses to questions and to predict future performance.
Approach:	They propose a student knowledge-guided code generation approach that combines program synthesis methods with student knowledge tracing methods to solve the OKT problem.
Outcome:	The proposed method is based on a student knowledge-guided code generation approach and validates on coding questions.

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Prithviraj Sen, Breno William Carvalho, Ibrahim Abdelaziz, Pavan Kapanipathi, Salim Roukos, Alexander Gray

MedCLIP: Contrastive Learning from Unpaired Medical Images and Text (2022.emnlp-main)

Challenge:	Knowledge base completion (KBC) is a human-interpretable dialect . rule-based KBC has a high quality but low accuracy .
Approach:	They propose to use logical neural networks to learn both kinds of rules in a common framework using gradient-based optimization.
Outcome:	The proposed method improves by 10% relative to SotA rule-based methods and by combining it with knowledge graph embeddings it achieves an additional 7.5% relative improvement.

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Zifeng Wang, Zhenbang Wu, Dinesh Agarwal, Jimeng Sun

GA-SAM: Gradient-Strength based Adaptive Sharpness-Aware Minimization for Improved Generalization (2022.emnlp-main)

Challenge:	Existing vision-text contrastive learning methods encounter many false negatives, i.e., images and reports from separate patients probably carry the same semantics but are wrongly treated as negatives.
Approach:	They propose to decouple medical image-text contrastive learning and replace it with semantic matching loss based on medical knowledge to eliminate false negatives in contrastive training.
Outcome:	The proposed framework outperforms state-of-the-art methods on zero-shot prediction, supervised classification, and image-text retrieval with only 20K pre-training data.

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Zhiyuan Zhang, Ruixuan Luo, Qi Su, Xu Sun

Sparse Teachers Can Be Dense with Knowledge (2022.emnlp-main)

Challenge:	Recent studies show flat minima tend to imply better generalization abilities . however, it has some difficulty implying SAM to some natural language tasks .
Approach:	They propose a flatness-aware minimization algorithm that can be applied to natural language tasks . they propose to use parameter corruptions to explain why flat minima generalize better .
Outcome:	The proposed algorithm can generalize better for flat minima that are robust against corruptions or perturbations.

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Yi Yang, Chen Zhang, Dawei Song

BBTv2: Towards a Gradient-Free Future with Large Language Models (2022.emnlp-main)

Challenge:	Existing methods for transferring knowledge from a teacher of large scale to a student of smaller scale are limiting in overall knowledgeableness.
Approach:	They propose a sparse teacher trick to remove over-parameterized teachers that produce student-unfriendly knowledge and thus limit overall knowledgeableness.
Outcome:	The proposed trick removes the parameters that result in student-unfriendliness and leads to compelling performance in comparison with baselines.

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Tianxiang Sun, Zhengfu He, Hong Qian, Yunhua Zhou, Xuanjing Huang, Xipeng Qiu

Passage-Mask: A Learnable Regularization Strategy for Retriever-Reader Models (2022.emnlp-main)

Challenge:	Recent work on parameter-efficient tuning (PET) only tunes a small portion of parameters while keeping most of the parameters of the LLM unchanged.
Approach:	They propose an improved version of Black-Box Tuning to tune PTMs through gradient descent . they prepend continuous prompts to every layer of the PTM and propose a divide-and-conquer gradient-free algorithm to optimize the prompts alternately.
Outcome:	The proposed method achieves comparable performance to full model tuning and state-of-the-art parameter-efficient methods under few-shot settings while maintaining much fewer tunable parameters.

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Shujian Zhang, Chengyue Gong, Xingchao Liu

Mixed-effects transformers for hierarchical adaptation (2022.emnlp-main)

Challenge:	Xu et al., 2021) find that retrieval-reader models overfit top-rank passages and fail to reason over entire retrieval passages.
Approach:	They propose a passage mask mechanism which desensitizes the impact from top-rank retrieval passages and prevents the model from overfitting.
Outcome:	Experiments on open question answering, dialogue conversation, and fact verification show that the proposed model outperforms baselines.

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Julia White, Noah Goodman, Robert Hawkins

On Measuring the Intrinsic Few-Shot Hardness of Datasets (2022.emnlp-main)

Challenge:	Language differs dramatically from context to context, but prompting can be ineffective when contexts are sparse, out-of-sample, or extra-textual.
Approach:	They propose a mixed-effects transformer approach for learning hierarchically-structured prefixes to account for structured variation in language use.
Outcome:	The proposed approach can be extended to transformer-based architectures while generalizing well to unseen contexts.

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Xinran Zhao, Shikhar Murty, Christopher Manning

Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling (2022.emnlp-main)

Challenge:	Recent work has shown that few-shot learning is successful for pre-trained models, but there is no concrete understanding of when and why it is successful.
Approach:	They propose a simple metric that estimates few-shot hardness for a given dataset . they propose metric which exploits feature-space invariances between training and test samples .
Outcome:	The proposed metric better accounts for few-shot hardness compared to existing notions and is 8-100x faster to compute.

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Bowen Xing, Ivor Tsang

An Empirical Study on Finding Spans (2022.emnlp-main)

Challenge:	Recent joint multiple intent detection and slot filling models ignore the dependencies among labels and label embeddings.
Approach:	They propose to construct a Heterogeneous Label Graph (HLG) containing two kinds of topologies and a novel model termed ReLa-Net which captures beneficial correlations among the labels from HLG.
Outcome:	The proposed model outperforms the previous model by over 20% on MixATIS dataset.

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Weiwei Gu, Boyuan Zheng, Yunmo Chen, Tongfei Chen, Benjamin Van Durme

Challenge:	Various information extraction tasks require a span finding component, which either directly yields the output or serves as an essential component of downstream linking.
Approach:	They propose methods for span finding, the selection of consecutive tokens in text for some downstream tasks.
Outcome:	The proposed methods perform better on masked language models and pre-trained encoders than on encoder-decoder models.

MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding (2022.emnlp-main)

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Zilong Wang, Jiuxiang Gu, Chris Tensmeyer, Nikolaos Barmpalios, Ani Nenkova, Tong Sun, Jingbo Shang, Vlad Morariu

Understanding Jargon: Combining Extraction and Generation for Definition Modeling (2022.emnlp-main)

Challenge:	Existing methods learn features from word-level or region-level but fail to consider both simultaneously.
Approach:	They propose a multi-modal multi-granular pre-training framework that encodes page-level, region-level and word-level information at the same time.
Outcome:	The proposed model learns features from word-level and region-level but fails to consider both simultaneously.

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Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-mei Hwu

ProsocialDialog: A Prosocial Backbone for Conversational Agents (2022.emnlp-main)

Challenge:	Existing methods for generating definitions of words/phrases perform poorly on jargon.
Approach:	They propose to combine extraction and generation for jargon definition modeling by extracting definitional information from the Web and incorporating extracted definitional data.
Outcome:	The proposed method outperforms state-of-the-art models significantly on jargon definitions.

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Hyunwoo Kim, Youngjae Yu, Liwei Jiang, Ximing Lu, Daniel Khashabi, Gunhee Kim, Yejin Choi, Maarten Sap

Exploiting Global and Local Hierarchies for Hierarchical Text Classification (2022.emnlp-main)

Challenge:	Existing dialogue systems fail to respond properly to potentially unsafe user utterances . existing systems either ignore or passively agree with unsafe content .
Approach:	They introduce a dataset to teach conversational agents to respond to problematic content following social norms.
Outcome:	The proposed dataset shows that ProsocialDialog generates more socially acceptable dialogues than existing models.

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Ting Jiang, Deqing Wang, Leilei Sun, Zhongzhi Chen, Fuzhen Zhuang, Qinghong Yang

Semantic-aware Contrastive Learning for More Accurate Semantic Parsing (2022.emnlp-main)

Challenge:	Existing methods encode label hierarchy in a global view, which makes them hard to exploit hierarchical information.
Approach:	They propose to leverage label hierarchy in multi-label text classification by encoding label hierarchy as a static hierarchical structure containing all labels.
Outcome:	The proposed method achieves significant improvement on three benchmark datasets compared with the state-of-the-art method HGCLR.

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Shan Wu, Chunlei Xin, Bo Chen, Xianpei Han, Le Sun

Scientific Paper Extractive Summarization Enhanced by Citation Graphs (2022.emnlp-main)

Challenge:	Existing studies on semantic parsing use Maximum Likelihood Estimation (MLE) to train discriminative semantic parses.
Approach:	They propose a semantic-aware contrastive learning algorithm which can learn to distinguish fine-grained meaning representations and take the overall sequence-level semantic into consideration.
Outcome:	The proposed algorithm improves on two standard datasets and gets state-of-the-art performance over existing methods.

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Xiuying Chen, Mingzhe Li, Shen Gao, Rui Yan, Xin Gao, Xiangliang Zhang

Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios (2022.emnlp-main)

Challenge:	citation graphs can be used to extract scientific papers under different conditions.
Approach:	They propose a multi-granularity unsupervised summarization model that fine tunes a pre-trained encoder model on the citation graph by link prediction tasks.
Outcome:	The proposed model outperforms baseline models on a public benchmark dataset.

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Ngoc Dang Nguyen, Lan Du, Wray Buntine, Changyou Chen, Richard Beare

Syntactic Multi-view Learning for Open Information Extraction (2022.emnlp-main)

Challenge:	Named Entity Recognition (NER) tasks require a large amount of training data and domains are often scarcely labeled.
Approach:	They propose a hardness-guided domain adaptation framework for bioNER tasks that leverages domain hardness information to improve the adaptability of the learnt model in low-resource scenarios.
Outcome:	The proposed model outperforms the state-of-the-art MetaNER model on biomedical datasets.

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Kuicai Dong, Aixin Sun, Jung-Jae Kim, Xiaoli Li

TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection (2022.emnlp-main)

Challenge:	Open Information Extraction (OpenIE) aims to generate structured tuples from unstructured open-domain text.
Approach:	They propose to model constituency and dependency trees into word-level graphs and combine them with sentential semantic representations to extract relational tuples.
Outcome:	The proposed model integrates constituency and dependency trees into word-level graphs and enables neural OpenIE to learn from syntactic structures.

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Chaoya Jiang, Haiyang Xu, Chenliang Li, Ming Yan, Wei Ye, Shikun Zhang, Bin Bi, Songfang Huang

CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation (2022.emnlp-main)

Challenge:	Existing vision-and-language pre-training models suffer from long visual sequences . experimental results show that TRIPS gains a speedup of 40% over previous similar VLP models .
Approach:	They propose an efficient vision-and-language pre-training model with text-relevant image patch selection, TRIPS, which reduces the visual sequence progressively with a text-guided patch-selection layer in the visual backbone for efficient training and inference.
Outcome:	The proposed model can speed up training and inference by 40% over previous models.

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Yinpei Dai, Wanwei He, Bowen Li, Yuchuan Wu, Zheng Cao, Zhongqi An, Jian Sun, Yongbin Li

Kernel-Whitening: Overcome Dataset Bias with Isotropic Sentence Embedding (2022.emnlp-main)

Challenge:	a new benchmark for goal-oriented dialog evaluation is needed to address the problem of knowledge sources, noisy user expressions, and the shortage of annotated data.
Approach:	They propose a Chinese benchmark for goal-oriented dialog evaluation that uses dialog sessions and 574,949 dialog turns to bridge the gap between academic benchmarks and spoken dialog scenarios.
Outcome:	The proposed benchmark contains 96,763 dialog sessions and 574,949 dialog turns totally.

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SongYang Gao, Shihan Dou, Qi Zhang, Xuanjing Huang

A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling (2022.emnlp-main)

Challenge:	Existing approaches to reduce dataset bias rely on spurious correlations and obstruct valid feature information while mitigating bias.
Approach:	They propose a representation normalization method which disentangles correlations between features of encoded sentences and a kernel approximation method which provides isotropic data distribution.
Outcome:	The proposed method eliminates the bias problem by providing isotropic data distribution while maintaining in-distribution accuracy.

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Ye Wang, Xinxin Liu, Wenxin Hu, Tao Zhang

Automatic Generation of Socratic Subquestions for Teaching Math Word Problems (2022.emnlp-main)

Challenge:	Existing document-level relation extraction methods focus on fully supervised scenarios but in real-world, incomplete labeling is a common problem because the number of entity pairs grows quadratically with the number.
Approach:	They propose a positive-unlabeled learning framework for document-level relation extraction (RE) that uses shift and squared ranking loss positive- unlabeles (SSR-PU) learning to solve incomplete labeling problem.
Outcome:	The proposed framework outperforms state-of-the-art methods under fully supervised and extremely unlabeled conditions and achieves 14 F1 points over the baseline with incomplete labeling.

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Kumar Shridhar, Jakub Macina, Mennatallah El-Assady, Tanmay Sinha, Manu Kapur, Mrinmaya Sachan

Mixture of Attention Heads: Selecting Attention Heads Per Token (2022.emnlp-main)

Challenge:	We hypothesize that questioning can enhance human performance and assist solvers .
Approach:	They propose to use large language models to generate sequential questions for math word problem-solving . they propose to apply these models to a variety of math word problems .
Outcome:	The proposed model improves the performance of a math word problem solver by generating more questions than other models.

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Xiaofeng Zhang, Yikang Shen, Zeyu Huang, Jie Zhou, Wenge Rong, Zhang Xiong

The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models (2022.emnlp-main)

Challenge:	Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing.
Approach:	They propose a new architecture that combines multi-head attention with the MoE mechanism and a sparsely gated architecture that allows for faster computations.
Outcome:	The proposed architecture can scale up the number of attention heads and the number parameters while preserving computational efficiency.

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Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh

Information-Theoretic Text Hallucination Reduction for Video-grounded Dialogue (2022.emnlp-main)

Challenge:	Pre-trained Transformer models provide robust language representations which can be specialized on various tasks.
Approach:	They propose an efficient pruning method based on approximate second-order information that allows pruning weight blocks to be used for pruning.
Outcome:	The proposed method is the first to be applied at the BERT scale and significantly pushes the boundaries of the current sparse models with respect to all metrics: model size, inference speed and task accuracy.

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Sunjae Yoon, Eunseop Yoon, Hee Suk Yoon, Junyeong Kim, Chang Yoo

DSM: Question Generation over Knowledge Base via Modeling Diverse Subgraphs with Meta-learner (2022.emnlp-main)

Challenge:	Existing video-grounded dialogue systems suffer from text hallucination problem due to learning spurious correlations from the fact that answer sentences in the dataset usually include the words of input texts.
Approach:	They propose to decode an answer sentence to a question using video and dialogue contexts.
Outcome:	The proposed framework shows that it generates adequate conversational responses to the queries of humans while following up on video and dialogue context.

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Shasha Guo, Jing Zhang, Yanling Wang, Qianyi Zhang, Cuiping Li, Hong Chen

RelU-Net: Syntax-aware Graph U-Net for Relational Triple Extraction (2022.emnlp-main)

Challenge:	Existing methods on knowledge base question generation learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraph.
Approach:	They propose a graph contrastive learning-based retriever to model diverse subgraphs with meta-learner to learn semantics-specific and semantics agnostic knowledge on and across these tasks.
Outcome:	The proposed approach reduces learning difficulty and improves performance on two widely-adopted benchmarks on KBQG.

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Yunqi Zhang, Yubo Chen, Yongfeng Huang

Evidence > Intuition: Transferability Estimation for Encoder Selection (2022.emnlp-main)

Challenge:	Existing methods focused on capturing semantic information but failed to incorporate syntactic structures of the sentence, which is proved to contain rich relational information.
Approach:	They propose a framework to capture syntactic information for relational triple extraction by contracting dependency tree into a core relational topology and eliminating redundant information with graph pooling operations.
Outcome:	The proposed framework incorporates syntactic information for relational triple extraction.

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Elisa Bassignana, Max Müller-Eberstein, Mike Zhang, Barbara Plank

Chunk-based Nearest Neighbor Machine Translation (2022.emnlp-main)

Challenge:	Existing studies on LM transferability have focused on a priori tuning of encoders . prior work has examined the different yet related tasks of performance prediction .
Approach:	They propose to generate quantitative evidence to predict which LM will perform best on a target task without fine-tuning all candidates.
Outcome:	The proposed model outperforms the standard of human practitioner ranking in 94% of the setups.

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Pedro Henrique Martins, Zita Marinho, André F. T. Martins

FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering (2022.emnlp-main)

Challenge:	Semi-parametric models augment generation with retrieval, but require expensive retrieval operation for every generated token.
Approach:	They propose a semi-parametric model which augments generation with retrieval by retrieving tokens from a datastore.
Outcome:	The proposed model can retrieve chunks of tokens from the datastore, instead of a single token, with a low decoding speed.

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Akhil Kedia, Mohd Abbas Zaidi, Haejun Lee

Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations (2022.emnlp-main)

Challenge:	generative models tend to be larger than extractive models due to the need for a decoder, run slower during inference due to auto-regressive decoded beam search, and their generated output suffers from hallucinations.
Approach:	They propose to extend transformer encoders with the ability to fuse information from multiple passages to provide cross-sample attention over all tokens across samples.
Outcome:	The proposed method outperforms the current state-of-the-art method by 2.5 Exact Match score on the Natural Question dataset while using only 25% of parameters and 35% of the latency during inference.

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Yudai Pan, Jun Liu, Lingling Zhang, Tianzhe Zhao, Qika Lin, Xin Hu, Qianying Wang

Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity (2022.emnlp-main)

Challenge:	Existing methods for relation prediction in knowledge graphs (KGs) are limited by the inductive setting because entities in training process are finite.
Approach:	They propose a graph convolutional network-based model LogCo with logical reasoning by contrastive representations that extracts subgraphs and relational paths between two entities to supply the entity-independence.
Outcome:	The proposed model outperforms existing methods on twelve inductive datasets.

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Jiahao Li, Quan Wang, Zhendong Mao, Junbo Guo, Yanyan Yang, Yongdong Zhang

MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation (2022.emnlp-main)

Challenge:	Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts.
Approach:	They propose an auxiliary task of Chinese pronunciation prediction to improve CSC . they propose adaptive weighting schemes and a delicate correction strategy .
Outcome:	The proposed auxiliary task improves Chinese pronunciation prediction on three benchmarks.

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Anna Currey, Maria Nadejde, Raghavendra Reddy Pappagari, Mia Mayer, Stanislas Lauly, Xing Niu, Benjamin Hsu, Georgiana Dinu

A Span-level Bidirectional Network for Aspect Sentiment Triplet Extraction (2022.emnlp-main)

Challenge:	Existing benchmarks have limited diversity in terms of gender phenomena, sentence structure, or language coverage.
Approach:	They propose a benchmark to evaluate gender accuracy in translation from English into eight widely-spoken languages.
Outcome:	The proposed benchmark provides realistic, gender-balanced, counterfactual data in eight language pairs where the gender of individuals is unambiguous in the input segment.

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Yuqi Chen, Chen Keming, Xian Sun, Zequn Zhang

On the Calibration of Massively Multilingual Language Models (2022.emnlp-main)

Challenge:	Aspect Sentiment Triplet Extraction (ASTE) is a new fine-grained sentiment analysis task . recent studies have focused on solving aspects term extraction, opinion term extraction and aspect-level sentiment classification tasks individually or in combination of two subtasks.
Approach:	They propose a span-level bidirectional network which utilizes all possible spans as input and extracts triplets from spans bidirectionally.
Outcome:	The proposed framework outperforms state-of-the-art methods and improves performance . it can extract triplets of aspect terms, sentiments, and opinion terms from review sentences .

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Kabir Ahuja, Sunayana Sitaram, Sandipan Dandapat, Monojit Choudhury

Momentum Contrastive Pre-training for Question Answering (2022.emnlp-main)

Challenge:	Massively Multilingual Language Models (MMLMs) have gained popularity due to their effectiveness in cross-lingual transfer.
Approach:	They investigate how well calibrated MMLMs are with respect to confidence . they find that calibration methods like temperature scaling and label smoothing improve calibration .
Outcome:	The proposed models are able to generalize in languages unseen during fine-tuning, but they are not reliable across languages.

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Minda Hu, Muzhi Li, Yasheng Wang, Irwin King

A Second Wave of UD Hebrew Treebanking and Cross-Domain Parsing (2022.emnlp-main)

Challenge:	Existing methods for extractive Question Answering generate cloze-like queries different from natural questions in syntax structure, which could overfit pre-trained models to simple keyword matching.
Approach:	They propose a method to align the answer probability between cloze-like and natural query-passage sample pairs.
Outcome:	The proposed method improves on three benchmarking QA datasets on supervised and zero-shot scenarios.

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Amir Zeldes, Nick Howell, Noam Ordan, Yifat Ben Moshe

Finding Dataset Shortcuts with Grammar Induction (2022.emnlp-main)

Challenge:	Foundational Hebrew NLP tasks have relied on various versions of the Hebrew Treebank . however, the data in the HTB is now over 30 years old and does not cover many aspects of contemporary Hebrew on the web.
Approach:	They propose to use Hebrew Wikipedia to stratify the text from a UD treebank.
Outcome:	The proposed treebank is based on a single-source newswire corpus selected from Hebrew Wikipedia.

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Dan Friedman, Alexander Wettig, Danqi Chen

Retrieval Augmentation for Commonsense Reasoning: A Unified Approach (2022.emnlp-main)

Challenge:	Prior work on shortcut detection focused on enumerating features like unigrams or bigrams . prior work relied on post-hoc models that reveal qualitative patterns without a clear statistical interpretation .
Approach:	They propose to use probabilistic grammars to characterize and discover shortcuts in NLP datasets using context-free grammars and synchronous context- free grammars.
Outcome:	The proposed grammars reveal interesting shortcut features in a number of datasets, including simple and high-level features, and automatically identify groups of test examples on which conventional classifiers fail.

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Wenhao Yu, Chenguang Zhu, Zhihan Zhang, Shuohang Wang, Zhuosheng Zhang, Yuwei Fang, Meng Jiang

Open World Classification with Adaptive Negative Samples (2022.emnlp-main)

Challenge:	Existing methods for retrieving encyclopedic knowledge lack a large corpus and effective commonsense retriever.
Approach:	They propose a framework for retrieval-augmented commonsense reasoning with a large commonsensense corpus and a commonseense retriever.
Outcome:	The proposed framework outperforms existing methods on commonsense reasoning tasks.

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Ke Bai, Guoyin Wang, Jiwei Li, Sunghyun Park, Sungjin Lee, Puyang Xu, Ricardo Henao, Lawrence Carin

Re3: Generating Longer Stories With Recursive Reprompting and Revision (2022.emnlp-main)

Challenge:	Existing models with no effective open category data during training are limited by the lack of effective open categories data during the training stage.
Approach:	They propose an approach to generate effective open category samples in the training stage and without requiring prior knowledge or external datasets.
Outcome:	The proposed approach generates effective synthetic open category samples in the training stage and without requiring any prior knowledge or external datasets.

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Kevin Yang, Yuandong Tian, Nanyun Peng, Dan Klein

Does Joint Training Really Help Cascaded Speech Translation? (2022.emnlp-main)

Challenge:	Recent work has generated short stories of several pages in length, but they are much shorter than typical short stories meant for human consumption.
Approach:	They propose a framework to generate long-range plot coherence and relevance by prompting a general-purpose language model and a language model.
Outcome:	The proposed framework generates stories of 2000-2500 words, compared to similar-length stories generated directly from the same model.

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Viet Anh Khoa Tran, David Thulke, Yingbo Gao, Christian Herold, Hermann Ney

MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition (2022.emnlp-main)

Challenge:	Currently, in speech translation, the straightforward approach delivers state-of-the-art results, but fundamental challenges such as error propagation remain.
Approach:	They propose to combine a cascaded recognition system with a machine translation system to improve cascade speech translation.
Outcome:	The proposed methods can improve cascaded speech translation and suggest alternative training methods.

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David Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel, Constantine Lignos, Jesujoba Alabi, Shamsuddeen Muhammad, Peter Nabende, Cheikh M. Bamba Dione, Andiswa Bukula, Rooweither Mabuya, Bonaventure F. P. Dossou, Blessing Sibanda, Happy Buzaaba, Jonathan Mukiibi, Godson Kalipe, Derguene Mbaye, Amelia Taylor, Fatoumata Kabore, Chris Chinenye Emezue, Anuoluwapo Aremu, Perez Ogayo, Catherine Gitau, Edwin Munkoh-Buabeng, Victoire Memdjokam Koagne, Allahsera Auguste Tapo, Tebogo Macucwa, Vukosi Marivate, Mboning Tchiaze Elvis, Tajuddeen Gwadabe, Tosin Adewumi, Orevaoghene Ahia, Joyce Nakatumba-Nabende, Neo Lerato Mokono, Ignatius Ezeani, Chiamaka Chukwuneke, Mofetoluwa Oluwaseun Adeyemi, Gilles Quentin Hacheme, Idris Abdulmumin, Odunayo Ogundepo, Oreen Yousuf, Tatiana Moteu, Dietrich Klakow

Ethics consideration sections in natural language processing papers (2022.emnlp-main)

Challenge:	Existing studies on named entity recognition methods for African languages focus on English as the source language, but there is evidence that it is not the best for low-resource languages.
Approach:	They propose to use human-annotated datasets to analyze named entity recognition tasks in 20 African languages to test whether they are effective.
Outcome:	The proposed method improves zero-shot F1 scores by 14% over 20 languages compared to using English .

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Luciana Benotti, Patrick Blackburn

Continued Pretraining for Better Zero- and Few-Shot Promptability (2022.emnlp-main)

Challenge:	a manual classification of ethics sections for ACL 2021 is presented . authors will be allowed extra space after the 8th page for a broader impact statement .
Approach:	They propose a manual classification of all ethical consideration sections for ACL 2021 . they also compare how many papers had an ethics consideration section per track .
Outcome:	The paper compares the number of papers with an ethics consideration section in ACL 2021 . it also examines obstacles to the discussion of ethical consideration sections .

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Zhaofeng Wu, Robert L Logan IV, Pete Walsh, Akshita Bhagia, Dirk Groeneveld, Sameer Singh, Iz Beltagy

Less is More: Summary of Long Instructions is Better for Program Synthesis (2022.emnlp-main)

Challenge:	Recent language model prompting methods can achieve high accuracy in zero- and few-shot settings while requiring few to no learned task-specific parameters.
Approach:	They propose to use a dedicated pretraining stage to improve promptability in zero-shot settings and few-shot tuning.
Outcome:	The proposed method improves promptability in zero- and few-shot settings, while the existing method yields subpar performance.

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Kirby Kuznia, Swaroop Mishra, Mihir Parmar, Chitta Baral

Is a Question Decomposition Unit All We Need? (2022.emnlp-main)

Challenge:	Despite the success of large pre-trained language models, they show below-par performance on the larger and more complicated programming related questions.
Approach:	They propose to use a human-generated summary of programming questions to improve LMs' performance.
Outcome:	The proposed approach outperforms baseline by 8.13% on the APPS dataset and 11.88% on the CodeContests dataset on an average in terms of strict accuracy.

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Pruthvi Patel, Swaroop Mishra, Mihir Parmar, Chitta Baral

Discourse-Aware Soft Prompting for Text Generation (2022.emnlp-main)

Challenge:	Large Language Models (LMs) have achieved state-of-the-art performance on many NLP benchmarks.
Approach:	They propose to decompose a hard question into simpler questions that are easier for models to answer.
Outcome:	The proposed approach significantly improves model performance (24% for GPT3 and 29% for RoBERTa-SQuAD along with a symbolic calculator) by decomposing a hard question into simpler questions that are easier for models to answer.

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Marjan Ghazvininejad, Vladimir Karpukhin, Vera Gor, Asli Celikyilmaz

ExPUNations: Augmenting Puns with Keywords and Explanations (2022.emnlp-main)

Challenge:	Recent advances in pre-trained langauge models (PLMs) have made great impact on text generation research.
Approach:	They propose to use hierarchical blocking to simulate a higher-level discourse structure of human written text and attention sparsity to learn sparse transformations on the softmax-function.
Outcome:	The proposed methods perform better on some generation tasks but don't generalize across all generation tasks.

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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

SLING: Sino Linguistic Evaluation of Large Language Models (2022.emnlp-main)

Challenge:	Puns add the challenge of fusing commonsense and world knowledge with the ability to interpret lexical-semantic ambiguity.
Approach:	They propose to augment existing datasets with detailed crowdsourced annotations of puns, keywords and fine-grained funniness ratings to challenge current models' ability to understand and generate humor.
Outcome:	The proposed tasks include explanation generation to aid with pun classification and keyword-conditioned pun generation to challenge state-of-the-art models' ability to understand and generate humor.

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Yixiao Song, Kalpesh Krishna, Rajesh Bhatt, Mohit Iyyer

Context-Situated Pun Generation (2022.emnlp-main)

Challenge:	Using pre-trained language models, we find that the accuracy of LMs is far below human performance.
Approach:	They propose a benchmark of Sino LINGuistics which consists of 38K sentence pairs in Mandarin Chinese grouped into 9 high-level linguistic phenomena.
Outcome:	The proposed model performs better on local phenomena than hierarchical models and has a strong gender and number bias.

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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Shuyang Gao, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

Retrieval-Augmented Generative Question Answering for Event Argument Extraction (2022.emnlp-main)

Challenge:	a new task for context-situated pun generation uses a given context to generate puns . human evaluation shows that 69% of top retrieved pun words can be used to generate context-based puns.
Approach:	They propose a task where puns are generated based on contextual keywords and pun words.
Outcome:	The proposed system generates successful puns 31% of the time given a plausible tuple of context words and pun pairs.

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Xinya Du, Heng Ji

Concadia: Towards Image-Based Text Generation with a Purpose (2022.emnlp-main)

Challenge:	Existing methods to extract arguments from documents are based on generating and post-processing a complex target sequence (template).
Approach:	They propose a retrieval-augmented generative QA model that retrieves the most similar QA pair and augments it as prompt to the current example's context, then decodes the arguments as answers.
Outcome:	The proposed model outperforms prior methods across fully supervised, domain transfer, and fewshot learning settings and compares with clustering-based sampling strategies.

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Elisa Kreiss, Fei Fang, Noah Goodman, Christopher Potts

Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics (2022.emnlp-main)

Challenge:	Existing models fail to generate fluent, truthful text, despite excellent results on benchmark datasets . current systems fail to produce texts that are useful in practice, authors argue .
Approach:	They propose to distinguish descriptions from captions based on their communicative roles . descriptions focus on visual features and are meant to replace an image . authors characterize commonalities and differences between descriptions and captions in a Wikipedia corpus .
Outcome:	The proposed model can generate fluent, truthful texts in a wide range of scenarios . it can also generate captions that are used to make an image accessible to users who can't see them .

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Elisa Kreiss, Cynthia Bennett, Shayan Hooshmand, Eric Zelikman, Meredith Ringel Morris, Christopher Potts

MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure (2022.emnlp-main)

Challenge:	Existing referenceless metrics do not take context into account, whereas contextual information is highly valued by BLV users.
Approach:	They propose a contextual version of the referenceless metric CLIPScore which addresses the disconnect to the BLV data.
Outcome:	The proposed evaluation metrics are based on a proof-of-concept with blind and low vision (BLV) participants.

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Yinya Huang, Hongming Zhang, Ruixin Hong, Xiaodan Liang, Changshui Zhang, Dong Yu

Explicit Query Rewriting for Conversational Dense Retrieval (2022.emnlp-main)

Challenge:	Current explanation datasets often employ synthetic data with simple reasoning structures.
Approach:	They propose a comprehensive logical reasoning explanation form that incorporates three main components to better fit the human cognitive process.
Outcome:	The proposed model performs better than existing models on real-life scenarios, but is more challenging for the current models.

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Hongjin Qian, Zhicheng Dou

Efficient Nearest Neighbor Emotion Classification with BERT-whitening (2022.emnlp-main)

Challenge:	In a conversational search scenario, a query might be context-dependent because some words are referred to previous expressions or omitted.
Approach:	They propose a model that performs query rewriting and context modelling in a unified framework by highlighting relevant terms in the query context.
Outcome:	The proposed model outperforms baseline models in terms of quality of query rewriting and quality of contextualized query embedding.

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Wenbiao Yin, Lin Shang

FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification (2022.emnlp-main)

Challenge:	Existing methods for retrieval-based methods use pre-trained models for similarity search, but they perform poorly in retrieving semantically similar sentences.
Approach:	They propose a non-parametric emotion classification method using nearest neighbor retrieval that can be added to any pre-trained model without further training.
Outcome:	The proposed method improves the pre-trained model by 1.17 F1-macro on two emotion classification datasets.

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Tingyu Xia, Yue Wang, Yuan Tian, Yi Chang

Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification (2022.emnlp-main)

Challenge:	Recent research shows keyword-driven methods can achieve state-of-the-art performance on various tasks.
Approach:	They propose an efficient weakly-supervised text classification approach using unlabeled data . they use dense text representation to retrieve class-relevant documents from unlabed corpus .
Outcome:	The proposed weakly-supervised classification method outperforms keyword-driven models on a wide range of classification tasks.

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Zi Lin, Jeremiah Liu, Jingbo Shang

A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (2022.emnlp-main)

Challenge:	Pre-trained models excel at graph semantic parsing with rich annotated data, but generalize poorly to out-of-distribution and long-tail examples.
Approach:	They propose a compositionality-aware approach to neural-symbolic inference informed by model confidence to capture different aspects of the graph prediction.
Outcome:	The proposed method outperforms state-of-the-art models on an English resource grammar parsing problem on standard in-domain and seven OOD corpora.

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Yuan Xia, Zhenhui Shi, Jingbo Zhou, Jiayu Xu, Chao Lu, Yehui Yang, Lei Wang, Haifeng Huang, Xia Zhang, Junwei Liu

Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework (2022.emnlp-main)

Challenge:	With the development of medical digitization, the extraction and structuring of electronic medical records (EMRs) have become challenging but fundamental tasks.
Approach:	They propose a speaker-aware dialogue encoder with multi-task learning which takes the speaker's identity into account and a co-attention fusion network to aggregate the utterance information.
Outcome:	The proposed framework outperforms the state-of-the-art methods on the public medical dialogue extraction datasets to demonstrate its superiority.

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Yiquan Wu, Yifei Liu, Weiming Lu, Yating Zhang, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang

RelCLIP: Adapting Language-Image Pretraining for Visual Relationship Detection via Relational Contrastive Learning (2022.emnlp-main)

Challenge:	Existing models lack interpretability due to the neglect of rationale in the prediction process.
Approach:	They propose a rationale-based legal judgment prediction framework that follows the judge's real trial logic and provides good interactivity and interpretability.
Outcome:	The proposed framework provides good interactivity and interpretability which enables practical use.

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Yi Zhu, Zhaoqing Zhu, Bingqian Lin, Xiaodan Liang, Feng Zhao, Jianzhuang Liu

Candidate Soups: Fusing Candidate Results Improves Translation Quality for Non-Autoregressive Translation (2022.emnlp-main)

Challenge:	Existing visual relationship detection models only use numeric ids of relation labels for training, but ignore semantic correlation between labels.
Approach:	They propose a visual Relationship prediction framework that transfers natural language knowledge from Contrastive Language-Image Pre-training models to enhance the relationship prediction.
Outcome:	The proposed framework improves visual relationship prediction by matching semantic correlations with relation triplets.

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Huanran Zheng, Wei Zhu, Pengfei Wang, Xiaoling Wang

Evaluating Parameter Efficient Learning for Generation (2022.emnlp-main)

Challenge:	Existing methods to improve NAT model's performance but do not fully utilize it.
Approach:	They propose a non-autoregressive translation method which can obtain high-quality translations while maintaining the inference speed of NAT models.
Outcome:	The proposed method outperforms the autoregressive translation model on three translation tasks with 7.6 speedup.

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Peng Xu, Mostofa Patwary, Shrimai Prabhumoye, Virginia Adams, Ryan Prenger, Wei Ping, Nayeon Lee, Mohammad Shoeybi, Bryan Catanzaro

McQueen: a Benchmark for Multimodal Conversational Query Rewrite (2022.emnlp-main)

Challenge:	Parameter efficient learning methods (PERMs) are gaining attention for their ability to adapt to a downstream task.
Approach:	They propose to use parameter efficient learning methods to improve model adaptation . they compare in-domain evaluations and generalizations to unseen domains and new datasets .
Outcome:	The proposed method outperforms finetuning and PERMs in in-domain evaluations.

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Yifei Yuan, Chen Shi, Runze Wang, Liyi Chen, Feijun Jiang, Yuan You, Wai Lam

Self-supervised Graph Masking Pre-training for Graph-to-Text Generation (2022.emnlp-main)

Challenge:	Recent studies have focused on conversational-related tasks that involve drawing information from more than one modality.
Approach:	They propose a task of multimodal conversational query rewrite which performs query . they collect a large-scale visual conversation dataset and benchmark it against other tasks .
Outcome:	The proposed task performs on a large-scale visual conversation dataset . it eliminates coreference and ellipsis in the original query without changing its semantic information.

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Jiuzhou Han, Ehsan Shareghi

Improving Stability of Fine-Tuning Pretrained Language Models via Component-Wise Gradient Norm Clipping (2022.emnlp-main)

Challenge:	Large-scale pre-trained language models (PLMs) have advanced Graph-to-Text generation by processing the linearised version of a graph.
Approach:	They propose to mask pre-training tasks that neither require supervision signals nor adjust the architecture of the underlying pre-trained encoder-decoder model.
Outcome:	The proposed method achieves state-of-the-art results on WebNLG+2020 and EventNarrative datasets and is very efficient in the low-resource setting.

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Chenghao Yang, Xuezhe Ma

Differentially Private Language Models for Secure Data Sharing (2022.emnlp-main)

Challenge:	Recent studies have attributed such instability to the catastrophic forgetting problem in the top layers of PLMs.
Approach:	They propose a component-wise gradient norm clipping method to adjust convergence speed for different components to improve generalization performance, convergence speed, and training stability.
Outcome:	The proposed method achieves consistent improvements in terms of generalization performance, convergence speed, and training stability.

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Justus Mattern, Zhijing Jin, Benjamin Weggenmann, Bernhard Schoelkopf, Mrinmaya Sachan

Conditional set generation using Seq2seq models (2022.emnlp-main)

Challenge:	a variety of deanonymization attacks allow the re-identification of individuals from tabular data.
Approach:	They propose to train a language model in a differentially private manner and sample data from it . they find that the model generates fluent textual datasets with privacy guarantees .
Outcome:	The proposed methods outperform direct classifiers with DP-SGD in the real-world.

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Aman Madaan, Dheeraj Rajagopal, Niket Tandon, Yiming Yang, Antoine Bosselut

Analyzing and Evaluating Faithfulness in Dialogue Summarization (2022.emnlp-main)

Challenge:	Several NLP tasks are instances of set generation.
Approach:	They propose a model-independent data augmentation approach that enlarges the model with the signals of order-invariance and cardinality.
Outcome:	The proposed method improves performance on four benchmark datasets with no additional annotations.

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Bin Wang, Chen Zhang, Yan Zhang, Yiming Chen, Haizhou Li

Twist Decoding: Diverse Generators Guide Each Other (2022.emnlp-main)

Challenge:	Existing studies on faithfulness of text summarization have not been conducted on abstractive summarizing.
Approach:	They propose a method to evaluate faithfulness of dialogue summarization models by multi-choice questions.
Outcome:	The proposed method can facilitate the development of dialogue summarization systems.

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Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng, Ximing Lu, Dragomir Radev, Yejin Choi, Noah A. Smith

Exploring Representation-level Augmentation for Code Search (2022.emnlp-main)

Challenge:	Using a variety of language generation models, ensembling models is challenging during inference.
Approach:	They propose a method that decodes text models that do not assume a shared vocabulary, tokenization or generation order.
Outcome:	The proposed method outperforms models decoded in isolation over various scenarios.

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Haochen Li, Chunyan Miao, Cyril Leung, Yanxian Huang, Yuan Huang, Hongyu Zhang, Yanlin Wang

Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables (2022.emnlp-main)

Challenge:	Recent data augmentations for code search are at the raw-data level, which requires additional code analysis and training cost.
Approach:	They propose a general format of representation-level augmentation that unifies existing methods.
Outcome:	The proposed methods can boost the performance of code search models on a large-scale dataset.

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Erxin Yu, Lan Du, Yuan Jin, Zhepei Wei, Yi Chang

STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension (2022.emnlp-main)

Challenge:	Recent discrete latent variable models have received a surge of interest in both NLP and CV . they are comparable to the continuous counterparts in representation learning, but are more interpretable in their predictions.
Approach:	They develop a topic-informed discrete latent variable model for semantic textual similarity . they inject the quantized representation into a transformer-based language model .
Outcome:	The proposed model outperforms strong baselines in semantic textual similarity tasks.

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Borui Wang, Chengcheng Feng, Arjun Nair, Madelyn Mao, Jai Desai, Asli Celikyilmaz, Haoran Li, Yashar Mehdad, Dragomir Radev

Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality? (2022.emnlp-main)

Challenge:	Abstractive dialogue summarization is an important standalone task in natural language processing, but no previous work has explored whether it can be used to boost an NLP system's performance on other important dialogue comprehension tasks.
Approach:	They propose a novel type of dialogue summarization task that decomposes and imitates the hierarchical, systematic and structured mental process that human beings usually go through when understanding and analyzing dialogues.
Outcome:	The proposed model improves the performance of transformer encoder language models on two important dialogue comprehension tasks.

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Pei Zhang, Baosong Yang, Hao-Ran Wei, Dayiheng Liu, Kai Fan, Luo Si, Jun Xie

PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training (2022.emnlp-main)

Challenge:	Neural machine translation models are often criticized for failures that happen without competency awareness.
Approach:	They propose a method that extends conventional NMT with a self-estimator to translate a source sentence and estimate its competency.
Outcome:	The proposed method performs on translation tasks intact and on quality estimation tasks better than existing methods.

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Zihui Gu, Ju Fan, Nan Tang, Preslav Nakov, Xiaoman Zhao, Xiaoyong Du

Ethical NLP

Challenge:	Table-based fact verification has attracted a lot of attention recently due to the lack of datasets that can be used to pre-train language models to be aware of common table operations.
Approach:	They propose a table-based fact verification tool that pre-trains language models to be aware of common table operations such as aggregating a column or comparing tuples.
Outcome:	The proposed method outperforms previous methods on two table-based fact verification datasets TabFact and SEM-TAB- FACTS.

Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis (2022.emnlp-main)

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Shuai Fan, Chen Lin, Haonan Li, Zhenghao Lin, Jinsong Su, Hang Zhang, Yeyun Gong, JIan Guo, Nan Duan

Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement (2022.emnlp-main)

Challenge:	Existing pre-trained language representation models (PLMs) capture sentiment information from word-level while under-considering sentence-level information.
Approach:	They propose a Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks that enhance the PLM’s knowledge about sentiment words.
Outcome:	The proposed model achieves state-of-the-art on various sentence-level and aspect-level sentiment classification benchmarks.

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Hui Liu, Wenya Wang, Haoliang Li

Efficiently Tuned Parameters Are Task Embeddings (2022.emnlp-main)

Challenge:	Sarcasm is a linguistic phenomenon indicating a discrepancy between literal meanings and implied intentions.
Approach:	They propose a hierarchical framework for sarcasm detection by exploring atomic-level congruity and composition-level convergence.
Outcome:	The proposed model outperforms existing methods on a public sarcasm detection dataset based on Twitter .

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Wangchunshu Zhou, Canwen Xu, Julian McAuley

COPEN: Probing Conceptual Knowledge in Pre-trained Language Models (2022.emnlp-main)

Challenge:	Existing methods for intermediate-task transfer are computationally infeasible to experiment with all intermediate combinations.
Approach:	They propose to use task-specific parameters updated in parameter-efficient tuning methods to predict inter-task transferability.
Outcome:	The proposed approach outperforms existing methods while being conceptually simple and computationally efficient.

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Hao Peng, Xiaozhi Wang, Shengding Hu, Hailong Jin, Lei Hou, Juanzi Li, Zhiyuan Liu, Qun Liu

Capturing Global Structural Information in Long Document Question Answering with Compressive Graph Selector Network (2022.emnlp-main)

Challenge:	Existing knowledge probing studies focus on evaluating factual knowledge of pre-trained language models (PLMs) but ignore conceptual knowledge.
Approach:	They evaluate conceptual knowledge of pre-trained language models by annotating 24k data instances covering 393 concepts.
Outcome:	The proposed tasks evaluate pre-trained language models' conceptual knowledge of entities, learn conceptual properties, and conceptualize entities in contexts.

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Yuxiang Nie, Heyan Huang, Wei Wei, Xian-Ling Mao

Structural generalization is hard for sequence-to-sequence models (2022.emnlp-main)

Challenge:	Existing methods to answer long document questions ignore the global structure of the long document, which is essential for long-range understanding.
Approach:	They propose a Compressive Graph Selector Network to capture the global structure of the long document in a compressive and iterative manner.
Outcome:	The proposed model outperforms existing methods on two datasets.

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Yuekun Yao, Alexander Koller

Contrastive Learning enhanced Author-Style Headline Generation (2022.emnlp-main)

Challenge:	Sequence-to-sequence models have been successful across many NLP tasks, but they have low generalization accuracy .
Approach:	They propose to use linguistic knowledge to overcome generalization limitations of seq2seq models . they show that human beings are able to understand and produce linguistic structures they have never observed before .
Outcome:	The proposed models can overcome this limitation by having linguistic knowledge built in.

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Hui Liu, Weidong Guo, Yige Chen, Xiangyang Li

Multi-Granularity Optimization for Non-Autoregressive Translation (2022.emnlp-main)

Challenge:	Current work only uses the article itself in the headline generation, but have not taken the writing style of headlines into account.
Approach:	They propose a model which takes historical headlines into account to integrate the stylistic features of the author into the model and integrate them into the decoder.
Outcome:	The proposed model can integrate the stylistic features of the author into the model and generate a headline that is appropriate for the article and consistent with the author’s style.

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Yafu Li, Leyang Cui, Yongjing Yin, Yue Zhang

Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks (2022.emnlp-main)

Challenge:	Non-autoregressive machine translation suffers severe performance deterioration due to the naive independence assumption.
Approach:	They propose a method which collects model behaviours on translation segments of various granularities and integrates feedback for backpropagation to reduce latency.
Outcome:	Experiments on four benchmark datasets show that the proposed method outperforms baseline models trained with cross-entropy loss and achieves the best performance on WMT’16 EnRo and highly competitive results on WTM’14 EnDe.

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Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Kuntal Kumar Pal, Maitreya Patel, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Savan Doshi, Shailaja Keyur Sampat, Siddhartha Mishra, Sujan Reddy A, Sumanta Patro, Tanay Dixit, Xudong Shen

Challenge:	a benchmark of 1,616 diverse NLP tasks and their expert-written instructions is used to test generalization of models to unseen tasks . a recent study shows that instruction-following models outperform instruction-based models by over 9% .
Approach:	They build a benchmark of 1,616 diverse NLP tasks and their expert-written instructions.
Outcome:	The proposed model outperforms existing instruction-following models by over 9% on the benchmark despite being smaller.

MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks (2022.emnlp-main)

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Zequn Liu, Kefei Duan, Junwei Yang, Hanwen Xu, Ming Zhang, Sheng Wang

DRLK: Dynamic Hierarchical Reasoning with Language Model and Knowledge Graph for Question Answering (2022.emnlp-main)

Challenge:	Existing meta-path generation methods cannot fully exploit rich textual information in HINs.
Approach:	They propose a text-infilling-based approach to generate meta-paths from textual information in HINs.
Outcome:	The proposed approach can classify edges in the zero-shot setting, where existing methods cannot generate meta-paths.

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Miao Zhang, Rufeng Dai, Ming Dong, Tingting He

AEG: Argumentative Essay Generation via A Dual-Decoder Model with Content Planning (2022.emnlp-main)

Challenge:	Existing work only uses the same QA context representation to interact with multiple layers of KG, which results in a restricted interaction.
Approach:	They propose a model that utilizes dynamic hierarchical interactions between QA context and KG for reasoning.
Outcome:	The proposed model performs state-of-the-art on two benchmark datasets and competitively on the others.

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Jianzhu Bao, Yasheng Wang, Yitong Li, Fei Mi, Ruifeng Xu

BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets (2022.emnlp-main)

Challenge:	Existing studies on argument generation focus on generating individual short arguments, while research on generating long and coherent argumentative essays is under-explored.
Approach:	They propose a task to automatically generate argumentative essays using a writing prompt.
Outcome:	The proposed model generates persuasive essays with higher diversity and less repetition compared to baselines.

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Minju Kim, Chaehyeong Kim, Yong Ho Song, Seung-won Hwang, Jinyoung Yeo

Wider & Closer: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition (2022.emnlp-main)

Challenge:	a number of largescale datasets targeting a specific conversational skill have recently become available.
Approach:	They propose a framework where multiple agents grounded to specific skills participate in a conversation to automatically annotate multi-skill dialogues.
Outcome:	The proposed framework can be used to build open-domain chatbots with diverse communicative skills.

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Jun-Yu Ma, Beiduo Chen, Jia-Chen Gu, Zhenhua Ling, Wu Guo, Quan Liu, Zhigang Chen, Cong Liu

An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks (2022.emnlp-main)

Challenge:	Existing mainstream methods for zero-shot cross-lingual named entity recognition ignore the rich and complementary information lying in the intermediate layers of pre-trained language models and domain-invariant information is easily lost during transfer.
Approach:	They propose a mixture of short-channel distillers to fully interact the rich hierarchical information in the teacher model and to transfer knowledge to the student model sufficiently and efficiently.
Outcome:	The proposed method shows great generalization and compatibility across languages and fields.

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Yuxiang Wu, Yu Zhao, Baotian Hu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel

Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation (2022.emnlp-main)

Challenge:	Existing methods rely on parametric models that store knowledge in parameters or retrieval-augmented models that have access to external knowledge sources.
Approach:	They propose a parametric parametric model that stores knowledge in its parameters or a retrieval-augmented model that has access to external knowledge sources.
Outcome:	The proposed method runs substantially faster across the board and produces more accurate results on WoW and ELI5.

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Xiaohui Song, Longtao Huang, Hui Xue, Songlin Hu

RuCoLA: Russian Corpus of Linguistic Acceptability (2022.emnlp-main)

Challenge:	Existing methods to capture emotions in conversation (ERC) lack the correlation between emotions and semantics, resulting in many challenges.
Approach:	They propose a Supervised Prototypical Contrastive Learning (SPCL) loss for the ERC task . they use a Prototype Network to leverage the supervised contrastive learning approach .
Outcome:	The proposed approach outperforms CoG-BART's proposed approach on three widely used benchmarks and shows that it is effective on multiple scenarios.

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Vladislav Mikhailov, Tatiana Shamardina, Max Ryabinin, Alena Pestova, Ivan Smurov, Ekaterina Artemova

Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform (2022.emnlp-main)

Challenge:	Recent research has focused on evaluating the grammatical knowledge of language models with acceptability judgments.
Approach:	They propose to build a corpus of linguistic acceptability in Russian using a binary LA approach.
Outcome:	The proposed set of tests shows that the most widely used language models still fall behind humans by a large margin when detecting morphological and semantic errors.

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Huiru Xiao, Xin Liu, Yangqiu Song, Ginny Wong, Simon See

Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge (2022.emnlp-main)

Challenge:	Existing studies have shown that the choice of space for knowledge graph (KG) embeddings has significant effects on the performance of KG completion tasks.
Approach:	They propose to use the Fourier transform to convert between real and complex hyperbolic space to capture hierarchical patterns.
Outcome:	The proposed models outperform the baseline models for knowledge graph (KG) embeddings.

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Longxu Dou, Yan Gao, Xuqi Liu, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Min-Yen Kan, Jian-Guang Lou

Programming Languages in NLP

Challenge:	Existing approaches to text-to-SQL require domain knowledge to parse expert questions into SQL queries.
Approach:	They propose a framework to leverage domain knowledge during parsing by building a new benchmark KnowSQL with domain-specific questions.
Outcome:	The proposed framework improves the performance of the proposed benchmark by 28.2%.

Should We Ban English NLP for a Year? (2022.emnlp-main)

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Anders Søgaard

LittleBird: Efficient Faster & Longer Transformer for Question Answering (2022.emnlp-main)

Challenge:	aaron carroll: two thirds of NLP research is devoted to developing technology for speakers of English . carroll says this bias feeds into consumer technologies to widen existing inequality gaps . he says we need to consider more concrete measures to mitigate climate change .
Approach:	a new paper argues that NLP is contributing to global inequalities through a digital language divide . a carbon tax, cap-and-trade and car-free Sundays are examples of measures to mitigate climate change .
Outcome:	a new paper argues that NLP is contributing to global inequalities through a digital language divide . a carbon tax, cap-and-trade and car-free Sundays are examples of measures to mitigate climate change .

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Minchul Lee, Kijong Han, Myeong Cheol Shin

WeTS: A Benchmark for Translation Suggestion (2022.emnlp-main)

Challenge:	Existing models for BERT have a limitation dealing with long inputs due to its attention mechanism.
Approach:	They propose a model based on BigBird with improved speed and memory footprint . they propose 'pack and unpack attention' to replace global attention .
Outcome:	The proposed model can work on long inputs even after being pre-trained on short inputs.

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Zhen Yang, Fandong Meng, Yingxue Zhang, Ernan Li, Jie Zhou

Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation (2022.emnlp-main)

Challenge:	Existing studies focus on overall performance of machine translation but ignore TS performance, authors say . if TS is applied into post-editing, it will reduce the time and cost of post-production.
Approach:	They propose to use a golden corpus annotated by experts to generate a translation suggestion model.
Outcome:	The proposed model improves on the golden corpus annotated by translators on four translation directions.

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Chen Wang, Yuchen Liu, Boxing Chen, Jiajun Zhang, Wei Luo, Zhongqiang Huang, Chengqing Zong

Abstractive Summarization Guided by Latent Hierarchical Document Structure (2022.emnlp-main)

Challenge:	Existing zero-shot methods fail to align speech and text into a shared semantic space . Existing methods require expensive and expensive parallel ST data .
Approach:	They propose a method that uses a shared discrete vocabulary space to align speech and text into a common space.
Outcome:	The proposed method significantly improves the SOTA and even performs on par with the strong supervised ST baselines.

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Yifu Qiu, Shay B. Cohen

Explainable Question Answering based on Semantic Graph by Global Differentiable Learning and Dynamic Adaptive Reasoning (2022.emnlp-main)

Challenge:	Sequential abstractive summarizations often do not capture hierarchical and inter-sentential dependencies in the summmarized document.
Approach:	They propose a hierarchy-aware graph neural network which captures hierarchical and inter-sentential dependencies in the summmarized document.
Outcome:	The proposed model improves strong sequence models such as BART with a 0.55 and 0.75 margin in ROUGE-1/2/L for CNN/DM and XSum.

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Jianguo Mao, Wenbin Jiang, Xiangdong Wang, Hong Liu, Yu Xia, Yajuan Lyu, QiaoQiao She

DuReader-Retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine (2022.emnlp-main)

Challenge:	Existing models for multi-hop Question Answering have improved the implicit reasoning ability, but the black box nature of pure neural networks has hindered the construction of explainable intelligent systems.
Approach:	They propose a global differentiation strategy to explore optimal reasoning paths from latent probability space and a Dynamic Adaptive Reasoner to enhance generalization of unseen questions.
Outcome:	The proposed method achieves 17% improvements in F1-score against BreakRC and shows better interpretability.

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Yifu Qiu, Hongyu Li, Yingqi Qu, Ying Chen, QiaoQiao She, Jing Liu, Hua Wu, Haifeng Wang

Pair-Based Joint Encoding with Relational Graph Convolutional Networks for Emotion-Cause Pair Extraction (2022.emnlp-main)

Challenge:	Existing datasets for non-English passage retrieval are lacking in quality and accuracy.
Approach:	They present a large-scale Chinese dataset for passage retrieval . they reduce false negatives by manually annotating results pooled from multiple retrievers .
Outcome:	The proposed dataset reduces false negatives in development and testing sets and removes similar training queries.

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Junlong Liu, Xichen Shang, Qianli Ma

Affective Knowledge Enhanced Multiple-Graph Fusion Networks for Aspect-based Sentiment Analysis (2022.emnlp-main)

Challenge:	Emotion cause pair extraction (ECPE) aims to extract emotion clauses and corresponding cause clauses.
Approach:	They propose a novel task called emotion-cause pair extraction to extract emotion clauses and corresponding cause clauses.
Outcome:	The proposed task can extract emotion clauses and cause clauses, and achieve state-of-the-art performance on the Chinese benchmark corpus.

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Siyu Tang, Heyan Chai, Ziyi Yao, Ye Ding, Cuiyun Gao, Binxing Fang, Qing Liao

IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages (2022.emnlp-main)

Challenge:	Existing methods for sentiment analysis ignore the roles of syntax dependency relation labels and affective semantic information in determining the sentiment polarity of social media users.
Approach:	They propose a new multi-graph fusion network to leverage the richer syntax dependency relation labels and affective semantic information of words.
Outcome:	The proposed model outperforms state-of-the-art methods on three datasets.

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Aman Kumar, Himani Shrotriya, Prachi Sahu, Amogh Mishra, Raj Dabre, Ratish Puduppully, Anoop Kunchukuttan, Mitesh M. Khapra, Pratyush Kumar

Improving Machine Translation with Phrase Pair Injection and Corpus Filtering (2022.emnlp-main)

Challenge:	IndicNLG is a non-English language that is hampered by the scarcity of datasets.
Approach:	They propose to create a dataset for natural language generation for 11 Indic languages . they use a set of pre-trained models to train multilingual models .
Outcome:	The proposed datasets show that pre-trained models perform well in multilingual and monolingual tasks.

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Akshay Batheja, Pushpak Bhattacharyya

An Anchor-based Relative Position Embedding Method for Cross-Modal Tasks (2022.emnlp-main)

Challenge:	In this paper, we show that the combination of Phrase Pair Injection and Corpus Filtering boosts the performance of Neural Machine Translation systems.
Approach:	They propose to combine Phrase Pair Injection and Corpus Filtering to boost performance of Neural Machine Translation systems.
Outcome:	The proposed method improves machine translation models on low-resource language pairs . BLEU score improves over models trained with whole pseudo-parallel corpus augmented with parallel corpus.

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Ya Wang, Xingwu Sun, Lian Fengzong, ZhanHui Kang, Chengzhong Xu Xu

Norm-based Noisy Corpora Filtering and Refurbishing in Neural Machine Translation (2022.emnlp-main)

Challenge:	Position Embedding (PE) is essential for transformer to capture the sequence ordering of input tokens.
Approach:	They propose a unified position embedding method that bridges the semantic gap between modalities and embeds the anchor-based distance to guide computation of cross-attention.
Outcome:	The proposed method obtains new SOTA results on a wide range of benchmarks.

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Yu Lu, Jiajun Zhang

TeleMelody: Lyric-to-Melody Generation with a Template-Based Two-Stage Method (2022.emnlp-main)

Challenge:	Existing noisy corpora filtering methods are insufficient to solve this problem, requiring multiple scorers trained on clean bitexts.
Approach:	They propose to use the information ratio from the source to the target side to distinguish unparallel sentence pairs by using norms of context vectors.
Outcome:	The proposed method performs comparably with state-of-the-art noisy corpora filtering techniques but is more efficient and easier to operate.

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Zeqian Ju, Peiling Lu, Xu Tan, Rui Wang, Chen Zhang, Songruoyao Wu, Kejun Zhang, Xiang-Yang Li, Tao Qin, Tie-Yan Liu

SEEN: Structured Event Enhancement Network for Explainable Need Detection of Information Recall Assistance (2022.emnlp-main)

Challenge:	a new lyric-to-melody generation system bridges the gap between lyrics and melodies . previous generation systems lack paired data and lack of control on generated melodie.
Approach:	They develop a lyric-to-melody generation system with music template to bridge the gap between lyrics and melodies.
Outcome:	The proposed system bridges the gap between lyrics and melodies by using music template.

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You-En Lin, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen

Rethinking Style Transformer with Energy-based Interpretation: Adversarial Unsupervised Style Transfer using a Pretrained Model (2022.emnlp-main)

Challenge:	Existing work on information recall focuses on reactively retrieving life events . but, proactively detecting the need for information recall services is rarely discussed .
Approach:	They propose a human-annotated life experience retelling dataset to detect the right time to trigger an information recall service.
Outcome:	The proposed system detects life event inconsistency, additional information in life events, and forgotten events.

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Hojun Cho, Dohee Kim, Seungwoo Ryu, ChaeHun Park, Hyungjong Noh, Jeong-in Hwang, Minseok Choi, Edward Choi, Jaegul Choo

Towards Robust k-Nearest-Neighbor Machine Translation (2022.emnlp-main)

Challenge:	Existing methods to train text style transfer models with adversarial loss degrade fluency compared to other metrics.
Approach:	They propose a method which leverages a pretrained language model to improve fluency by restructuring the discriminator and the model itself.
Outcome:	The proposed model achieves state-of-the-art on three public benchmarks and achieved state-outperformance on the overall metrics.

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Hui Jiang, Ziyao Lu, Fandong Meng, Chulun Zhou, Jie Zhou, Degen Huang, Jinsong Su

Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation (2022.emnlp-main)

Challenge:	k-Nearest-Neighbor Machine Translation (kNN-MT) is a popular research paradigm in machine translation.
Approach:	They propose a confidence-enhanced kNN-MT model with robust training to reduce noise . they introduce NMT confidence to refine the modeling of important components of kN-MT .
Outcome:	The proposed model improves on four benchmark datasets and is robust to training.

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Yang Yu, Fangzhao Wu, Chuhan Wu, Jingwei Yi, Qi Liu

TABS: Efficient Textual Adversarial Attack for Pre-trained NL Code Model Using Semantic Beam Search (2022.emnlp-main)

Challenge:	Existing work fine tunes the PLM with the news recommendation task, which can cause a domain shift problem.
Approach:	They propose a self-supervised method to adapt general PLM to news domain with a contrastive matching task between news titles and news bodies.
Outcome:	The proposed method can improve both the effectiveness and efficiency of the large PLM-based news recommendation model while maintaining its performance.

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YunSeok Choi, Hyojun Kim, Jee-Hyong Lee

Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples (2022.emnlp-main)

Challenge:	Existing black-box adversarial attacks on pre-trained models generate adversarials with greedy search.
Approach:	They propose an efficient beam search black-box adversarial attack method . they use contextual semantic filtering to effectively reduce the search space .
Outcome:	The proposed method shows good performance in terms of attack success rate, number of queries, and semantic similarity for two tasks: NL code search classification and retrieval tasks.

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Chengyuan Liu, Leilei Gan, Kun Kuang, Fei Wu

Helping the Weak Makes You Strong: Simple Multi-Task Learning Improves Non-Autoregressive Translators (2022.emnlp-main)

Challenge:	State-of-the-art methods based on pre-trained models have achieved remarkable performance on the standard test dataset.
Approach:	They propose to incorporate hierarchical structure of logical forms into the model and exploit automatically generated counterfactual data for training.
Outcome:	The proposed method is effective to alleviate spurious correlations between the headers of the tables and operators of the logical form.

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Xinyou Wang, Zaixiang Zheng, Shujian Huang

RACE: Retrieval-augmented Commit Message Generation (2022.emnlp-main)

Challenge:	Non-autoregressive (NAR) neural machine translation models require a conditional independence assumption on target sequences, resulting in less informative learning signals.
Approach:	They propose a model-agnostic multi-task learning framework to provide more informative learning signals for NAR models under conventional MLE training.
Outcome:	The proposed framework improves accuracy of multiple NAR baselines without additional decoding overhead.

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Ensheng Shi, Yanlin Wang, Wei Tao, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, Hongbin Sun

PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation (2022.emnlp-main)

Challenge:	Existing approaches to automatically generate commit messages are repetitive or redundant.
Approach:	They propose a retrieval-augmented neural commit message generation method which treats the retrieved similar commit as an exemplar and leverages it to generate an accurate commit message.
Outcome:	The proposed method outperforms baselines on a large dataset with five programming languages and can boost existing Seq2Seq models in commit message generation.

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Ao Liu, Haoyu Dong, Naoaki Okazaki, Shi Han, Dongmei Zhang

GHAN: Graph-Based Hierarchical Aggregation Network for Text-Video Retrieval (2022.emnlp-main)

Challenge:	Logical table-to-text generation requires models to derive logical-level facts from table records via logical inference.
Approach:	They propose a pretrained logical form generator framework to improve generation fidelity . they use a dataset to test the logical inference accuracy of the framework .
Outcome:	The proposed framework outperforms baselines on LOGICNLG and CONTLOG on two benchmarks.

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Yahan Yu, Bojie Hu, Yu Li

MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text (2022.emnlp-main)

Challenge:	Existing approaches to text-video retrieval are limited due to structural and semantic differences between text and video.
Approach:	They propose an end-to-end graph-based hierarchical aggregation network for text-video retrieval according to the hierarchy possessed by text and video.
Outcome:	The proposed model achieves Recall@1 of 73.0%, 65.6%, and 64.0% better than the current state-of-the-art model.

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Wenhu Chen, Hexiang Hu, Xi Chen, Pat Verga, William Cohen

PHEE: A Dataset for Pharmacovigilance Event Extraction from Text (2022.emnlp-main)

Challenge:	Pre-trained language models store a massive amount of world knowledge implicitly in their parameters, but large models often fail to encode information about rare entities and events.
Approach:	They propose a retrieval-augmented model which accesses an external non-parametric memory to augment language generation.
Outcome:	The proposed model outperforms existing models by 10-20% absolute on two datasets and under distractor and full-wiki settings.

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Zhaoyue Sun, Jiazheng Li, Gabriele Pergola, Byron Wallace, Bino John, Nigel Greene, Joseph Kim, Yulan He

OTSeq2Set: An Optimal Transport Enhanced Sequence-to-Set Model for Extreme Multi-label Text Classification (2022.emnlp-main)

Challenge:	Using NLP methods to discover and extract adverse drug events from unstructured textual data is difficult because it requires time-consuming manual curation.
Approach:	They propose to use a hierarchical event schema to extract annotated events from medical case reports and biomedical literature to analyze patient data.
Outcome:	The proposed dataset is the largest public dataset to date and contains over 5000 events from medical case reports and biomedical literature.

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Jie Cao, Yin Zhang

SimQA: Detecting Simultaneous MT Errors through Word-by-Word Question Answering (2022.emnlp-main)

Challenge:	Extreme multi-label text classification (XMTC) is a task of finding the most relevant subset labels from an extremely large label set.
Approach:	They propose an autoregressive sequence-to-set model for XMTC tasks named OTSeq2Set.
Outcome:	The proposed model outperforms other competitive baselines on 4 benchmark datasets.

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HyoJung Han, Marine Carpuat, Jordan Boyd-Graber

Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations (2022.emnlp-main)

Challenge:	a good SimulMT system will allow the downstream QA system to answer correctly as quickly as possible.
Approach:	They propose a word-by-word question answering evaluation task to evaluate if models translate salient elements of a question correctly.
Outcome:	a new evaluation task aims to show whether models translate salient elements of a question accurately and quickly . evaluators can reveal weaknesses in existing neural systems, hallucinating or omitting facts . human evaluation is too costly and slow to guide system development, authors say .

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Zhihui Xie, Handong Zhao, Tong Yu, Shuai Li

Rethinking the Authorship Verification Experimental Setups (2022.emnlp-main)

Challenge:	Existing studies show that pre-trained ML-LMs can achieve zero-shot cross-lingual transfer without explicit cross-linguistic supervision.
Approach:	They propose a method to remove language-specific factors from multilingual embedding spaces by using a single value decomposition method with multiple monolingual corpora as input.
Outcome:	The proposed method can boost language agnosticism without finetuning . Empirical results show that it consistently leads to improvements over existing models.

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Florin Brad, Andrei Manolache, Elena Burceanu, Antonio Barbalau, Radu Tudor Ionescu, Marius Popescu

Challenge:	Identifying the author of a text is one of the most versatile NLP tasks, with applications ranging from plagiarism detection to forensics and monitoring the activity of cyber-criminals.
Approach:	They propose five new public splits over the PAN dataset to isolate and identify biases related to the text topic and to the author’s writing style.
Outcome:	The proposed models are competitive with state-of-the-art methods and generalize better on dark reddit datasets.

Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification (2022.emnlp-main)

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Chunpu Xu, Jing Li

Training Language Models with Memory Augmentation (2022.emnlp-main)

Challenge:	Social media users are using images and text to voice opinions and share ideas.
Approach:	They propose to use user comments to extract hinting features from user comments and explore them via self-training.
Outcome:	The proposed framework improves on four social media benchmarks for image-text relation classification, sarcasm detection, sentiment classification, and hate speech detection.

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Zexuan Zhong, Tao Lei, Danqi Chen

Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages (2022.emnlp-main)

Challenge:	Existing methods for training memory-augmented language models only introduce mem-ories at testing time or represent them using a separately trained encoder.
Approach:	They propose a training approach that directly takes in-batch examples as accessible memory and new methods for memory construction and data batching that are used for adapting to different sets of memories at testing time.
Outcome:	The proposed approach reduces perplexity from 18.70 to 15.37 on multiple language modeling and machine translation benchmarks.

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Paul Röttger, Debora Nozza, Federico Bianchi, Dirk Hovy

Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder (2022.emnlp-main)

Challenge:	Hate speech datasets focus on English-language content, hindering effective models . annotating hateful content is expensive, time-consuming and potentially harmful to annotators.
Approach:	They propose to use ISO 639-1 codes to fine-tune models on one source language and apply them to another language.
Outcome:	The proposed approach performs well on some tasks, but fails on many others.

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Zhenghao Liu, Han Zhang, Chenyan Xiong, Zhiyuan Liu, Yu Gu, Xiaohua Li

Controlled Text Reduction (2022.emnlp-main)

Challenge:	Existing work reserves the principle dimensions of query and document embeddings for building more efficient retrieval systems.
Approach:	They propose to use Conditional Autoencoder to compress high-dimensional embeddings to maintain the same embeddable distribution and better recover ranking features.
Outcome:	The proposed algorithm achieves comparable ranking performance with its teacher model and makes the retrieval system more efficient.

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Aviv Slobodkin, Paul Roit, Eran Hirsch, Ori Ernst, Ido Dagan

Questioning the Validity of Summarization Datasets and Improving Their Factual Consistency (2022.emnlp-main)

Challenge:	Abstractive text summarization models separate the salience detection phase from the text generation phase.
Approach:	They propose to formalize Controlled Text Reduction as a standalone task . they advocate the potential of such models for modular fully-automatic summarization .
Outcome:	The proposed model shows that it is possible to produce a reduced version of a source text using decomposed modeling.

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Yanzhu Guo, Chloé Clavel, Moussa Kamal Eddine, Michalis Vazirgiannis

Invariant Language Modeling (2022.emnlp-main)

Challenge:	Abstractive summarization systems have a lack of a defined definition for the task . factual consistency is a key factor in summarizing, but there are still deficiencies . a new study shows that summarized summarisation models achieve improved performance .
Approach:	They propose a filtered summarization dataset with improved factual consistency to address this problem . they argue that the dataset should become a valid benchmark for developing and evaluating summarizing systems .
Outcome:	The proposed model improves on a popular summarization dataset with improved factual consistency.

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Maxime Peyrard, Sarvjeet Ghotra, Martin Josifoski, Vidhan Agarwal, Barun Patra, Dean Carignan, Emre Kiciman, Saurabh Tiwary, Robert West

AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning (2022.emnlp-main)

Challenge:	Existing methods to remove spurious correlations and biases involve expensive domain alignment.
Approach:	They propose a framework for learning invariant representations that generalize better across environments . they adapt a game-theoretic implementation of IRM to language models .
Outcome:	The proposed framework can remove structured noise, ignore correlations and achieve better generalization across environments.

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Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, Jianfeng Gao

How “Multi” is Multi-Document Summarization? (2022.emnlp-main)

Challenge:	Standard fine-tuning of large pre-trained language models requires updating hundreds of millions to billions of parameters and storing a large copy of the PLM weights for every task.
Approach:	They propose a parameter-efficient fine-tuning technique where small trainable components are injected into the PLM and updated during fine-uning.
Outcome:	The proposed method outperforms SOTA parameter-efficient fine-tuning and full model fine-uning on GLUE development set with RoBERTa-large encoder.

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Ruben Wolhandler, Arie Cattan, Ori Ernst, Ido Dagan

BioReader: a Retrieval-Enhanced Text-to-Text Transformer for Biomedical Literature (2022.emnlp-main)

Challenge:	Multi-document summarization (MDS) aims at combining information spread across multiple documents . a single document often covers the full summary content .
Approach:	They propose a measure to evaluate the degree to which a summary is "disperse" they propose to combine information from multiple documents into a single document to generate a concise summary .
Outcome:	The proposed measure evaluates the degree to which a summary is "disperse" the measure is applied to several popular MDS datasets and state-of-the-art systems.

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Giacomo Frisoni, Miki Mizutani, Gianluca Moro, Lorenzo Valgimigli

T-Modules: Translation Modules for Zero-Shot Cross-Modal Machine Translation (2022.emnlp-main)

Challenge:	Recent research has equipped language models with the ability to attend over relevant and factual information from non-parametric external sources, drawing a complementary path to architectural scaling.
Approach:	They propose a retrieval-enhanced text-to-text model that augments the input prompt by fetching and assembling relevant scientific literature chunks from a neural database centered on PubMed.
Outcome:	The proposed model outperforms state-of-the-art models on a broad array of downstream tasks while using up to 3x fewer parameters.

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Paul-Ambroise Duquenne, Hongyu Gong, Benoît Sagot, Holger Schwenk

LILA: A Unified Benchmark for Mathematical Reasoning (2022.emnlp-main)

Challenge:	Existing approaches to perform zero-shot cross-modal transfer between speech and text are limited to a very small number of language pairs.
Approach:	They propose a method to perform zero-shot cross-modal transfer between speech and text for translation tasks by using a speech decoder.
Outcome:	The proposed model significantly improves state-of-the-art for zero-shot speech translation on Must-C.

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Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark, Ashwin Kalyan

Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding (2022.emnlp-main)

Challenge:	Towards evaluating and improving AI systems in this domain, we propose a mathematical reasoning benchmark based on 23 diversetasks .
Approach:	They propose a mathematical reasoning benchmark that includes 23 diverse tasks . they extend the benchmark by collecting task instructions and solutions in the form of Python programs .
Outcome:	The proposed model improves on multi-tasking while the best performing model only achieves 60.40%.

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Md Mosharaf Hossain, Eduardo Blanco

GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation (2022.emnlp-main)

Challenge:	Negation poses a challenge in many natural language understanding tasks . leveraging sentences with negation and affirmative interpretations is beneficial for many tasks involving humans .
Approach:	They propose to collect negated sentences and their affirmative interpretations and leverage them to build a plug-and-play neural generator that generates an affirmative interpreter.
Outcome:	The proposed method does not require manual effort and does not impact other tasks.

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Lunyiu Nie, Shulin Cao, Jiaxin Shi, Jiuding Sun, Qi Tian, Lei Hou, Juanzi Li, Jidong Zhai

InforMask: Unsupervised Informative Masking for Language Model Pretraining (2022.emnlp-main)

Challenge:	Existing approaches to neural semantic parsing are limited by the semantic gap between natural and formal languages.
Approach:	They propose a unified intermediate representation for graph query languages, named GraphQ IR, which has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure.
Outcome:	The proposed representation can convert user queries into graphQ IR, which can later be losslessly compiled into various downstream graph query languages.

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Nafis Sadeq, Canwen Xu, Julian McAuley

CTRLsum: Towards Generic Controllable Text Summarization (2022.emnlp-main)

Challenge:	Masked language modeling is used for pretraining large language models for knowledge-intensive tasks.
Approach:	They propose an unsupervised masking strategy that exploits Pointwise Mutual Information to select the most informative tokens to mask.
Outcome:	The proposed strategy outperforms random masking and previously proposed masking strategies on the factual recall benchmark LAMA and the question answering benchmark SQuAD v1 and v2.

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Junxian He, Wojciech Kryscinski, Bryan McCann, Nazneen Rajani, Caiming Xiong

Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation (2022.emnlp-main)

Challenge:	Existing summarization systems produce generic summaries that are disconnected from users’ preferences and expectations.
Approach:	They propose a generic framework to control generated summaries through a set of keywords.
Outcome:	The proposed framework is comparable or better than strong pretrained systems on three domains of summarization datasets and five control tasks.

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Max Glockner, Yufang Hou, Iryna Gurevych

A Framework for Adapting Pre-Trained Language Models to Knowledge Graph Completion (2022.emnlp-main)

Challenge:	Existing NLP task definitions for fact-checking cannot refute misinformation as professional fact- checkers do for the majority of claims.
Approach:	They compare NLP-based fact-checking with professional fact- checkers . they find that evidence must be sufficient to refute the claim and not leaked .
Outcome:	The proposed models fail to meet the criteria for realistic fact-checking . they also fail to satisfy the criteria of leaked evidence .

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Justin Lovelace, Carolyn Rosé

Mutual Information Alleviates Hallucinations in Abstractive Summarization (2022.emnlp-main)

Challenge:	Recent work has demonstrated that entity representations can be extracted from pre-trained language models to develop knowledge graph completion models that are more robust to the naturally occurring sparsity found in knowledge graphs.
Approach:	They propose unsupervised and supervised methods to extract more informative representations from pre-trained language models to develop knowledge graph completion models.
Outcome:	The proposed model outperforms recent neural models in terms of performance and unsupervised processing methods.

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Liam van der Poel, Ryan Cotterell, Clara Meister

Toward the Limitation of Code-Switching in Cross-Lingual Transfer (2022.emnlp-main)

Challenge:	Abstractive summarization models exhibit the tendency to hallucinate, i.e., output content not supported by the source document.
Approach:	They propose a decoding strategy that optimizes for pointwise mutual information of source and target tokens when models exhibit uncertainty.
Outcome:	The proposed method decreases the probability of hallucinated tokens while maintaining the Rouge and BERT-S scores of top-performing decoding strategies.

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Yukun Feng, Feng Li, Philipp Koehn

Syntactically Rich Discriminative Training: An Effective Method for Open Information Extraction (2022.emnlp-main)

Challenge:	Recent studies have shown the success of multilingual pretrained models for cross-lingual knowledge transfer.
Approach:	They propose to make code-switched sentences replace tokens from multiple languages so they are grammatically consistent . they also consider the similarity between context and the switched tokens to ensure that the newly substituted sentences are grammatically consistent - a limitation that could affect inference .
Outcome:	The proposed method outperforms the mBERT and original code-switching method on cross-lingual POS and Named-Entity-Recognition tasks on 30+ languages.

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Frank Mtumbuka, Thomas Lukasiewicz

Transformer-based Entity Typing in Knowledge Graphs (2022.emnlp-main)

Challenge:	Open information extraction (OIE) is the task of extracting facts from natural language text.
Approach:	They propose a method for computing syntactically rich text embeddings using the structure of dependency trees and a discriminative approach to OIE where tokens in the generated fact are classified as "real" and "fake" they propose to reduce repetitive tokens and improve models' ability to generate implicit facts by a factor of 23%.
Outcome:	The proposed method reduces repetitive tokens by a factor of 23% on the CaRB, OIE2016, and LSOIE datasets and improves on augmented datasets.

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Zhiwei Hu, Victor Gutierrez-Basulto, Zhiliang Xiang, Ru Li, Jeff Pan

NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge (2022.emnlp-main)

Challenge:	Existing knowledge graphs encoding entity types are far from complete, since in real-world applications they are continuously emerging.
Approach:	They propose a transformer-based approach to infer plausible entity types by encoding neighbours' information by a local transformer and a global transformer.
Outcome:	The proposed approach outperforms the state-of-the-art on two real-world datasets.

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Revanth Gangi Reddy, Sai Chetan Chinthakindi, Zhenhailong Wang, Yi Fung, Kathryn Conger, Ahmed ELsayed, Martha Palmer, Preslav Nakov, Eduard Hovy, Kevin Small, Heng Ji

IsoVec: Controlling the Relative Isomorphism of Word Embedding Spaces (2022.emnlp-main)

Challenge:	Current claims detection methods focus on sentence analysis, ignoring other attributes . a key element of identifying misinformation is detecting the claims and the arguments that have been presented.
Approach:	They propose a benchmark for attribute-aware claim detection in the news domain . they extend the problem to include extraction of additional attributes related to each claim .
Outcome:	The proposed system performs well on the test, but human performance is still poor.

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Kelly Marchisio, Neha Verma, Kevin Duh, Philipp Koehn

Adversarial Concept Erasure in Kernel Space (2022.emnlp-main)

Challenge:	a faulty cross-lingual mapping technique results in non-isomorphic word embedding spaces . we address the root-cause of this problem by increasing the relative isomorphism of word embedsing spaces.
Approach:	They address the root-cause of faulty cross-lingual mapping by incorporating global measures of isomorphism into the skipgram loss function.
Outcome:	The proposed method improves bilingual lexicon induction under domain mismatch and with training algorithm dissimilarities.

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Shauli Ravfogel, Francisco Vargas, Yoav Goldberg, Ryan Cotterell

The Authenticity Gap in Human Evaluation (2022.emnlp-main)

Challenge:	Large neural networks in NLP produce real-valued representations that encode the bit of human language that they were trained on.
Approach:	They propose a kernelization of the recently-proposed linear concept-removal objective and propose to remove linear subspaces from the representation space.
Outcome:	The proposed kernelization protects against the ability of nonlinear adversaries to recover the concept.

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Kawin Ethayarajh, Dan Jurafsky

BERT in Plutarch’s Shadows (2022.emnlp-main)

Challenge:	Using the standard protocol to evaluate NLGs is often violated, resulting in annotator ratings cease to reflect their preferences.
Approach:	They propose a human evaluation protocol called system-level probabilistic assessment (SPA) this protocol is based on the assumption that annotators are biased by likert scales .
Outcome:	The proposed protocol can recover the ordering of GPT-3 models by size, but less than half of the expected preferences can be recovered when human evaluation is done with the standard protocol.

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Ivan Yamshchikov, Alexey Tikhonov, Yorgos Pantis, Charlotte Schubert, Jürgen Jost

Leveraging Locality in Abstractive Text Summarization (2022.emnlp-main)

Challenge:	Existing methods to estimate authorship of texts rely on statistical patterns like word or sentence length to distinguish between different authors.
Approach:	They propose to use a BERT language model to identify authorship in ancient Greek texts . they find that the Placita Philosophorum and other works are similar to those written by Alexandrian authors .
Outcome:	The proposed model can shed new light on the authorship question in ancient Greek and shows similarities with the works written by authors from Alexandrian contexts.

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Yixin Liu, Ansong Ni, Linyong Nan, Budhaditya Deb, Chenguang Zhu, Ahmed Hassan Awadallah, Dragomir Radev

Salience Allocation as Guidance for Abstractive Summarization (2022.emnlp-main)

Challenge:	Neural attention models have improved on many natural language processing tasks, but their quadratic memory complexity hinders their applications in long text summarization.
Approach:	They propose to use a restricted context to study locality in text summarization . they propose to employ a quadratic memory growth with respect to the input length .
Outcome:	The proposed model has better performance than baseline models with efficient attention modules.

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Fei Wang, Kaiqiang Song, Hongming Zhang, Lifeng Jin, Sangwoo Cho, Wenlin Yao, Xiaoyang Wang, Muhao Chen, Dong Yu

Fine-tuned Language Models are Continual Learners (2022.emnlp-main)

Challenge:	Abstractive summarization models implicitly learn to capture the salient information from scratch.
Approach:	They propose a method that uses salience expectation to guide abstractive summarization by averaging salient content to a fixed threshold.
Outcome:	The proposed method can be easily adapted to documents with various abstractiveness and achieves high performance.

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Thomas Scialom, Tuhin Chakrabarty, Smaranda Muresan

Natural Logic-guided Autoregressive Multi-hop Document Retrieval for Fact Verification (2022.emnlp-main)

Challenge:	Recent work on large language models relies on intuition that most tasks can be described via natural language instructions.
Approach:	They propose that a model should be able to keep extending its knowledge without forgetting previous skills.
Outcome:	The proposed model can learn 8 new diverse language generation tasks while maintaining good performance on previous tasks, spanning in total of 70 datasets.

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Rami Aly, Andreas Vlachos

AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis (2022.emnlp-main)

Challenge:	Recent evidence retrieval approaches rely on heuristics and assume hyperlinks between documents.
Approach:	They propose a retrieval method that combines a retriever and a proof system that reranks documents and reorders them .
Outcome:	The proposed method exceeds or is on par with the current state-of-the-art on FEVER, HoVer and FEVEROUS-S while using 5 to 10 times less memory than competing systems.

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Sabyasachi Kamila, Walid Magdy, Sourav Dutta, MingXue Wang

Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning (2022.emnlp-main)

Challenge:	Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health.
Approach:	They propose a weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data.
Outcome:	The proposed framework outperforms weakly supervised baselines on four benchmark datasets and is able to generate multiple aspect category-sentiment pairs per review sentence.

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Roshanak Mirzaee, Parisa Kordjamshidi

A Survey of Active Learning for Natural Language Processing (2022.emnlp-main)

Challenge:	Existing datasets on spatial language processing are either synthetic or at small scale.
Approach:	They propose a dataset for transfer learning on spatial question answering and spatial role labeling that includes a larger variety of spatial relation types and spatial expressions.
Outcome:	The proposed dataset can be used to evaluate spatial language processing models in real-world situations.

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Zhisong Zhang, Emma Strubell, Eduard Hovy

Bernice: A Multilingual Pre-trained Encoder for Twitter (2022.emnlp-main)

Challenge:	Existing literature surveys on active learning for NLP are too specific or too general, covering deep active learning.
Approach:	They propose to use active learning to improve model learning and annotation cost for NLP problems.
Outcome:	The proposed approach is based on a large dataset of data-driven machine learning models.

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Alexandra DeLucia, Shijie Wu, Aaron Mueller, Carlos Aguirre, Philip Resnik, Mark Dredze

CEFR-Based Sentence Difficulty Annotation and Assessment (2022.emnlp-main)

Challenge:	Existing language models for Twitter are monolingual, adapted from other domains, or trained on limited amount of in-domain data.
Approach:	They propose a multilingual RoBERTa language model that is trained from scratch on 2.5 billion tweets with a custom tweet-focused tokenizer.
Outcome:	The proposed model outperforms or matches models trained on monolingual and multilingual tweets on a variety of benchmarks and is more efficient compute- and data-wise to train completely on in-domain data with a specialized domain-specific tokenizer.

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Yuki Arase, Satoru Uchida, Tomoyuki Kajiwara

Text Complexity

Challenge:	Controllable text simplification is a crucial assistive technique for language learning and teaching.
Approach:	They propose a sentence-level assessment model to handle unbalanced level distribution . previous studies have suggested that controllable text simplification is difficult to apply .
Outcome:	The proposed method outperforms baselines in readability assessment by scoring macro-F1 on the level assessment.

Simple Questions Generate Named Entity Recognition Datasets (2022.emnlp-main)

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Hyunjae Kim, Jaehyo Yoo, Seunghyun Yoon, Jinhyuk Lee, Jaewoo Kang

TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models (2022.emnlp-main)

Challenge:	Recent named entity recognition models rely on human-annotated datasets . however, in-domain dictionaries and sentences are often unavailable or expensive to construct for many entity types.
Approach:	They propose an ask-to-generate approach which automatically generates NER datasets by asking natural language questions to an open-domain question answering system.
Outcome:	The proposed model outperforms the previous best model by 19.5 F1 score on six benchmarks and achieves state-of-the-art performance.

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Joel Jang, Seonghyeon Ye, Changho Lee, Sohee Yang, Joongbo Shin, Janghoon Han, Gyeonghun Kim, Minjoon Seo

Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction (2022.emnlp-main)

Challenge:	Language Models (LMs) become outdated as the world changes, a phenomenon called temporal misalignment.
Approach:	They propose a lifelong benchmark that utilizes the difference between consecutive snapshots of English Wikipedia and English Wikidata for training and evaluation.
Outcome:	The proposed benchmark can be trained on the difference between consecutive snapshots of English Wikipedia and English Wikidata for training and evaluation.

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Lu Dai, Bang Wang, Wei Xiang, Yijun Mo

Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation (2022.emnlp-main)

Challenge:	Existing prompt-tuning methods for event argument extraction lack entity information . eAE is a key step of event extraction, but it requires a pre-trained language model to extract event arguments.
Approach:	They propose a prompt-tuning method that takes advantage of entity information and pre-trained language models.
Outcome:	The proposed method outperforms the state-of-the-art prompt-tuning methods on an english dataset.

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Peiyi Wang, Yifan Song, Tianyu Liu, Binghuai Lin, Yunbo Cao, Sujian Li, Zhifang Sui

ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering (2022.emnlp-main)

Challenge:	Existing studies attribute catastrophic forgetting to the corruption of the learned representations as new relations come . Continual relation extraction models suffer from catastrophic forgetting when learning new relations .
Approach:	They propose to use adversarial class augmentation mechanism to learn more precise representations . they propose to train the model on a sequence of tasks where two new relations are discovered .
Outcome:	The proposed model improves on two popular benchmarks.

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Zhiyu Chen, Shiyang Li, Charese Smiley, Zhiqiang Ma, Sameena Shah, William Yang Wang

A Span-based Multimodal Variational Autoencoder for Semi-supervised Multimodal Named Entity Recognition (2022.emnlp-main)

Challenge:	Recent advances in large pre-trained language models have brought the NLP field into a new era.
Approach:	They propose a large-scale dataset to study the chain of numerical reasoning in conversational question answering.
Outcome:	The proposed dataset should push forward the exploration of real-world, complex reasoning tasks as the next research focus.

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Baohang Zhou, Ying Zhang, Kehui Song, Wenya Guo, Guoqing Zhao, Hongbin Wang, Xiaojie Yuan

R-TeaFor: Regularized Teacher-Forcing for Abstractive Summarization (2022.emnlp-main)

Challenge:	Existing methods for named entity recognition on social media are not efficient for semi-supervised MNER because of the mismatch between the posted text and image.
Approach:	They propose a novel method to fuse the text and image features for multimodal named entity recognition under semi-supervised setting by exploiting modal-specific VAEs.
Outcome:	The proposed method outperforms baselines under supervised setting and improves performance with less labeled data than existing semi-supervised methods.

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Guan-Yu Lin, Pu-Jen Cheng

Modeling Consistency Preference via Lexical Chains for Document-level Neural Machine Translation (2022.emnlp-main)

Challenge:	Existing studies on teacher-forcing do not consider the pairwise relationship between training data and modified data, which provides more information during training.
Approach:	They propose to use teacher-forcing to address exposure bias problem by modifying training data to simulate model-generated results.
Outcome:	Empirically, the proposed method outperforms state-of-the-art models and can be generalized to different pre-trained models.

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Xinglin Lyu, Junhui Li, Shimin Tao, Hao Yang, Ying Qin, Min Zhang

Just Fine-tune Twice: Selective Differential Privacy for Large Language Models (2022.emnlp-main)

Challenge:	Experimental results show that consistency preference for lexical chains reduces lexical translation inconsistency . Lexical translation consistency is a common discourse phenomenon .
Approach:	They propose a consistency-aware model which captures consistency context . they then define consistency-tailored latent variables which guide translation of corresponding sentences .
Outcome:	The proposed model significantly improves translation performance in ChineseEnglish and FrenchEnglish translation tasks.

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Weiyan Shi, Ryan Shea, Si Chen, Chiyuan Zhang, Ruoxi Jia, Zhou Yu

Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents (2022.emnlp-main)

Challenge:	Existing approaches to protect language models from privacy leakage suffer from limited user control and low utility . et al., 2018: a novel framework that achieves SDP for state-of-the-art large transformer-based models.
Approach:	They propose a framework that applies differential privacy to large language models . they use redacted in-domain data to fine-tune the model with original in- domain data .
Outcome:	The proposed framework achieves strong utility compared to baselines.

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Marcio Fonseca, Yftah Ziser, Shay B. Cohen

Open-Domain Sign Language Translation Learned from Online Video (2022.emnlp-main)

Challenge:	Using a factorization approach, summarization decisions are conflated into a single feedforward step without taking into account contextual factors.
Approach:	They propose to factorize summarization into two steps following a budget and content guidance.
Outcome:	The proposed method outperforms PEGASUS in domain adaptation and generates significantly higher ROUGE scores on multiple benchmarks for long document summarization.

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Bowen Shi, Diane Brentari, Gregory Shakhnarovich, Karen Livescu

Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change (2022.emnlp-main)

Challenge:	Existing work on sign language translation has focused mainly on data collected in controlled environments or domains, which limits its applicability to real-world settings.
Approach:	They propose to use sign search as a pretext task and fusion of mouthing and handshape features to improve sign language translation in real-world settings.
Outcome:	The proposed techniques produce consistent and large improvements over baseline models based on prior work.

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Zhaochen Su, Zecheng Tang, Xinyan Guan, Lijun Wu, Min Zhang, Juntao Li

ULN: Towards Underspecified Vision-and-Language Navigation (2022.emnlp-main)

Challenge:	Existing methods to improve neural language models perform poorly on emerging data.
Approach:	They propose a lexical-level masking strategy to post-train a neural language model using static data from past years.
Outcome:	The proposed method outperforms existing methods on two pre-trained language models, two classification tasks, and four benchmark datasets.

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Weixi Feng, Tsu-Jui Fu, Yujie Lu, William Yang Wang

Federated Model Decomposition with Private Vocabulary for Text Classification (2022.emnlp-main)

Challenge:	Existing vision-and-language navigation models are brittle to multi-level language underspecification.
Approach:	They propose to use multi-level underspecified instructions to guide agents . they propose to learn GSS for navigation agent to ground multi- level instructions . experimental results show existing VLN models are still brittle to multi-language underspecification .
Outcome:	Experimental results show that the proposed framework outperforms baselines on ULN by 10% relative success rate across all levels.

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Zhuo Zhang, Xiangjing Hu, Lizhen Qu, Qifan Wang, Zenglin Xu

ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks (2022.emnlp-main)

Challenge:	Existing methods to train federated learning (FL) for natural language processing require sensitive data to leave local devices.
Approach:	They propose a fedrated model decomposition method that protects the privacy of vocabularies . they propose an adaptive updating technique to improve the performance of local models .
Outcome:	The proposed method protects the privacy of vocabularies in federated learning tasks . it maintains competitive performance and provides better privacy-preserving capacity compared to status quo methods.

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Kai Xiong, Xiao Ding, Zhongyang Li, Li Du, Ting Liu, Bing Qin, Yi Zheng, Baoxing Huai

Video Question Answering: Datasets, Algorithms and Challenges (2022.emnlp-main)

Challenge:	Causal chain reasoning models suffer from two main transitive problems: threshold effect and scene drift.
Approach:	They propose a framework that uses exogenous variables to represent causal pairs and estimates the threshold and scene contradictions using structural causal recurrent neural networks.
Outcome:	The proposed framework outperforms baselines on Chinese and English CCR datasets.

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Yaoyao Zhong, Wei Ji, Junbin Xiao, Yicong Li, Weihong Deng, Tat-Seng Chua

Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation (2022.emnlp-main)

Challenge:	Recent advances in video question answering have led to a surge in popularity . despite the popularity, VideoQA remains one of the greatest challenges .
Approach:	They categorize the video question-answer datasets into normal VideoQA, multi-modal VideoQA and knowledge-based VideoQA according to the modalities invoked in the question-announcement pairs.
Outcome:	The proposed methods are mainly designed for Factoid QA and inference VideoQA . the proposed methods have been compared with other methods and are robust and interpretable.

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Deng Cai, Xin Li, Jackie Chun-Sing Ho, Lidong Bing, Wai Lam

Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling (2022.emnlp-main)

Challenge:	Existing studies on multilingual sentence embeddings focus on cross-lingual semantic textual similarity and transfer tasks.
Approach:	They propose a method to improve existing multilingual sentence embeddings with Abstract Meaning Representation (AMR) . they compare existing multi-lingual sentence embedded with AMR and improve their versions by reducing the surface variations across different languages and expressions.
Outcome:	The proposed method improves state-of-the-art multilingual sentence embeddings on transfer tasks and semantic textual similarity tests.

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Zhijun Wang, Xuebo Liu, Min Zhang

Boundary-Driven Table-Filling for Aspect Sentiment Triplet Extraction (2022.emnlp-main)

Challenge:	Existing research treats Chinese character as a minimum unit for representation . however, such representation suffers from two bottlenecks: 1) learning bottleneck; 2) parameter bottleneck, each individual character has to be represented by a unique vector.
Approach:	They propose a representation method for Chinese characters to break the representation bottlenecks . they map each stroke to a specific Latin character, thus allowing similar Chinese characters .
Outcome:	The proposed representation method breaks two representation bottlenecks in Chinese character representation . it maps each stroke to a specific Latin character, thus allowing similar Chinese characters to have similar representations .

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Yice Zhang, Yifan Yang, Yihui Li, Bin Liang, Shiwei Chen, Yixue Dang, Min Yang, Ruifeng Xu

Attention and Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition (2022.emnlp-main)

Challenge:	Existing work focuses on extracting aspect terms and opinion terms without considering the relations between aspect terms .
Approach:	They propose a task to extract aspect terms, opinion terms, and expressed sentiments from a two-dimensional (2D) table.
Outcome:	The proposed method achieves state-of-the-art on several public benchmarks and is well-suited to the ASTE task.

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Renjie Zhou, Zhongyi Xie, Jian Wan, Jilin Zhang, Yong Liao, Qiang Liu

Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)

Challenge:	Named entity recognition (NER) is the recognition of entities with specific meanings in the text, mainly including person, organization, location, etc.
Approach:	They propose an edge-aware node joint update module and introduce a node-awful edge update module to explore hidden in structured information and solve the wrong dependency label information to some extent.
Outcome:	The proposed model can exploit the structured information on the dependency tree to improve the recognition of long entities.

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Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou

Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models (2022.emnlp-main)

Challenge:	Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text.
Approach:	They propose to use a sentence-level dataset to benchmark Open Event Extraction without restricting event types.
Outcome:	The proposed dataset contains more than 42,000 news titles in 34 topics collected from Chinese web pages.

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Chaitanya Malaviya, Sudeep Bhatia, Mark Yatskar

Cognitive Modeling

Challenge:	Cognitive psychologists have documented that humans use cognitive heuristics to make quick decisions while expending less effort.
Approach:	They propose tracking annotator heuristic traces where they measure low-effort annotation strategies that could indicate usage of various cognitive heurs.
Outcome:	The proposed tracking annotator heuristic traces shows that annotators are using multiple cognitive heurs based on psychological tests.

Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts (2022.emnlp-main)

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Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish Sabharwal

ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation (2022.emnlp-main)

Challenge:	Question-answering datasets require a broad set of reasoning skills.
Approach:	They use QDMR representations to programmatically create hard-to-cheat synthetic contexts for real questions in multi-step reasoning datasets.
Outcome:	The proposed model improves performance by 13 F1 points on 4 multi-step QA datasets and 21 points on more complex questions.

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Fan Yin, Yao Li, Cho-Jui Hsieh, Kai-Wei Chang

G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks (2022.emnlp-main)

Challenge:	Existing methods show poor performance under Far Boundary (FB) adversarial examples.
Approach:	They propose to use a new technique to detect adversarial examples based on data and model uncertainty to outperform existing methods.
Outcome:	The proposed method outperforms existing methods by 3.6 and 6.0 AUC points under each scenario.

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Zhongwei Wan, Yichun Yin, Wei Zhang, Jiaxin Shi, Lifeng Shang, Guangyong Chen, Xin Jiang, Qun Liu

Towards Unifying Reference Expression Generation and Comprehension (2022.emnlp-main)

Challenge:	Existing domain-adaptive pre-training (DAPT) models tend to forget the general knowledge acquired by general PLMs, leading to catastrophic forgetting and sub-optimal performance.
Approach:	They propose a framework which augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge.
Outcome:	The proposed framework augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge.

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Duo Zheng, Tao Kong, Ya Jing, Jiaan Wang, Xiaojie Wang

Textual Manifold-based Defense Against Natural Language Adversarial Examples (2022.emnlp-main)

Challenge:	Existing models for REG and REC have distinct inputs and connections between them . a new model for REg and reprehension is needed to solve these problems .
Approach:	They propose a unified model for REG and REC that fuses image, region and text . they propose Vision-conditioned Masked Language Modeling and Text-Conditioned Region Prediction .
Outcome:	The proposed model outperforms existing models on REG and REC tasks.

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Dang Nguyen Minh, Anh Tuan Luu

Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters (2022.emnlp-main)

Challenge:	Existing studies on adversarial images have shown that they leave the low-dimensional data manifold . Various defenses have been proposed to counter adversarials in NLP .
Approach:	They propose a defense mechanism that learns the embedding space manifold of the underlying language model and projects novel inputs back to the approximated structure before classification.
Outcome:	The proposed defense outperforms existing defenses under various attack settings while remaining unaffected to the clean accuracy.

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Hongyu Zhao, Hao Tan, Hongyuan Mei

Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives (2022.emnlp-main)

Challenge:	Adapter-tuning is a paradigm that transfers a pretrained language model to downstream tasks . Previously proposed adapters are all feed-forward neural networks .
Approach:	They propose to use tiny-attention attention with extremely small per-head dimensionality as adapters to modify hidden states at each position . they propose to average multiple attention heads' weights during deployment to reduce its inference computation cost.
Outcome:	The proposed adapter outperforms other adapter-tuning methods on the GLUE benchmark . it uses attention with extremely small per-head dimensionality to modify hidden states .

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Si Sun, Chenyan Xiong, Yue Yu, Arnold Overwijk, Zhiyuan Liu, Jie Bao

ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts (2022.emnlp-main)

Challenge:	Recent research shows strong empirical advantages of dense retrieval in various information access scenarios, such as openQA.
Approach:	They propose a method which accumulates momentum negatives from past iterations and approximates future iteration with lookahead negatives as "teleportations" on web search and OpenQA, ANCE-Tele outperforms previous state-of-the-art systems of similar size and eliminates the dependency on sparse retrieval negatives.
Outcome:	The proposed method outperforms previous state-of-the-art systems on web search and OpenQA and is competitive among systems with significantly more parameters.

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Akari Asai, Mohammadreza Salehi, Matthew Peters, Hannaneh Hajishirzi

Exploration of the Usage of Color Terms by Color-blind Participants in Online Discussion Platforms (2022.emnlp-main)

Challenge:	a new multi-task, parameter-efficient language model tuning method learns to transfer knowledge across different tasks via a mixture of soft prompts.
Approach:	They propose a multi-task, parameter-efficient language model tuning method that uses soft prompts to learn to transfer knowledge across different tasks.
Outcome:	The proposed method outperforms prompt tuning and outperfies or matches fully fine-tuned tuning approaches that use 10 times more parameters.

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Ella Rabinovich, Boaz Carmeli

Challenge:	Existing studies on the role of sensory vs. linguistic input in the way we acquire and use language have not explored the relative effects of various factors on our linguistic system.
Approach:	They propose to use a newly curated dataset to examine the conceptual perception of colors by color-blind speakers.
Outcome:	The findings shed some new and interesting light on the role of sensory experience on our linguistic system.

DEER: Descriptive Knowledge Graph for Explaining Entity Relationships (2022.emnlp-main)

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Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-mei Hwu

META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI (2022.emnlp-main)

Challenge:	Existing knowledge graphs lack two desired features for modeling entity relationships: openness and informativeness.
Approach:	They propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and generate relation descriptions using a transformer-based relation description synthesizing model.
Outcome:	The proposed system extracts and generates high-quality relation descriptions without human labeling.

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Liangtai Sun, Xingyu Chen, Lu Chen, Tianle Dai, Zichen Zhu, Kai Yu

Understanding and Improving Knowledge Distillation for Quantization Aware Training of Large Transformer Encoders (2022.emnlp-main)

Challenge:	Current task-oriented dialogue systems focus on multi-turn text/speech interaction, then call back-end APIs to perform task.
Approach:	They propose a GUI-based task-oriented dialogue system that can perform GUI operations on real APPs without invoking TOD-specific backend APIs.
Outcome:	The proposed GUI-based task-oriented dialogue system can perform GUI operations on real APPs and execute tasks without invoking TOD-specific backend APIs.

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Minsoo Kim, Sihwa Lee, Suk-Jin Hong, Du-Seong Chang, Jungwook Choi

Exploring Mode Connectivity for Pre-trained Language Models (2022.emnlp-main)

Challenge:	Knowledge distillation (KD) has been used for quantization-aware training to improve the ability of a lightweight model with the transferred knowledge from the teacher.
Approach:	They propose two methods to improve attention recovery of quantized large Transformers by combining attention-map and attention-output losses.
Outcome:	The proposed methods achieve state-of-the-art accuracy for quantized large Transformer encoder models with sub-2-bit weight quantization.

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Yujia Qin, Cheng Qian, Jing Yi, Weize Chen, Yankai Lin, Xu Han, Zhiyuan Liu, Maosong Sun, Jie Zhou

Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks (2022.emnlp-main)

Challenge:	Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, starting from which high-performance minima could be found.
Approach:	They investigate the geometric connections of different minima through the lens of mode connectivity, which measures whether two minima can be connected with a low-loss path.
Outcome:	The proposed model can be used to find low-loss paths between two minima, and to understand how their mode connectivity affects their task knowledge.

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Jaehoon Oh, Jongwoo Ko, Se-Young Yun

Increasing Visual Awareness in Multimodal Neural Machine Translation from an Information Theoretic Perspective (2022.emnlp-main)

Challenge:	Recent work has shown promising transferability of pre-trained multilingual language models.
Approach:	They propose a cross-lingual fine-tuning algorithm that uses SupCon and MixUp to combine them to improve performance.
Outcome:	The proposed algorithm improves cross-lingual transferability by using SupCon and MixUp.

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Baijun Ji, Tong Zhang, Yicheng Zou, Bojie Hu, Si Shen

Improving Event Coreference Resolution Using Document-level and Topic-level Information (2022.emnlp-main)

Challenge:	Existing studies focus on extracting multi-granularity visual features for integration or designing model architectures for better message passing across various modalities.
Approach:	They propose to decompose the informative visual signals into two parts: source-specific information and target-specific info.
Outcome:	The proposed method can enhance the visual awareness of MMT models against strong baselines.

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Sheng Xu, Peifeng Li, Qiaoming Zhu

Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding (2022.emnlp-main)

Challenge:	Experimental results show that our model outperforms the SOTA baselines due to the encoding length limitation.
Approach:	They propose a longformer-based encoder and an encoder with a trigger-mask mechanism to learn sentence-level embeddings based on local context.
Outcome:	The proposed model outperforms the baselines on the KBP 2017 dataset.

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Rishabh Bhardwaj, Amrita Saha, Steven C.H. Hoi, Soujanya Poria

Boosting Natural Language Generation from Instructions with Meta-Learning (2022.emnlp-main)

Challenge:	Prompt Tuning has been successful as a parameter-efficient method of conditioning large-scale pre-trained language models to perform downstream tasks.
Approach:	They propose to use a vector-quantized input-contextualized prompt as an extension to the soft prompt tuning framework to learn contextualization of soft prompt tokens.
Outcome:	The proposed prompt outperforms soft prompt tuning by an average margin of 1.19% on various language understanding tasks like SuperGLUE, QA, Relation classification, NER and NLI.

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Budhaditya Deb, Ahmed Hassan Awadallah, Guoqing Zheng

Topical Segmentation of Spoken Narratives: A Test Case on Holocaust Survivor Testimonies (2022.emnlp-main)

Challenge:	Recent work shows that language models trained with multi-task instructional learning (MTIL) can solve diverse NLP tasks in zero-shot settings with improved performance compared to prompt tuning.
Approach:	They propose to adapt meta-learning to MTIL in three directions: 1) Model Agnostic Meta Learning (MAML), 2) Hyper-Network adaptation to generate task specific parameters conditioned on instructions.
Outcome:	The proposed approaches improve over strong baselines in zero-shot settings and are most impactful when the test tasks are strictly zero- shot and are "hard"

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Eitan Wagner, Renana Keydar, Amit Pinchevski, Omri Abend

Unifying the Convergences in Multilingual Neural Machine Translation (2022.emnlp-main)

Challenge:	Topical segmentation is a task that has been neglected in recent work . a drawback of this approach is the lack of interpretability, which is crucial in some contexts.
Approach:	They propose to model running (spoken) narratives using topic segmentation . they hypothesize that boundary points between segments correspond to low mutual information .
Outcome:	The proposed approaches show significant improvements over manual approaches.

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Yichong Huang, Xiaocheng Feng, Xinwei Geng, Bing Qin

Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field (2022.emnlp-main)

Challenge:	Existing approaches to multilingual neural machine translation are overfitting and inconsistency is ignored .
Approach:	They propose a training strategy that picks up language-specific best checkpoints for each language pair to teach the current model on the fly.
Outcome:	The proposed training strategy alleviates convergence inconsistency and achieves state-of-the-art on language pairs.

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Chengyue Jiang, Yong Jiang, Weiqi Wu, Pengjun Xie, Kewei Tu

Help me write a Poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing (2022.emnlp-main)

Challenge:	Entity typing assigns semantic types to entities mentioned in text.
Approach:	They propose to use an undirected graphical model to formulate the UFET problem by combining unary potentials with a pairwise conditional random field model.
Outcome:	The proposed model outperforms the existing model with little cost and is thousands of times faster than the existing neural network module.

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Tuhin Chakrabarty, Vishakh Padmakumar, He He

Open Relation and Event Type Discovery with Type Abstraction (2022.emnlp-main)

Challenge:	Recent work in training large language models to follow natural language instructions has opened up exciting opportunities for natural language interface design.
Approach:	They propose to train large language models to follow natural language instructions and to test whether LLMs improve the quality of the generated content.
Outcome:	The proposed system is competitive to publicly available LLMs trained on instructions and can satisfy unseen compositional instructions.

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Sha Li, Heng Ji, Jiawei Han

Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples (2022.emnlp-main)

Challenge:	Conventional "closed-world" information extraction methods rely on human ontologies to define scope for extraction.
Approach:	They propose a type abstraction approach where models are prompted to generalize and name the type . they use the similarity between inferred names to induce clusters .
Outcome:	The proposed method is complementary to token representations on relation extraction and event extraction datasets.

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Linlin Liu, Xin Li, Ruidan He, Lidong Bing, Shafiq Joty, Luo Si

Revisiting Grammatical Error Correction Evaluation and Beyond (2022.emnlp-main)

Challenge:	Existing methods for language model pretraining use limited knowledge graph data for knowledge-intensive tasks.
Approach:	They propose to make better use of multilingual annotations and language agnostic properties of KG triples for pretraining LMs.
Outcome:	The proposed models show significant performance improvements on a wide range of knowledge-intensive cross-lingual tasks.

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Peiyuan Gong, Xuebo Liu, Heyan Huang, Min Zhang

R2D2: Robust Data-to-Text with Replacement Detection (2022.emnlp-main)

Challenge:	Pretraining-based (PT) evaluation metrics are not effective for training grammatical error correction systems.
Approach:	They propose a pretraining-based GEC evaluation metric which only uses PT-based metrics to score the corrected parts of the system.
Outcome:	The proposed evaluation metric outperforms existing methods on a CoNLL14 evaluation task.

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Linyong Nan, Lorenzo Jaime Flores, Yilun Zhao, Yixin Liu, Luke Benson, Weijin Zou, Dragomir Radev

IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension (2022.emnlp-main)

Challenge:	Existing methods to mitigate unfaithful text generation are inadequate . data-to-text generation requires a structured input format .
Approach:	They propose a training framework that addresses unfaithful Data-to-Text generation by training a system as a generator and faithfulness discriminator with additional replacement detection and unlikelihood learning tasks.
Outcome:	The proposed training framework improves FeTaQA, LogicNLG, and ToTTo fidelity on D2T systems.

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Rifki Afina Putri, Alice Oh

Machine Reading Comprehension

Challenge:	Existing MRC datasets in Indonesian are inadequate because of the small size and limited question types.
Approach:	They propose to combine automatic and manual unanswerable question generation to minimize the cost of manual dataset construction while maintaining the dataset quality.
Outcome:	The proposed dataset significantly improves the performance of Indonesian MRC models, showing a large improvement for unanswerable questions.

XLM-D: Decorate Cross-lingual Pre-training Model as Non-Autoregressive Neural Machine Translation (2022.emnlp-main)

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Yong Wang, Shilin He, Guanhua Chen, Yun Chen, Daxin Jiang

Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction (2022.emnlp-main)

Challenge:	Existing pre-training language models have been successful in natural language understanding and autoregressive generation tasks, but non-autoregressive models have not been sufficiently successful.
Approach:	They propose a pre-trained masked language model (MLM) and a non-autoregressive generation model with a lightweight decorator.
Outcome:	The proposed model outperforms the previous mask-predict model on translation datasets by 19.9x.

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Qin Dai, Benjamin Heinzerling, Kentaro Inui

Assist Non-native Viewers: Multimodal Cross-Lingual Summarization for How2 Videos (2022.emnlp-main)

Challenge:	Existing bi-encoder architectures do not allow any sharing between text and knowledge graphs . john sutter: experimental results show that enabling full interaction yields strong improvements.
Approach:	They propose cross-stitch bi-encoders that allow full interaction between text and KG . they say the amount of sharing is dynamically controlled via cross-attention-based gates .
Outcome:	Experimental results show that bi-encoder architectures yield strong improvements . cross-stitch mechanism allows sharing and updating representations between two encoders .

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Nayu Liu, Kaiwen Wei, Xian Sun, Hongfeng Yu, Fanglong Yao, Li Jin, Guo Zhi, Guangluan Xu

PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance (2022.emnlp-main)

Challenge:	Existing multimodal summarization methods are limited to monolingual videos . a proposed task aims to generate cross-lingual summaries from multimodal inputs .
Approach:	They propose a task to generate cross-lingual summaries from multimodal inputs of videos . they propose fusion network that integrates multimodal and cross-linguistic information .
Outcome:	The proposed task outperforms existing methods on a reorganized How2 dataset on the reorganized How2 data set.

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Yang Deng, Wenqiang Lei, Wenxuan Zhang, Wai Lam, Tat-Seng Chua

Generative Data Augmentation with Contrastive Learning for Zero-Shot Stance Detection (2022.emnlp-main)

Challenge:	Existing studies on financial question answering systems focus on passively responding to user queries.
Approach:	They propose a new dataset to facilitate conversational question answering over hybrid contexts in finance . they propose PACIFIC to combine clarification question generation and CQA .
Outcome:	The proposed method performs multi-task learning over all sub-tasks in PACIFIC . it incorporates a simple ensemble strategy to alleviate error propagation issue .

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Yang Li, Jiawei Yuan

Better Few-Shot Relation Extraction with Label Prompt Dropout (2022.emnlp-main)

Challenge:	Existing methods for zero-shot stance detection are labor-intensive to train for each new target.
Approach:	They propose a generative data augmentation approach to generate training samples containing unseen and seen targets and map them into the same embedding space with contrastive learning.
Outcome:	The proposed model achieves state-of-the-art on most topics in the task of zero-shot stance detection.

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Peiyuan Zhang, Wei Lu

Break it Down into BTS: Basic, Tiniest Subword Units for Korean (2022.emnlp-main)

Challenge:	Existing studies assume textual labels are always present during learning and prediction.
Approach:	They propose a method which randomly drops out textual labels in the learning process.
Outcome:	The proposed approach improves the few-shot relation extraction task by randomly dropping out labels in the learning process.

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Nayeon Kim, Jun-Hyung Park, Joon-Young Choi, Eojin Jeon, Youjin Kang, SangKeun Lee

The Devil in Linear Transformer (2022.emnlp-main)

Challenge:	Existing word embeddings for Korean use the internal structure of words with subword information to improve the quality of word representations.
Approach:	They introduce Basic, Tiniest Subword (BTS) units for Korean language that are inspired by Hangeul, the Korean writing system.
Outcome:	The proposed framework outperforms the state-of-the-art Korean word embedding by 11.8% on all intrinsic and extrinsic tasks.

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Zhen Qin, Xiaodong Han, Weixuan Sun, Dongxu Li, Lingpeng Kong, Nick Barnes, Yiran Zhong

Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective (2022.emnlp-main)

Challenge:	Existing linear transformers suffer from performance degradations on various tasks and corpus.
Approach:	They propose a new linear attention that replaces scaling with a normalization to stabilize gradients and confine attention to neighbouring tokens in early layers.
Outcome:	The proposed model outperforms vanilla transformers on the long-range arena benchmark while being significantly more space-time efficient.

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Ping Yang, Junjie Wang, Ruyi Gan, Xinyu Zhu, Lin Zhang, Ziwei Wu, Xinyu Gao, Jiaxing Zhang, Tetsuya Sakai

Hypoformer: Hybrid Decomposition Transformer for Edge-friendly Neural Machine Translation (2022.emnlp-main)

Challenge:	Existing approaches to zero-shot learning are format-agnostic and can address new learning tasks without additional training.
Approach:	They propose a new paradigm for zero-shot learning that is format agnostic and compatible with any format and applicable to a list of language tasks.
Outcome:	The proposed model shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as text classification and commonsense reasoning.

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Sunzhu Li, Peng Zhang, Guobing Gan, Xiuqing Lv, Benyou Wang, Junqiu Wei, Xin Jiang

FigMemes: A Dataset for Figurative Language Identification in Politically-Opinionated Memes (2022.emnlp-main)

Challenge:	Existing methods to compress Transformer are limited to sub-components, e.g., selfattention networks or embedding layer.
Approach:	They propose a Hybrid Tensor-Train decomposition which retains full rank and meanwhile reduces operations and parameters.
Outcome:	The proposed model outperforms light-weight SOTA methods on three translation tasks and achieves 7.1 points absolute improvement in BLEU and 1.27 X speedup on IWSLT’14 De-En task.

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Chen Liu, Gregor Geigle, Robin Krebs, Iryna Gurevych

UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction (2022.emnlp-main)

Challenge:	FigMemes is a dataset for figurative language classification in politically-opinionated memes.
Approach:	They propose to use figurative language classification to identify politically-opinionated memes by analyzing their datasets and comparing them to other machine learning models.
Outcome:	The proposed dataset includes annotations of six commonly used types of figurative language in politically-opinionated memes and a wide range of topics and visual styles.

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Wei Tang, Benfeng Xu, Yuyue Zhao, Zhendong Mao, Yifeng Liu, Yong Liao, Haiyong Xie

X-FACTOR: A Cross-metric Evaluation of Factual Correctness in Abstractive Summarization (2022.emnlp-main)

Challenge:	Existing approaches to extract rich correlations between entities and relations are not fully exploited by existing methods.
Approach:	They propose to unify entities and relations by jointly encoding them within a concatenated natural language sequence and unify the modeling of interactions with a proposed Interaction Map.
Outcome:	The proposed method is more efficient and efficient than existing methods and can be scaled up to 2021.

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Subhajit Chaudhury, Sarathkrishna Swaminathan, Chulaka Gunasekara, Maxwell Crouse, Srinivas Ravishankar, Daiki Kimura, Keerthiram Murugesan, Ramón Fernandez Astudillo, Tahira Naseem, Pavan Kapanipathi, Alexander Gray

ParaTag: A Dataset of Paraphrase Tagging for Fine-Grained Labels, NLG Evaluation, and Data Augmentation (2022.emnlp-main)

Challenge:	Abstractive summarization models produce factually inconsistent summaries that are not supported by the original article.
Approach:	They propose a fact-aware filtering mechanism that improves the factuality of abstractive summarization models.
Outcome:	The proposed method improves the quality of training data and the factuality of generated summaries.

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Shuohang Wang, Ruochen Xu, Yang Liu, Chenguang Zhu, Michael Zeng

Factual Accuracy is not Enough: Planning Consistent Description Order for Radiology Report Generation (2022.emnlp-main)

Challenge:	Existing datasets only annotate a binary label for each sentence pair. Existing models only annnotate binary labels for each phrase pair.
Approach:	They propose a novel binary paraphrase classification task that annotates the degree of paraphrase between sentences and a new annotation schema that labels the minimum spans of tokens in a sentence that don't have the corresponding paraphrases in the other sentence.
Outcome:	The proposed dataset can be used to train an automatic scorer for language generation evaluation.

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Toru Nishino, Yasuhide Miura, Tomoki Taniguchi, Tomoko Ohkuma, Yuki Suzuki, Shoji Kido, Noriyuki Tomiyama

FLUTE: Figurative Language Understanding through Textual Explanations (2022.emnlp-main)

Challenge:	Radiology report generation systems can reduce the workload of radiologists by automatically describing the findings in medical images.
Approach:	They propose a planning-based radiology report generation system that generates the overall structure of reports as “plans” prior to generating reports that are accurate and consistent in order.
Outcome:	The proposed system improves the content order score by 5.1 pt in time series critical scenarios and the clinical factual accuracy F-score by 9.1 p.t. in time-series irrelevant scenarios.

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Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, Smaranda Muresan

Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation (2022.emnlp-main)

Challenge:	Figurative language understanding is a recognizing textual entailment task, but lacks data for figurative language.
Approach:	They propose to use a dataset to analyze figurative NLI instances with explanations to improve models' performance.
Outcome:	The proposed dataset can scale up models even for figurative language using human annotations.

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Wenhao Wu, Wei Li, Jiachen Liu, Xinyan Xiao, Sujian Li, Yajuan Lyu

RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees (2022.emnlp-main)

Challenge:	Existing models for text generation are weak enough to handle perturbations in inputs, leading to degeneration in faithfulness and informativeness.
Approach:	They propose a framework for improving faithfulness and informativeness of Seq2Seq models by perturbing word representations and word swapping.
Outcome:	The proposed framework improves faithfulness and informativeness of Seq2Seq models under automatic and human evaluation settings.

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Tengxiao Liu, Qipeng Guo, Xiangkun Hu, Yue Zhang, Xipeng Qiu, Zheng Zhang

Let the CAT out of the bag: Contrastive Attributed explanations for Text (2022.emnlp-main)

Challenge:	Existing structured reasoning frameworks lack internal decision probability and cannot model the tree as a whole.
Approach:	They propose a Reinforcement Learning based Entailment Tree generation framework that is trained using the cumulative signals across the whole tree.
Outcome:	The proposed framework offers explicit deductions with entailment steps in a tree structure.

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Saneem Chemmengath, Amar Prakash Azad, Ronny Luss, Amit Dhurandhar

monoQA: Multi-Task Learning of Reranking and Answer Extraction for Open-Retrieval Conversational Question Answering (2022.emnlp-main)

Challenge:	XAI has seen an explosion of interest in explaining black box behavior . contrastive/counterfactual explanations have seen a surge of interest recently .
Approach:	They propose a method which provides contrastive explanations for natural language text data with a novel twist by exploiting attribute classifiers.
Outcome:	The proposed method outperforms state-of-the-art methods on four benchmark metrics.

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Sarawoot Kongyoung, Craig Macdonald, Iadh Ounis

Composing Ci with Reinforced Non-autoregressive Text Generation (2022.emnlp-main)

Challenge:	Existing approaches to the Conversational Question Answering task have used multi-task learning to solve the task.
Approach:	They propose to use multi-task learning to improve the ORConvQA task by sharing the reranker and reader’s learned structure in a generative model.
Outcome:	The proposed model outperforms baseline models on the OR-QuAC and OR-CoQA datasets and significantly outperformed existing strong baseline models.

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Yan Song

MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning (2022.emnlp-main)

Challenge:	Existing approaches to compose Ci are limited in handling the constraints of tune patterns . authors propose a non-autoregressive approach to generate Ci using a synchronous process .
Approach:	They propose to compose Ci using a non-autoregressive approach that takes into account rigid formats . they propose to apply reinforcement learning to the generation process with rigid constraints .
Outcome:	The proposed method outperforms baselines and previous studies on a Ci dataset . it allows the model to perform synchronous generation while maintaining the format and content requirement.

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Yuwei Xia, Mengqi Zhang, Qiang Liu, Shu Wu, Xiao-Yu Zhang

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)

Challenge:	Existing models rely on historical information to learn embeddings for entities, but ignore the evolution of facts.
Approach:	They propose a Temporal Meta-learning framework to learn evolutionary meta-knowledge from TKGs.
Outcome:	The proposed method improves on four widely-used datasets and three backbones on a wide range of scenarios on tKGs.

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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si

Q-TOD: A Query-driven Task-oriented Dialogue System (2022.emnlp-main)

Challenge:	Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment.
Approach:	They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives.
Outcome:	The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering.

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Xin Tian, Yingzhan Lin, Mengfei Song, Siqi Bao, Fan Wang, Huang He, Shuqi Sun, Hua Wu

Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings (2022.emnlp-main)

Challenge:	Existing pipelined task-oriented dialogue systems have difficulties adapting to unseen domains . end-to-end systems are plagued by large-scale knowledge bases in practice .
Approach:	They propose a query-driven task-oriented dialogue system that extracts dialogue context information into a natural language query.
Outcome:	The proposed system outperforms strong baselines and establishes a new state-of-the-art performance on three publicly available task-oriented dialogue datasets.

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Che Liu, Rui Wang, Junfeng Jiang, Yongbin Li, Fei Huang

WR-One2Set: Towards Well-Calibrated Keyphrase Generation (2022.emnlp-main)

Challenge:	Dialogue embeddings are a critical prerequisite for semantically understanding dialogues.
Approach:	They propose a self-guided contrastive learning approach called dial2vec that captures interaction patterns between interlocutors and leverages them to guide the learning of the embeddings corresponding to each interlocuter.
Outcome:	The proposed approach achieves 8.7, 9.0, and 13.8 points absolute improvements over the strongest baseline on the three evaluation tasks respectively.

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Binbin Xie, Xiangpeng Wei, Baosong Yang, Huan Lin, Jun Xie, Xiaoli Wang, Min Zhang, Jinsong Su

Eeny, meeny, miny, moe. How to choose data for morphological inflection. (2022.emnlp-main)

Challenge:	Experimental results show that keyphrase generation has serious calibration errors . ONE2SET generates short phrases summarizing an input document .
Approach:	They propose a paradigm for keyphrase generation that generates short phrases summarizing an input document.
Outcome:	The proposed model over-estimates tokens and makes it well-calibrated on common datasets.

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Saliha Muradoglu, Mans Hulden

Morphology

Challenge:	Data scarcity is a major bottleneck for many natural language processing tasks . active learning aims to reduce the cost of data annotation by selecting the most informative examples to label.
Approach:	They propose to use oracle experiments to select data that is most informative for the model.
Outcome:	The proposed sampling strategies show that they improve on the oracle experiment and the 10-cycle iteration using Natügu as a case study.

An Adaptive Logical Rule Embedding Model for Inductive Reasoning over Temporal Knowledge Graphs (2022.emnlp-main)

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Xin Mei, Libin Yang, Xiaoyan Cai, Zuowei Jiang

UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning (2022.emnlp-main)

Challenge:	Existing methods for temporal knowledge graphs (TKGs) are incomplete and therefore lack interpretability.
Approach:	They propose an interpretable temporal knowledge graph reasoning model that captures deep causal logic by learning rule embeddings.
Outcome:	The proposed model outperforms state-of-the-art models on the ICEWS14, ICEW0515 and ICEw18 datasets.

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Yutao Mou, Pei Wang, Keqing He, Yanan Wu, Jingang Wang, Wei Wu, Weiran Xu

Open-domain Video Commentary Generation (2022.emnlp-main)

Challenge:	Existing methods to detect out-of-domain (OOD) intents ignore alignment between representation learning and scoring function, limiting performance.
Approach:	They propose a unified neighborhood learning framework to detect OOD intents . they propose to align representation learning with scoring function .
Outcome:	The proposed method is able to detect out-of-domain (OOD) intents from user queries.

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Edison Marrese-Taylor, Yumi Hamazono, Tatsuya Ishigaki, Goran Topić, Yusuke Miyao, Ichiro Kobayashi, Hiroya Takamura

One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks (2022.emnlp-main)

Challenge:	Existing approaches to generate live commentary on specific domains have been limited.
Approach:	They propose to generate live commentary from transcribed videos in an open-domain setting . they propose to use well-known neural architectures to build models based on transcriptions .
Outcome:	The proposed model is based on well-known neural architectures and based off existing models.

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Manuel Senge, Timour Igamberdiev, Ivan Habernal

Counterfactual Recipe Generation: Exploring Compositional Generalization in a Realistic Scenario (2022.emnlp-main)

Challenge:	Existing research on the efficiency of differentially-private stochastic gradient descent (DP-SGD) in NLP is inconclusive or even counter-intuitive.
Approach:	They propose to use differentially-private stochastic gradient descent (DP-SGD) to preserve privacy in NLP by using modern neural models based on BERT and XtremeDistil architectures to conduct extensive experiments.
Outcome:	The proposed models and training strategies provide the best trade-off between privacy and performance on different NLP tasks.

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Xiao Liu, Yansong Feng, Jizhi Tang, Chengang Hu, Dongyan Zhao

Tutoring Helps Students Learn Better: Improving Knowledge Distillation for BERT with Tutor Network (2022.emnlp-main)

Challenge:	Existing models fail to learn and use culinary knowledge in a compositional way, argues a new study.
Approach:	They propose a task that asks models to modify a base recipe according to the change of an ingredient.
Outcome:	The proposed model can perform compositional generalization in a realistic setting . existing models have difficulties in modifying ingredients while preserving original style .

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Junho Kim, Jun-Hyung Park, Mingyu Lee, Wing-Lam Mok, Joon-Young Choi, SangKeun Lee

Does Corpus Quality Really Matter for Low-Resource Languages? (2022.emnlp-main)

Challenge:	Existing knowledge distillation approaches for language models have overlooked the difficulty of training examples.
Approach:	They propose a framework that controls difficulty of training examples during pre-training by a tutor network.
Outcome:	The proposed framework outperforms state-of-the-art KD methods with student models on the GLUE benchmark.

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Mikel Artetxe, Itziar Aldabe, Rodrigo Agerri, Olatz Perez-de-Viñaspre, Aitor Soroa

Unifying Data Perspectivism and Personalization: An Application to Social Norms (2022.emnlp-main)

Challenge:	Existing work on multilingual pre-training has relied on automatically filtered versions of CommonCrawl.
Approach:	They propose to use tailored crawling to identify and scrape websites with high-quality content to improve representation learning in Basque.
Outcome:	The proposed corpus, called EusCrawl, has a much higher quality according to native annotators than the Basque portion of popular multilingual corpora like CC100 and mC4.

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Joan Plepi, Béla Neuendorf, Lucie Flek, Charles Welch

Does Self-Rationalization Improve Robustness to Spurious Correlations? (2022.emnlp-main)

Challenge:	Obtaining a single ground truth is not possible or necessary for subjective tasks.
Approach:	They propose a set of personalization methods to model annotators and compare their effectiveness for predicting social norms.
Outcome:	The proposed model outperforms existing models and compares performance across subsets of social situations that vary by the closeness of the relationship between parties in conflict.

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Alexis Ross, Matthew Peters, Ana Marasovic

Efficient Pre-training of Masked Language Model via Concept-based Curriculum Masking (2022.emnlp-main)

Challenge:	Rationalization is fundamental to human reasoning and learning.
Approach:	They evaluate robustness to spurious correlations in encoder-decoder and decoder-only models . authors say explanations can come at the cost of robustness .
Outcome:	The proposed model outputs are more interpretable and easier to interact with for end-users than nonrationalizing models.

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Mingyu Lee, Jun-Hyung Park, Junho Kim, Kang-Min Kim, SangKeun Lee

Subword Evenness (SuE) as a Predictor of Cross-lingual Transfer to Low-resource Languages (2022.emnlp-main)

Challenge:	Masked language modeling (MLM) has been widely used for pre-training effective bidirectional representations but comes at a substantial training cost.
Approach:	They propose a concept-based curriculum masking method that evaluates the MLM difficulty of each token based on a carefully-designed linguistic difficulty criterion.
Outcome:	The proposed method significantly improves pre-training efficiency with the original BERT model at half the training cost.

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Olga Pelloni, Anastassia Shaitarova, Tanja Samardzic

A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss (2022.emnlp-main)

Challenge:	English is the most natural choice for cross-lingual transfer, but it is often not the best choice for low-resource languages.
Approach:	They propose to use pre-trained multilingual models to improve performance in low-resource languages via cross-lingual transfer.
Outcome:	The results show that languages written in non-Latin and non-alphabetic scripts are the best choices for improving performance on Masked Language Modelling tasks in a diverse set of 30 low-resource languages.

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Wenbiao Li, Wang Ziyang, Yunfang Wu

Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis (2022.emnlp-main)

Challenge:	Traditional readability assessment models employ hundreds of linguistic features, but it is less explored for readability assessments.
Approach:	They propose a BERT-based model with feature projection and length-balanced loss to determine the difficulty level of a given text.
Outcome:	The proposed model achieves significant improvements over baseline models on three English benchmark datasets and one Chinese dataset.

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Zhihao Du, ShiLiang Zhang, Siqi Zheng, Zhi-Jie Yan

GREENER: Graph Neural Networks for News Media Profiling (2022.emnlp-main)

Challenge:	Existing approaches to speaker diarization treat speaker dependency and overlaps as multi-label classification problems.
Approach:	They propose to reformulate overlapped speaker diarization task as a single-label prediction problem via power set encoding (PSE) to overcome the disadvantages, they propose a speaker overlap-aware neural diarisation model which incorporates a context-independent scorer and a contextual-dependent score.
Outcome:	The proposed model outperforms the state-of-the-art methods on speaker voice activity detection and improves relative diarization error reduction by 6.30%.

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Panayot Panayotov, Utsav Shukla, Husrev Taha Sencar, Mohamed Nabeel, Preslav Nakov

Graph Hawkes Transformer for Extrapolated Reasoning on Temporal Knowledge Graphs (2022.emnlp-main)

Challenge:	a new method for profiling news media on the Web addresses the factuality of reporting and bias problem . a recent study has focused on text features but has focused primarily on text .
Approach:	They propose a model that models the similarity between media outlets based on their audience overlap . they propose GREENER, which builds a graph of inter-media connections based upon audience overlap.
Outcome:	The proposed model improves on state-of-the-art models on two datasets.

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Haohai Sun, Shangyi Geng, Jialun Zhong, Han Hu, Kun He

UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation (2022.emnlp-main)

Challenge:	Existing methods for entity prediction cannot predict when an event will occur . there are many facts not related to the query that can confuse the model .
Approach:	They propose a temporal knowledge Graph reasoning model based on Graph Hawkes Transformer . the model captures instantaneous structural and temporal evolution information .
Outcome:	The proposed model performs much better under long-term evolution scenarios.

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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Youzheng Wu, Xiaodong He, Tiejun Zhao

Don’t Prompt, Search! Mining-based Zero-Shot Learning with Language Models (2022.emnlp-main)

Challenge:	Existing methods for question answering using knowledge resources are mixed-of-experts and semantic parsing-based.
Approach:	They propose a semantic-parsing-based approach to perform Unified discrete Reasoning over heterogeneous knowledge resources as Program Generation.
Outcome:	The proposed approach improves interpretability and scalability over table and text . it achieves promising performance on the TAT-QA dataset without annotation .

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Mozes van de Kar, Mengzhou Xia, Danqi Chen, Mikel Artetxe

SEMGraph: Incorporating Sentiment Knowledge and Eye Movement into Graph Model for Sentiment Analysis (2022.emnlp-main)

Challenge:	Recent work has obtained strong zero-shot results by prompting language models.
Approach:	They propose a mining-based approach that uses regular expressions to mine labeled examples from unlabeled corpora and fine tune a pretrained model.
Outcome:	The proposed method outperforms prompting on a wide range of tasks when using comparable templates.

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Bingbing Wang, Bin Liang, Jiachen Du, Min Yang, Ruifeng Xu

Cross-lingual neural fuzzy matching for exploiting target-language monolingual corpora in computer-aided translation (2022.emnlp-main)

Challenge:	Existing research on sentiment analysis based on eye movement signals has been attributed importance.
Approach:	They propose a linguistic probing eye movement paradigm to extract eye movement features based on the relationship between linguistic features and human reading behavior.
Outcome:	The proposed graph architecture achieves state-of-the-art performance on two sentiment analysis datasets with eye movement signals and three sentiment analysis data without eye movement signal.

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Miquel Esplà-Gomis, Víctor M. Sánchez-Cartagena, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez

Multi-Label Intent Detection via Contrastive Task Specialization of Sentence Encoders (2022.emnlp-main)

Challenge:	CAT tools based on translation memories (TMs) are limited in their use for a number of translation tasks due to the limited availability of in-domain TMs.
Approach:	They propose a neural approach to exploit in-domain TMs and in-target-language (TL) monolingual corpora to exploit CAT tools.
Outcome:	The proposed approach exploits in-domain TMs and in-target-language (TL) monolingual corpora and increases translation proposals on four language pairs.

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Ivan Vulić, Iñigo Casanueva, Georgios Spithourakis, Avishek Mondal, Tsung-Hsien Wen, Paweł Budzianowski

Discovering Language-neutral Sub-networks in Multilingual Language Models (2022.emnlp-main)

Challenge:	a novel framework for task-oriented dialog ToD systems is proposed . a task of recognizing the user's intent or goal from their utterance is a crucial component of any TOD system.
Approach:	They propose to transform general-purpose sentences into task-specialized SEs by contrastive fine-tuning on annotated multi-label data.
Outcome:	The proposed framework yields effective mID models with large gains over non-specialized models across a spectrum of different m ID datasets.

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Negar Foroutan, Mohammadreza Banaei, Rémi Lebret, Antoine Bosselut, Karl Aberer

Parameter-Efficient Tuning Makes a Good Classification Head (2022.emnlp-main)

Challenge:	a recent study shows that multilingual pre-trained language models transfer well on cross-lingual downstream tasks.
Approach:	They conceptualize language neutrality as a function of overlap between language-encoding sub-networks of multilingual models.
Outcome:	The proposed model performs well on cross-lingual tasks despite being pre-trained on multiple languages .

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Zhuoyi Yang, Ming Ding, Yanhui Guo, Qingsong Lv, Jie Tang

STGN: an Implicit Regularization Method for Learning with Noisy Labels in Natural Language Processing (2022.emnlp-main)

Challenge:	In recent years, pretrained models revolutionized the paradigm of natural language understanding . but the final-layer output of the backbone, i.e. the input of the classification head, will change greatly during finetuning .
Approach:	They propose to append a randomly initialized classification head after the pretrained backbone and finetune the whole model.
Outcome:	The proposed classification head can be replaced with the randomly initialized heads for a stable performance gain.

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Tingting Wu, Xiao Ding, Minji Tang, Hao Zhang, Bing Qin, Ting Liu

Hongkuan Zhang, Saku Sugawara, Akiko Aizawa, Lei Zhou, Ryohei Sasano, Koichi Takeda

Challenge:	Existing work to mitigate the effect of noisy labels is limited to specific tasks or training procedures, making it hard to be widely used.
Approach:	They propose a stochastic tailor-made gradient noise to mitigate the effect of noisy labels by introducing benign noise into stochistic gradient descent.
Outcome:	The proposed method can be used to discriminate correct samples from incorrect ones and boost existing training methods.

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Debiasing Masks: A New Framework for Shortcut Mitigation in NLU (2022.emnlp-main)

Challenge:	Existing image captioning approaches treat image-caption pairs indistinctly without considering the differences in their learning difficulties.
Approach:	They propose a pretrained vision–language model that measures cross-modal similarity and a model that uses cross-module similarity to measure the difficulty of captioning.
Outcome:	The proposed model achieves superior performance and competitive convergence speed to baselines without incurring additional training costs.

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Johannes Mario Meissner, Saku Sugawara, Akiko Aizawa

Extending Phrase Grounding with Pronouns in Visual Dialogues (2022.emnlp-main)

Challenge:	Debiasing language models from unwanted behaviors in natural language understanding datasets is a topic with increasing interest in the NLP community.
Approach:	They propose a method to debiase language models from unwanted behaviors in NLU tasks by identifying pruning masks that can be applied to a finetuned model.
Outcome:	The proposed method shows superior performance and performance over standard methods.

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Panzhong Lu, Xin Zhang, Meishan Zhang, Min Zhang

EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain (2022.emnlp-main)

Challenge:	Conventional phrase grounding aims to localize noun phrases mentioned in a caption to their corresponding image regions.
Approach:	They extend the task by considering pronouns to include noun phrases and pronounos . they construct a dataset of phrase grounding with noun and pronom phrases to image regions .
Outcome:	Experiments show that pronouns are easier to ground than noun phrases . a baseline model with coreference information can significantly boost the grounding performance .

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Dennis Aumiller, Ashish Chouhan, Michael Gertz

Differentiable Data Augmentation for Contrastive Sentence Representation Learning (2022.emnlp-main)

Challenge:	Existing summarization datasets focus on overly exposed domains and are primarily monolingual with few multilingual datasets.
Approach:	They propose a new summarization dataset based on manually curated document summaries from the European Union law platform EUR-Lex.
Outcome:	The proposed dataset is based on document summaries of legal acts from the European Union law platform (EUR-Lex).

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Tianduo Wang, Wei Lu

Text Style Transferring via Adversarial Masking and Styled Filling (2022.emnlp-main)

Challenge:	a contrastive learning framework is used to fine-tune pre-trained language models with unlabeled sentences or labeled sentences.
Approach:	They propose a method that makes hard positives from unlabeled sentences . they use a prefix attached to a model to allow for differentiable data augmentation .
Outcome:	The proposed method yields significant improvements over existing methods under semi-supervised and supervised settings.

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Jiarui Wang, Richong Zhang, Junfan Chen, Jaein Kim, Yongyi Mao

Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution (2022.emnlp-main)

Challenge:	Existing models for text style transfer suffer from two challenges: the word masking procedure may mistakenly remove unexpected words and the selected words in the word filling procedure lack diversity and semantic consistency.
Approach:	They propose a style transfer model with adversarial masking and styled filling techniques to solve these challenges.
Outcome:	The proposed model performs well on two benchmark text style transfer data sets.

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Aiwei Liu, Honghai Yu, Xuming Hu, Shu’ang Li, Li Lin, Fukun Ma, Yawen Yang, Lijie Wen

Query-based Instance Discrimination Network for Relational Triple Extraction (2022.emnlp-main)

Challenge:	Existing methods to attack transformer models are not effective at character level, but they are a natural attack scenario.
Approach:	They propose a character-level adversarial attack method against transformer models . they use a gradient-based method to find the most vulnerable words in a sentence .
Outcome:	The proposed method outperforms previous methods on sentence-level and token-level tasks.

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Zeqi Tan, Yongliang Shen, Xuming Hu, Wenqi Zhang, Xiaoxia Cheng, Weiming Lu, Yueting Zhuang

Learning Inter-Entity-Interaction for Few-Shot Knowledge Graph Completion (2022.emnlp-main)

Challenge:	Recent approaches to extract relational triples from open domain texts suffer from error propagation, relation redundancy and lack of high-level connections.
Approach:	They propose a query-based approach to construct instance-level representations for relational triples . they use query embeddings and token embeddables to extract all types of triples in one step .
Outcome:	The proposed method achieves state-of-the-art on five widely used benchmarks.

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Yuling Li, Kui Yu, Xiaoling Huang, Yuhong Zhang

Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter (2022.emnlp-main)

Challenge:	Recent FKGC studies focus on learning semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities.
Approach:	They propose a model to learn semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities.
Outcome:	The proposed model outperforms state-of-the-art methods on two public datasets.

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Megha Sundriyal, Atharva Kulkarni, Vaibhav Pulastya, Md. Shad Akhtar, Tanmoy Chakraborty

ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (2022.emnlp-main)

Challenge:	Current vogue is to employ manual fact-checkers to efficiently classify and verify such data to combat this avalanche of misinformation and fake news.
Approach:	They propose a large-scale Twitter corpus with token-level claim spans on more than 7.5k tweets and a model that automatically detects and extracts the snippets of misinformation.
Outcome:	The proposed model outperforms baseline systems on several evaluation metrics, improving by 1.5 points.

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Jiaan Wang, Fandong Meng, Ziyao Lu, Duo Zheng, Zhixu Li, Jianfeng Qu, Jie Zhou

Max Müller-Eberstein, Rob van der Goot, Barbara Plank

Challenge:	Existing approaches to building cross-lingual summarization systems on dialogue documents are limited.
Approach:	They propose a benchmark dataset for building cross-lingual summarization systems on dialogue documents.
Outcome:	The proposed model outperforms pipeline models on ClidSum and mDialBART.

Spectral Probing (2022.emnlp-main)

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Challenge:	Contextualized embeddings capture linguistic phenomena at distinct layers and frequencies . current studies rely on handcrafted filters and are limited to English .
Approach:	They develop a fully learnable frequency filter to identify spectral profiles for any given task.
Outcome:	The proposed filter enables more granular analyses than handcrafted filters and improves efficiency.

QASem Parsing: Text-to-text Modeling of QA-based Semantics (2022.emnlp-main)

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Ayal Klein, Eran Hirsch, Ron Eliav, Valentina Pyatkin, Avi Caciularu, Ido Dagan

Keyphrase Generation via Soft and Hard Semantic Corrections (2022.emnlp-main)

Challenge:	Existing work suggests the appeals of incorporating explicit semantic representations into NLP . semi-structured natural language structures provide an intermediate meaning-capturing representation .
Approach:	They propose a semi-structured natural-language representation of textual information . they examine input and output linearization strategies and multitask learning .
Outcome:	The proposed model is based on pre-trained sequence-to-sequence language models . it is easy to use and can be used for downstream tasks that benefit from it .

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Guangzhen Zhao, Guoshun Yin, Peng Yang, Yu Yao

Modal-specific Pseudo Query Generation for Video Corpus Moment Retrieval (2022.emnlp-main)

Challenge:	Extensive experiments show that CorrKG is capable of generating high-quality keyphrases.
Approach:	They propose a correction model CorrKG on top of the MLE pipeline to correct the biases . the adaptive adaptive mass learning scheme is designed to better fit OT and FreqFS .
Outcome:	The proposed model overcomes the semantic biases in keyphrase generation using OT and FreqFS techniques.

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Minjoon Jung, SeongHo Choi, JooChan Kim, Jin-Hwa Kim, Byoung-Tak Zhang

DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models (2022.emnlp-main)

Challenge:	Existing studies have shown promising results in video corpus moment retrieval . however, they relied on the expensive query annotations for the VCMR .
Approach:	They propose a self-supervised learning framework to localize video corpus moment without annotations.
Outcome:	The proposed framework can localize the video corpus moment without any explicit annotation on TVR dataset.

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Hongyu Zhu, Yan Chen, Jing Yan, Jing Liu, Yu Hong, Ying Chen, Hua Wu, Haifeng Wang

DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages (2022.emnlp-main)

Challenge:	a comprehensive evaluation of QM models should be conducted on natural texts, not on artificial adversarial examples . ral models are often not robust to adversarials, which means they predict unexpected outputs .
Approach:	They use a Chinese dataset to evaluate the robustness of QM models . they show that the effect of artificial adversarial examples does not work on natural texts .
Outcome:	The proposed model is more robust than other models on natural questions with 32 linguistic perturbations.

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Gabriele Sarti, Arianna Bisazza, Ana Guerberof-Arenas, Antonio Toral

Bridging Fairness and Environmental Sustainability in Natural Language Processing (2022.emnlp-main)

Challenge:	Recent advances in neural language modeling and multilingual training have prompted widespread adoption of machine translation (MT) technologies across an unprecedented range of world languages.
Approach:	They propose to use a dataset to assess the impact of two state-of-the-art NMT systems, Google Translate and the multilingual mBART-50 model, on translation productivity.
Outcome:	The proposed model is faster than translation from scratch, but the magnitude of productivity gains varies widely across systems and languages.

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Marius Hessenthaler, Emma Strubell, Dirk Hovy, Anne Lauscher

UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition (2022.emnlp-main)

Challenge:	a lack of research on the interplay between fairness and environmental impact is a problem in natural language processing . fairness is prone to encode and amplify stereotypical social biases, according to several studies .
Approach:	They evaluate a technique to reduce energy consumption of English NLP models by knowledge distillation for its impact on fairness.
Outcome:	The proposed method reduces energy consumption and environmental impact of English NLP models.

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Guimin Hu, Ting-En Lin, Yi Zhao, Guangming Lu, Yuchuan Wu, Yongbin Li

Is the Brain Mechanism for Hierarchical Structure Building Universal Across Languages? An fMRI Study of Chinese and English (2022.emnlp-main)

Challenge:	Existing studies study sentiment and emotion separately and do not fully exploit the complementary knowledge behind the two.
Approach:	They propose a multimodal sentiment knowledge-sharing framework that unifies MSA and ERC tasks from features, labels, and models.
Outcome:	The proposed framework achieves consistent improvements on four public benchmark datasets on MOSI, MOSEI, MELD, and IEMOCAP.

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Xiaohan Zhang, Shaonan Wang, Nan Lin, Chengqing Zong

HashFormers: Towards Vocabulary-independent Pre-trained Transformers (2022.emnlp-main)

Challenge:	Existing studies have shown that the brain builds hierarchical syntactic structures, but it is unknown whether they are universal across languages.
Approach:	They analyze the working memory requirements when applying parsing strategies to two languages: Chinese and English.
Outcome:	The proposed method shows that the brain adopts parsing strategies with less memory load according to different language structures.

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Huiyin Xue, Nikolaos Aletras

MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering (2022.emnlp-main)

Challenge:	Existing pre-trained language models are vocabulary-dependent, mapping by default each token to its corresponding embedding.
Approach:	They propose a family of vocabulary-independent pre-trained transformers that support unlimited vocabulary . they propose to map each token to its corresponding embedding by default .
Outcome:	The proposed models are more memory efficient than existing models while achieving comparable performance on multiple text classification tasks.

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Jiaxin Wang, Lingling Zhang, Jun Liu, Xi Liang, Yujie Zhong, Yaqiang Wu

Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation (2022.emnlp-main)

Challenge:	Existing methods for open relation extraction (OpenRE) focus on labeled and pre-defined instances, which are costly to acquire in reality.
Approach:	They propose a framework that can extract relations without pre-defined types from open-domain corpus with efficient knowledge transfer from a few pre-determined relational instances.
Outcome:	The proposed framework achieves the new SOTA results for OpenRE on different datasets.

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Mengting Hu, Yike Wu, Hang Gao, Yinhao Bai, Shiwan Zhao

SocioProbe: What, When, and Where Language Models Learn about Sociodemographics (2022.emnlp-main)

Challenge:	Recent work on aspect sentiment quad prediction (ASQP) uses a template to extract aspect quadruplets from review sentences.
Approach:	They propose to use a pre-trained language model to select proper orders from a template order perspective to improve aspect sentiment quad prediction.
Outcome:	The proposed method outperforms state-of-the-art methods significantly in low-resource settings.

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Anne Lauscher, Federico Bianchi, Samuel R. Bowman, Dirk Hovy

When does Parameter-Efficient Transfer Learning Work for Machine Translation? (2022.emnlp-main)

Challenge:	Pre-trained language models have outperformed other models on a wide range of tasks . however, there is still little understanding of their knowledge of higher-level aspects of language .
Approach:	They investigate whether pre-trained language models have knowledge of sociodemographics . they use traditional probing techniques to probe the knowledge of single-GPU PLMs based on multiple English data sets .
Outcome:	The results show that pre-trained language models outperform other models on a wide range of tasks.

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Ahmet Üstün, Asa Cooper Stickland

Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer (2022.emnlp-main)

Challenge:	Prior work indicates that parametric fine-tuning methods may not work as well for machine translation (MT).
Approach:	They propose to use parameter-efficient fine-tuning methods to adapt large pre-trained models while only tuning a small number of parameters.
Outcome:	The proposed methods outperform full fine-tuning for many downstream tasks when the parameter budget corresponds to 10% of the model parameters.

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Ahmet Üstün, Arianna Bisazza, Gosse Bouma, Gertjan van Noord, Sebastian Ruder

Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems (2022.emnlp-main)

Challenge:	Existing multilingual models cannot fully leverage training data when it is available in different task-language combinations.
Approach:	They propose a single hypernetwork that unifies multi-task and multilingual learning with efficient adaptation.
Outcome:	The proposed model achieves the best or competitive gain when a mixture of multiple resources is available while being significantly more efficient than existing models.

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Jialiang Xu, Mengyu Zhou, Xinyi He, Shi Han, Dongmei Zhang

Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence (2022.emnlp-main)

Challenge:	Numerical Question Answering is the task of answering questions that require numerical capabilities.
Approach:	They propose to conduct numerical capability diagnosis on a series of Numerical Question Answering systems and datasets.
Outcome:	The proposed approach relieves existing systems’ lack of robust numerical capabilities.

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Mengxiao Song, Bowen Yu, Li Quangang, Wang Yubin, Tingwen Liu, Hongbo Xu

Towards Pragmatic Production Strategies for Natural Language Generation Tasks (2022.emnlp-main)

Challenge:	Existing joint models only use training procedure to determine the implicit correlation between intents and slots.
Approach:	They propose to make full use of the statistical co-occurrence frequency between intents and slots as prior knowledge to enhance joint multiple intent detection and slot filling.
Outcome:	The proposed model outperforms state-of-the-art models on two public multi-intent datasets.

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Mario Giulianelli

LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling (2022.emnlp-main)

Challenge:	Using language to communicate successfully requires effort.
Approach:	They propose a conceptual framework for the design of natural language generation systems that follow efficient and effective production strategies to achieve complex communicative goals.
Outcome:	The proposed framework is applied to visually grounded referential games and abstractive text summarisation tasks with real-world applications.

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Dongsheng Chen, Chaofan Tao, Lu Hou, Lifeng Shang, Xin Jiang, Qun Liu

Communication breakdown: On the low mutual intelligibility between human and neural captioning (2022.emnlp-main)

Challenge:	Recent large-scale video-language pre-trained models have shown appealing performance on downstream tasks.
Approach:	They propose a video-text model that adapts a pre-trained image-language model into a text-based model without heavy pre-training.
Outcome:	The proposed model outperforms existing models on video-text retrieval and video question answering tasks without heavy pre-training.

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Roberto Dessì, Eleonora Gualdoni, Francesca Franzon, Gemma Boleda, Marco Baroni

Normalizing Mutual Information for Robust Adaptive Training for Translation (2022.emnlp-main)

Challenge:	0-shot performance of a neural caption-based image retriever is higher when fed captions from a human-produced caption generator . despite the fact that the caption generator does not take the set of distractor images into account, this performance is only marginally above chance level.
Approach:	They compare the 0-shot performance of a neural caption-based image retriever with captions from a human-produced captioner.
Outcome:	The proposed model performs better when given human-produced captions or neural captions . the best pre-trained model perform better when fed captions produced by an out-of-the-box model .

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Youngwon Lee, Changmin Lee, Hojin Lee, Seung-won Hwang

Bilingual Synchronization: Restoring Translational Relationships with Editing Operations (2022.emnlp-main)

Challenge:	Neural machine translation models have been reported to generate hallucinations . despite the success of the models, there are still challenges to improve fluency .
Approach:	They propose a scoring metric for the importance of target sentences and tokens to encourage fluent translations.
Outcome:	The proposed metric improves translation fluency and source-faithfulness . the proposed nmi model is not properly normalized, the authors argue .

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Jitao Xu, Josep Crego, François Yvon

Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering (2022.emnlp-main)

Challenge:	Neural Machine Translation (NMT) is a one-shot process that generates the target language equivalent of some source text from scratch.
Approach:	They propose a machine translation task which assumes an initial target sequence, that must be transformed into a valid translation of the source.
Outcome:	The proposed system outperforms other systems trained for similar tasks.

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Helena Bonaldi, Sara Dellantonio, Serra Sinem Tekiroğlu, Marco Guerini

JANUS: Joint Autoregressive and Non-autoregressive Training with Auxiliary Loss for Sequence Generation (2022.emnlp-main)

Challenge:	a new approach to combat online hate speech is being proposed for NLG . existing methods to train NLG are limited to 2-turn interactions, while in real life, interactions can consist of multiple turns.
Approach:	They propose to combine human annotators with machine generated dialogues to create a dataset . DIALOCONAN is the first dataset comprising over 3000 fictitious multi-turn dialogues .
Outcome:	The proposed approach combines human experts over machine generated dialogues . it is the first dataset comprising over 3000 fictitious multi-turn dialogues between a hater and an NGO operator .

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Xiaobo Liang, Lijun Wu, Juntao Li, Min Zhang

Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering (2022.emnlp-main)

Challenge:	Existing approaches to train autoregressive and non-autoregressive models only consider relevance of model parameters, ignoring correlations between the two manners.
Approach:	They propose a joint autoregressive and non-autoregressive training method using aUxiliary losS to enhance the model performance in both AR and NAR manners simultaneously.
Outcome:	The proposed method improves the model performance in both AR and NAR manners and reduces the inference latency.

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Jialin Wu, Raymond Mooney

Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer? (2022.emnlp-main)

Challenge:	Existing outsideknowledge visual question answering systems lack retrieved knowledge and supervision is weak .
Approach:	They propose an Entity-Focused Retrieval model that provides stronger supervision during training and recognizes question-relevant entities to help retrieve more specific knowledge.
Outcome:	The proposed model achieves superior retrieval performance on the currently largest outside-knowledge VQA dataset.

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Ningyu Xu, Tao Gui, Ruotian Ma, Qi Zhang, Jingting Ye, Menghan Zhang, Xuanjing Huang

“It’s Not Just Hate”: A Multi-Dimensional Perspective on Detecting Harmful Speech Online (2022.emnlp-main)

Challenge:	Multilingual BERT (mBERT) has demonstrated considerable cross-lingual syntactic ability, but it is not well understood what leads to this variation and whether it fairly reflects difference between languages.
Approach:	They propose to use multilingual BERT to enable zero-shot cross-lingual transfer of syntactic knowledge between different languages by generating grammatical relations in 24 different languages.
Outcome:	The results show that the distance between the distributions of different languages is highly consistent with the syntactic difference in terms of linguistic formalisms.

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Federico Bianchi, Stefanie HIlls, Patricia Rossini, Dirk Hovy, Rebekah Tromble, Nava Tintarev

Long Text Generation with Topic-aware Discrete Latent Variable Model (2022.emnlp-main)

Challenge:	Detecting offensive content is becoming a critical task in natural language processing . but most datasets use a single binary label for hate or incivility, even though each concept is multi-faceted . a more fine-grained multi-label approach addresses conceptual and performance issues .
Approach:	They propose to use a dataset to annotate offensive online speech with six labels . they propose to apply a more fine-grained approach to predicting incivility and hateful content .
Outcome:	The proposed approach outperforms or matches benchmark datasets on the annotated tweets.

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Erguang Yang, Mingtong Liu, Deyi Xiong, Yujie Zhang, Yufeng Chen, Jinan Xu

TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Base (2022.emnlp-main)

Challenge:	Recent work focuses on the modeling of discourse relation, resulting in discrete codes learning shallow semantics.
Approach:	They propose a topic-aware latent code-guided text generation model that encourages discrete codes to model information about topics.
Outcome:	The proposed model generates more topic-relevant and coherent texts.

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Yiheng Shu, Zhiwei Yu, Yuhan Li, Börje Karlsson, Tingting Ma, Yuzhong Qu, Chin-Yew Lin

Structure-Unified M-Tree Coding Solver for Math Word Problem (2022.emnlp-main)

Challenge:	KBQA is a challenging area for pre-trained language models due to its extensive space and complexity.
Approach:	They propose a model that uses multi-grained retrieval to focus on most relevant KB contexts . constrained decoding is used to control output space and reduce generation errors .
Outcome:	The proposed model outperforms existing models on GrailQA and WebQuestionsSP.

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Bin Wang, Jiangzhou Ju, Yang Fan, Xinyu Dai, Shujian Huang, Jiajun Chen

Numerical Reasoning

Challenge:	Existing models that take into account the binary tree structure of mathematical expressions have achieved better performance, but the output space is non-deterministic.
Approach:	They propose a Structure-Unified M-Tree Coding Solver which applies a tree with any M branches to unify the output structures.
Outcome:	The proposed model outperforms several state-of-the-art models under similar experimental conditions and performs much better under low-resource conditions.

FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information (2022.emnlp-main)

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Yijia Shao, Mengyu Zhou, Yifan Zhong, Tao Wu, Hongwei Han, Shi Han, Gideon Huang, Dongmei Zhang

Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework (2022.emnlp-main)

Challenge:	FormLM is a pre-trained language model for creating semi-structured forms where questions and descriptions are organized by predefined structures.
Approach:	They propose to enhance pre-trained language model with form structural information to model online forms and recommend form creation ideas.
Outcome:	The proposed model outperforms general-purpose language models on all tasks, with an improvement by 4.71 on Question Recommendation and 10.6 on Block Type Suggestion in terms of ROUGE-1 and Macro-F1, respectively.

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Yiming Chen, Yan Zhang, Bin Wang, Zuozhu Liu, Haizhou Li

GPS: Genetic Prompt Search for Efficient Few-Shot Learning (2022.emnlp-main)

Challenge:	Existing supervised sentence embedding techniques rely on expensive human-annotated sentence pairs as the supervised signals.
Approach:	They propose a semi-supervised sentence embedding framework that leverages large-scale unlabeled data.
Outcome:	The proposed framework surpasses state-of-the-art methods on four domain adaptation tasks.

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Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Wang Yanggang, Haiyu Li, Zhilin Yang

Multitask Instruction-based Prompting for Fallacy Recognition (2022.emnlp-main)

Challenge:	Pretrained language models are often finetuned for downstream tasks, which has been shown to improve performance over non-pretrained models.
Approach:	They propose a genetic algorithm to automatically search for the best prompt for few-shot learning with pretrained language models by gradient-free algorithm.
Outcome:	Experiments on diverse datasets show that the proposed method outperforms manual prompts by 2.6 points.

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Tariq Alhindi, Tuhin Chakrabarty, Elena Musi, Smaranda Muresan

Rethinking Multi-Modal Alignment in Multi-Choice VideoQA from Feature and Sample Perspectives (2022.emnlp-main)

Challenge:	Fallacies are used as seemingly valid arguments to support a position and persuade the audience about its validity.
Approach:	They propose to use instruction-based prompting to recognize 28 unique fallacies across datasets . they also analyze the effect of model size and prompt choice on model performance .
Outcome:	The proposed approach can recognize 28 unique fallacies across domains and genres.

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Shaoning Xiao, Long Chen, Kaifeng Gao, Zhao Wang, Yi Yang, Zhimeng Zhang, Jun Xiao

Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach (2022.emnlp-main)

Challenge:	Existing approaches to VideoQA focus on utilizing frame- or object-level visual representations, but they neglect visual-language interactions.
Approach:	They propose to break down video into trajectories and first leverage trajectory feature in VideoQA to enhance alignment between two modalities.
Outcome:	The proposed method outperforms all the state-of-the-art models on the NExT-QA benchmark.

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Miao Chen, Xinjiang Lu, Tong Xu, Yanyan Li, Zhou Jingbo, Dejing Dou, Hui Xiong

Hierarchical Phrase-Based Sequence-to-Sequence Learning (2022.emnlp-main)

Challenge:	Currently, the generalization issues hinder the applicability of neural table-to-text models due to the limited source tables.
Approach:	They propose a table-structureaware text generation model with pretrained language model and propose TASD to bridge the gap between the structured table and text input.
Outcome:	The proposed model bridges the gap between the structured table and text input and generates accurate and fluent descriptive texts on two public datasets.

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Bailin Wang, Ivan Titov, Jacob Andreas, Yoon Kim

Natural Language Deduction with Incomplete Information (2022.emnlp-main)

Challenge:	a neural transducer that incorporates hierarchical phrases as a source of inductive bias during training and as explicit constraints during inference is described.
Approach:	They propose a neural transducer that incorporates hierarchical phrases as a source of inductive bias during training and as explicit constraints during inference.
Outcome:	The proposed model performs well on small scale machine translation benchmarks.

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Zayne Sprague, Kaj Bostrom, Swarat Chaudhuri, Greg Durrett

Character-centric Story Visualization via Visual Planning and Token Alignment (2022.emnlp-main)

Challenge:	Existing systems for reasoning given incomplete information are inadequate . current approaches to reasoning are based on latent reasoning by large language models .
Approach:	They propose a system that generates a natural language "proof" by abductively inferring a premise from another premise and a conclusion.
Outcome:	The proposed system can handle the underspecified setting where not all premises are stated at the outset; additional assumptions need to be materialized to prove a claim.

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Hong Chen, Rujun Han, Te-Lin Wu, Hideki Nakayama, Nanyun Peng

ASQA: Factoid Questions Meet Long-Form Answers (2022.emnlp-main)

Challenge:	Story visualization is a task that requires machines to understand long text inputs and produce a globally consistent image sequence that illustrates the contents of the story.
Approach:	They propose to augment VQ-VAE with a text-to-visual-token (transformer) architecture to enable multiple image generation based on a complete story.
Outcome:	The proposed method excels at preserving characters and produces higher quality image sequences compared with baselines.

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Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, Ming-Wei Chang

Algorithms for Acyclic Weighted Finite-State Automata with Failure Arcs (2022.emnlp-main)

Challenge:	Recent progress on factoid question answering (QA) does not easily transfer to the task of long-form QA where the goal is to generate detailed explanations.
Approach:	They propose a task that focuses on ambiguous factoid questions which have different correct answers depending on interpretation.
Outcome:	The proposed metric is reliable and demonstrates agreement between this metric and human judgments, and reveals a considerable gap between human performance and strong baselines.

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Anej Svete, Benjamin Dayan, Ryan Cotterell, Tim Vieira, Jason Eisner

Challenge:	Weighted finite-state automata (WSFAs) are common formalisms in NLP.
Approach:	They propose an algorithm for semiring-weighted WFSAs with av-erage out symbol fractions .
Outcome:	The proposed algorithms are faster than the standard methods for weighted finite-state automata.

Towards Better Document-level Relation Extraction via Iterative Inference (2022.emnlp-main)

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Liang Zhang, Jinsong Su, Yidong Chen, Zhongjian Miao, Min Zijun, Qingguo Hu, Xiaodong Shi

Efficient Adversarial Training with Robust Early-Bird Tickets (2022.emnlp-main)

Challenge:	Existing methods only consider feature information of entity pairs, but our model exploits both feature information and previous predictions of entity pair.
Approach:	They propose a document-level relation extraction model with iterative inference to extract relations between entities from raw texts.
Outcome:	The proposed model outperforms existing methods on three commonly-used datasets.

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Zhiheng Xi, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang

Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks (2022.emnlp-main)

Challenge:	Existing methods to improve the robustness of pre-trained language models are expensive because of the need to generate adversarial examples via gradient descent.
Approach:	They propose an adversarial optimization method that searches for robust tickets with structured sparsity in the early stage and fine-tunes tickets in the remaining time.
Outcome:	The proposed method achieves up to 7 13 training speedups while maintaining comparable or even better robustness compared to the most competitive state-of-the-art methods.

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Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, Taylor Berg-Kirkpatrick, Reza Shokri

SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages (2022.emnlp-main)

Challenge:	Prior attempts at measuring leakage of MLMs via membership inference attacks have been inconclusive, implying potential robustness of Mlms to privacy attacks.
Approach:	They propose a stronger membership inference attack based on likelihood ratio hypothesis testing that involves an additional MLM to more accurately quantify the privacy risks of memorization in MLMs.
Outcome:	The proposed attack improves the AUC of prior membership inference attacks from 0.66 to an alarmingly high 0.90 level on models trained on medical notes.

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Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard, Caroline Brun, James Henderson, Laurent Besacier

TextFusion: Privacy-Preserving Pre-trained Model Inference via Token Fusion (2022.emnlp-main)

Challenge:	Existing models for multilingual machine translation use scaling up the number of parameters to overcome the curse of multilinguality.
Approach:	They propose a multilingual machine translation model that shares information between similar languages and scales up the number of parameters to overcome the curse of multilinguality.
Outcome:	The proposed model outperforms previous models on low-resource benchmarks while improving inference latency and memory usage.

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Xin Zhou, Jinzhu Lu, Tao Gui, Ruotian Ma, Zichu Fei, Yuran Wang, Yong Ding, Yibo Cheung, Qi Zhang, Xuanjing Huang

Learning to Explain Selectively: A Case Study on Question Answering (2022.emnlp-main)

Challenge:	Existing methods to preserve inference privacy are available as cloud services . however, the risk of privacy leakage remains, according to recent studies .
Approach:	They propose a method to preserve inference privacy by fusing token representations in the cloud.
Outcome:	The proposed method preserves inference privacy without sacrificing performance on different scenarios.

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Shi Feng, Jordan Boyd-Graber

ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation (2022.emnlp-main)

Challenge:	Recent advances in machine learning (ML) have obstructed the use of NNs.
Approach:	They propose to learn to explain"selectively" for each decision that the user makes . they use a model to choose the best explanation from a set of candidates and update this model with feedback .
Outcome:	The proposed model improves human performance on a question-based task for experts and crowdworkers.

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Zhaocong Li, Xuebo Liu, Derek F. Wong, Lidia S. Chao, Min Zhang

Better Hit the Nail on the Head than Beat around the Bush: Removing Protected Attributes with a Single Projection (2022.emnlp-main)

Challenge:	Existing transfer learning methods for low-resource NMT are static, which simply transfer knowledge from a parent model to a child model once via parameter initialization.
Approach:	They propose a transfer learning method that can continuously transfer knowledge from the parent model during the training of the child model.
Outcome:	The proposed method can transfer knowledge from the parent model to the child model during the training of the child.

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Pantea Haghighatkhah, Antske Fokkens, Pia Sommerauer, Bettina Speckmann, Kevin Verbeek

IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models (2022.emnlp-main)

Challenge:	Existing methods to remove specific information from embeddings are based on multiple iterations, but multiple iters increase the risk of negative effects.
Approach:	They propose two methods that find a single targeted projection: Mean Projection and Tukey Median Projection.
Outcome:	The proposed method removes biases by removing information from embedding spaces . it is cleaner than the previous method because it removes separability based on the target .

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Chenguang Wang, Xiao Liu, Dawn Song

ConNER: Consistency Training for Cross-lingual Named Entity Recognition (2022.emnlp-main)

Challenge:	Recent studies show pre-trained LMs store linguistic and relational knowledge . pre-training LM models can answer "fill-in-the-blank" questions based on pre-defined relations .
Approach:	They propose an open information extraction benchmark for pre-trained language models . they turn pre-trained LMs into zero-shot OIE systems to examine open relational information .
Outcome:	The proposed benchmark outperforms state-of-the-art methods on factual OIE datasets without training sets.

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Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

A Sequential Flow Control Framework for Multi-hop Knowledge Base Question Answering (2022.emnlp-main)

Challenge:	Existing consistency training methods for named entity recognition (NER) are likely to violate the consistency hypothesis or focus on coarse-grain consistency.
Approach:	They propose a consistency training framework for cross-lingual named entity recognition that leverages unlabeled target-language data and dropout-based consistency training on labeled source-language datasets.
Outcome:	The proposed framework improves on translation-based consistency training on unlabeled target-language data and dropout-based consistent training on labeled source-language datasets.

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Minghui Xie, Chuzhan Hao, Peng Zhang

ACENet: Attention Guided Commonsense Reasoning on Hybrid Knowledge Graph (2022.emnlp-main)

Challenge:	Existing methods for multi-hop reasoning in knowledge base question answering are coarse-grained and may bring information loss.
Approach:	They propose a sequential reasoning self-attention mechanism to capture the crucial reasoning information of each hop in a more fine-grained way.
Outcome:	The proposed model achieves new state-of-the-art Hits@1 of 76.8% on WebQSP and is also effective when KB is incomplete.

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Chuzhan Hao, Minghui Xie, Peng Zhang

Revisiting DocRED - Addressing the False Negative Problem in Relation Extraction (2022.emnlp-main)

Challenge:	Existing approaches estimate plausibility of candidate choices separately based on their respective KGs, without considering the interference among different choices.
Approach:	They propose an Attention guided Commonsense rEasoning Network to integrate hybrid knowledge into the neural network.
Outcome:	The proposed model outperforms existing methods on CommonsenseQA and OpenbookQA datasets and shows significant performance gains.

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Qingyu Tan, Lu Xu, Lidong Bing, Hwee Tou Ng, Sharifah Mahani Aljunied

Towards Summary Candidates Fusion (2022.emnlp-main)

Challenge:	Using incomplete annotations, we find that false negative samples are prevalent in the DocRED dataset . we reannotate 4,053 documents in the dataset by adding the missed relation triples back to the original DocRED.
Approach:	They propose to re-annotate 4,053 documents in the document-level relation extraction dataset by adding missing relation triples back to the original DocRED.
Outcome:	The proposed dataset improves on the existing DocRED dataset by 13 F1 points.

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Mathieu Ravaut, Shafiq Joty, Nancy Chen

Multimodal Robustness for Neural Machine Translation (2022.emnlp-main)

Challenge:	Existing methods for abstractive summarization are limited by the quality of the first-stage candidates.
Approach:	They propose a method that fuses several summary candidates to produce a novel abstractive second-stage summary.
Outcome:	The proposed method improves ROUGE scores and qualitative properties of fused summaries on several summarization datasets.

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Yuting Zhao, Ioan Calapodescu

TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction (2022.emnlp-main)

Challenge:	Existing approaches to deal with noisy multimodal inputs are not robust enough to deal effectively with noisy data.
Approach:	They propose a method that composes domain adapters to deal with noisy inputs . they combine these adapters at runtime via dynamic routing or when source of noise is unknown .
Outcome:	The proposed model is flexible and state-of-the-art to deal with noisy multimodal inputs.

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Yizhi Li, Wei Fan, Chao Liu, Chenghua Lin, Jiang Qian

IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection (2022.emnlp-main)

Challenge:	Existing knowledge graph embedding methods restrict entities on hyper-ellipsoid surfaces, resulting in suboptimal knowledge graph completion.
Approach:	They propose a score function that leverages relation-specific translations between head and tail entities to relax constraints on hyper-ellipsoid surfaces.
Outcome:	The proposed method achieves state-of-the-art performance on link prediction and generalizes well to datasets in different domains and scales.

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Jingcheng Deng, Hengwei Dai, Xuewei Guo, Yuanchen Ju, Wei Peng

Predicting Prerequisite Relations for Unseen Concepts (2022.emnlp-main)

Challenge:	Existing studies focus on matching between candidate options and historical dialogues while ignoring the reasoning ability of the model.
Approach:	They propose an Implicit Relational Reasoning Graph Network to address these issues . they propose to implicitly extract dependencies between utterances and options .
Outcome:	The proposed model outperforms human models on two multi-turn dialogue reasoning benchmark datasets.

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Yaxin Zhu, Hamed Zamani

Contrastive Learning with Expectation-Maximization for Weakly Supervised Phrase Grounding (2022.emnlp-main)

Challenge:	Concept prerequisite learning (CPL) is a task of building a concept graph by structuring open knowledge in prerequisite relations.
Approach:	They propose to use both content-based and graph-based models to build a concept graph by structuring open knowledge in prerequisite relations.
Outcome:	The proposed approach improves F1 scores by 10% on three public benchmarks.

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Keqin Chen, Richong Zhang, Samuel Mensah, Yongyi Mao

Beyond prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations (2022.emnlp-main)

Challenge:	Weakly supervised phrase grounding aims to learn an alignment between phrases in a caption and objects in an image using only caption-image annotations.
Approach:	They propose a novel contrastive learning framework that adaptively refines the target prediction by using only caption-image annotations.
Outcome:	The proposed framework outperforms existing methods on two widely used benchmarks, Flickr30K Entities and RefCOCO+.

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Yu Fei, Zhao Meng, Ping Nie, Roger Wattenhofer, Mrinmaya Sachan

Generalizing over Long Tail Concepts for Medical Term Normalization (2022.emnlp-main)

Challenge:	Existing methods for zero-shot text classification involve heavy human engineering or complicated self-training pipelines.
Approach:	They propose to fit unlabeled text with a Bayesian Gaussian Mixture Model and use class names to cluster them.
Outcome:	The proposed approach outperforms prompt-based methods on topic and sentiment datasets and outperformed previous studies significantly on unbalanced datasets.

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Beatrice Portelli, Simone Scaboro, Enrico Santus, Hooman Sedghamiz, Emmanuele Chersoni, Giuseppe Serra

Unsupervised Opinion Summarisation in the Wasserstein Space (2022.emnlp-main)

Challenge:	Medical term normalization is a task of mapping a text to a large number of output classes.
Approach:	They propose a learning strategy that leverages hierarchical information to enhance generalizability of models.
Outcome:	The proposed strategy produces state-of-the-art performance on seen concepts and consistent improvements on unseen ones, allowing efficient zero-shot knowledge transfer across text typologies and datasets.

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Jiayu Song, Iman Munire Bilal, Adam Tsakalidis, Rob Procter, Maria Liakata

Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks (2022.emnlp-main)

Challenge:	Recent work on opinion summarisation of social media posts has focused on reviews . however, it is important to capture user opinions in online discussions over specific topics .
Approach:	They propose an unsupervised opinion summarisation model which uses the Wasserstein distance to generate a single summary from a group of documents.
Outcome:	The proposed model outperforms the state-of-the-art on ROUGE metrics and produces the best summaries with respect to meaning preservation according to human evaluations.

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Colin Leong, Joshua Nemecek, Jacob Mansdorfer, Anna Filighera, Abraham Owodunni, Daniel Whitenack

Disentangling Uncertainty in Machine Translation Evaluation (2022.emnlp-main)

Challenge:	In total, the initial release of the Bloom Library datasets covers 363 languages across 32 language families.
Approach:	They present a set of multimodal and multilingual datasets for language modeling, image captioning, visual storytelling, and speech synthesis/recognition.
Outcome:	The Bloom Library datasets cover 363 languages across 32 language families.

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Chrysoula Zerva, Taisiya Glushkova, Ricardo Rei, André F. T. Martins

Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing (2022.emnlp-main)

Challenge:	Trainable evaluation metrics for machine translation (MT) exhibit strong correlation with human judgements, but they are often hard to interpret and might produce unreliable scores under noisy or out-of-domain data.
Approach:	They propose to use Monte Carlo dropout and deep ensembles to quantify uncertainty in machine translation and assess their ability to target different sources of aleatoric and epistemic uncertainty.
Outcome:	The proposed measures can target different sources of aleatoric and epistemic uncertainty, with a reduction in computational costs.

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Nan Xu, Fei Wang, Bangzheng Li, Mingtao Dong, Muhao Chen

EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing (2022.emnlp-main)

Challenge:	Existing entity typing models are subject to spurious correlations due to shortcuts and biased training.
Approach:	They propose a method to augment existing model biases by combining spurious correlations with debiasedcounterparts to improve generalization.
Outcome:	The proposed method improves generalization of different entity typing models on the original and debiased test sets.

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Nora Kassner, Fabio Petroni, Mikhail Plekhanov, Sebastian Riedel, Nicola Cancedda

Knowledge Representation

Challenge:	Existing work on Entity Linking assumes that the knowledge base is complete and all mentions can be linked.
Approach:	They propose a temporally segmented Unknown Entity Discovery and Indexing (EDIN) benchmark where unknown entities have to be integrated into existing entity linking systems.
Outcome:	The proposed system detects, clusters, and indexes mentions of unknown entities in context.

POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events (2022.emnlp-main)

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Sai Vallurupalli, Sayontan Ghosh, Katrin Erk, Niranjan Balasubramanian, Francis Ferraro

Challenge:	Existing language models lag behind human performance in subtle ways in understanding complex situations, e.g., if the Argentine government yields to [IMF] pressure to rescind emergency legislation meant to protect ordinary families like the Brofmans.
Approach:	They propose to pre-identify a participant in a complex event and annotate their volitional engagement in causing the situation.
Outcome:	The proposed model can be used to infer the collective impact of salient events that make up a complex event, annotate volitional engagement of participants, and ground the outcome in state changes of the participants.

Measuring the Mixing of Contextual Information in the Transformer (2022.emnlp-main)

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Javier Ferrando, Gerard I. Gállego, Marta R. Costa-jussà

Dealing with Abbreviations in the Slovenian Biographical Lexicon (2022.emnlp-main)

Challenge:	Experimentally, we show that ALTI provides more faithful explanations and increased robustness than gradient-based methods.
Approach:	They propose to measure token-to-token interactions within each layer and then use them to aggregate model predictions.
Outcome:	The proposed method provides more faithful explanations and increased robustness than gradient-based methods.

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Angel Daza, Antske Fokkens, Tomaž Erjavec

AfriCLIRMatrix: Enabling Cross-Lingual Information Retrieval for African Languages (2022.emnlp-main)

Challenge:	Abbreviations are a significant challenge for NLP systems because they cause tokenization and out-of-vocabulary errors.
Approach:	They propose a method for identifying abbreviations in a Slovenian biographical lexicon . they use a newly developed dataset to evaluate the method against common ad-hoc solutions .
Outcome:	The proposed method performs better than ad-hoc solutions on a Slovenian biographical lexicon.

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Odunayo Ogundepo, Xinyu Zhang, Shuo Sun, Kevin Duh, Jimmy Lin

CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation (2022.emnlp-main)

Challenge:	Existing datasets for cross-lingual information retrieval are limited in many languages, especially those spoken in Africa.
Approach:	They propose to build a test collection for cross-lingual information retrieval in 15 diverse African languages.
Outcome:	AfriCLIRMatrix contains 6 million queries in English and 23 million relevance judgments automatically mined from Wikipedia inter-language links, covering many more African languages than any existing information retrieval test collection.

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Abhilasha Ravichander, Matt Gardner, Ana Marasovic

Towards Opening the Black Box of Neural Machine Translation: Source and Target Interpretations of the Transformer (2022.emnlp-main)

Challenge:	Negation is fundamental to human communication.
Approach:	They propose a dataset which requires reasoning about implications of negated statements in paragraphs . they collect paragraphs with diverse negation cues and crowdworkers ask questions about implications .
Outcome:	The first dataset in english requires reasoning about implications of negated statements in paragraphs . it features 14,182 question-answer pairs with over 200 unique negation cues based on crowd-workers . the best performing model achieves only 42% on consistency metric, well below human performance of 81%.

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Javier Ferrando, Gerard I. Gállego, Belen Alastruey, Carlos Escolano, Marta R. Costa-jussà

ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture (2022.emnlp-main)

Challenge:	Neural Machine Translation (NMT) relies on source sentence and target prefix attributions for each input token.
Approach:	They propose an interpretability method that tracks input tokens’ attributions for both contexts and extends it to any encoder-decoder Transformer-based model.
Outcome:	The proposed method can be extended to any encoder-decoder Transformer-based model and provides insights into their behaviour.

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Youssef Mohamed, Mohamed Abdelfattah, Shyma Alhuwaider, Feifan Li, Xiangliang Zhang, Kenneth Church, Mohamed Elhoseiny

Decoding a Neural Retriever’s Latent Space for Query Suggestion (2022.emnlp-main)

Challenge:	ArtELingo is a benchmark and dataset designed to encourage work on diversity across languages and cultures.
Approach:	They introduce a benchmark and dataset designed to encourage work on diversity across languages and cultures.
Outcome:	The new benchmark and dataset compared artELingo annotations across languages and cultures and found that diversity improves the performance of baseline models.

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Leonard Adolphs, Michelle Chen Huebscher, Christian Buck, Sertan Girgin, Olivier Bachem, Massimiliano Ciaramita, Thomas Hofmann

T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation (2022.emnlp-main)

Challenge:	Neural retrieval models have replaced bag-of-words methods for document retrieval . however, they lack the interpretability of bag-off-word models .
Approach:	They train a query decoder that generates a meaningful query from a latent representation of a neural search engine.
Outcome:	The proposed model outperforms both query reformulation and PRF information retrieval baselines.

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Anubhav Jangra, Preksha Nema, Aravindan Raghuveer

PromptBERT: Improving BERT Sentence Embeddings with Prompts (2022.emnlp-main)

Challenge:	Unavailability of parallel corpora for training text style transfer models is a challenge but common . a large corpus of parallel data is not available for text style transfers .
Approach:	They propose to use AMR as an intermediate style agnostic representation to train TST models.
Outcome:	The proposed model outperforms state-of-the-art models in the style agnostic task.

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Ting Jiang, Jian Jiao, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Denvy Deng, Qi Zhang

Extending Logic Explained Networks to Text Classification (2022.emnlp-main)

Challenge:	Existing research shows that BERT and RoBERTa are poor in sentence embeddings due to static token embeddable bias and ineffective BERT layers.
Approach:	They propose a novel contrastive learning method for better sentence embeddings by using a template denoising technique.
Outcome:	The proposed method achieves 2.29 and 2.58 points of improvement compared to SimCSE and RoBERTa in the unsupervised setting.

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Rishabh Jain, Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini, Davide Buffelli, Pietro Lio

Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database (2022.emnlp-main)

Challenge:	Recent studies have proposed explainable-by-design neural models providing logic explanations for their predictions, but these models favour global explanations, while local ones tend to be noisy and verbose.
Approach:	They propose to use LENp to improve local explanations by perturbing input words to improve sensitivity and faithfulness of local explanation.
Outcome:	The proposed model provides better local explanations than LIME and is more user-friendly than Lime as attested by a human survey.

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Ye Liu, Semih Yavuz, Rui Meng, Dragomir Radev, Caiming Xiong, Yingbo Zhou

RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction (2022.emnlp-main)

Challenge:	Existing approaches on semantic parsing suffer from exponential growth of logical form candidates and can hardly generalize to unseen data.
Approach:	They propose a unified semantic parser for question answering on KB and DB . they define the primitive as the essential element in their framework .
Outcome:	The proposed framework can predict logical forms by altering and composing top-ranked primitives with different operations.

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Zhoujin Tian, Chaozhuo Li, Shuo Ren, Zhiqiang Zuo, Zengxuan Wen, Xinyue Hu, Xiao Han, Haizhen Huang, Denvy Deng, Qi Zhang, Xing Xie

On Parsing as Tagging (2022.emnlp-main)

Challenge:	Existing approaches focus on minimizing distances between words in aligned pairs, while suffering from low discriminative capability to distinguish the relative orders between positive and negative candidates.
Approach:	They propose a ranking-oriented induction model to learn personalized mapping function for each word.
Outcome:	The proposed model can learn personalized mapping function for each word on public datasets including rich-resource and low-resourced languages.

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Afra Amini, Ryan Cotterell

Distilled Dual-Encoder Model for Vision-Language Understanding (2022.emnlp-main)

Challenge:	Existing approaches to reduce constituency parsing to tagging are based on linearization, learning, and decoding . linearization of the derivation tree is the most critical factor in achieving accurate parsers as taggers .
Approach:	They propose a pipeline with three steps for reducing constituency parsing to tagging . they find that linearization and learning are critical factors for accurate parsers .
Outcome:	The proposed pipelines are linearized, learning, and decoded, and have three steps to achieve accurate parsing as taggers.

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Zekun Wang, Wenhui Wang, Haichao Zhu, Ming Liu, Bing Qin, Furu Wei

Argument Mining for Review Helpfulness Prediction (2022.emnlp-main)

Challenge:	Experimental results show that the proposed cross-modal attention distillation is crucial to the success of our framework.
Approach:	They propose a framework that distills knowledge of fusion-encoder teacher into dual-encoding student model.
Outcome:	The proposed model is competitive with the fusion-encoder teacher model in performance, but suffers from a lack of deep cross-modal interactions.

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Zaiqian Chen, Daniel Verdi do Amarante, Jenna Donaldson, Yohan Jo, Joonsuk Park

Hierarchical Multi-Label Classification of Scientific Documents (2022.emnlp-main)

Challenge:	Argumentational features have been shown to be promising indicators of product review helpfulness, but their utility has been limited due to the lack of resources and large-scale experiments investigating their utility.
Approach:	They present an argumentational argumentation model that annotates 878 Amazon reviews on headphones and uses it to evaluate argument quality.
Outcome:	The proposed model improves the state-of-the-art model under text-only and text-and-image settings.

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Mobashir Sadat, Cornelia Caragea

Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering (2022.emnlp-main)

Challenge:	Automated topic classification is a useful tool for managing scientific documents in a digital collection.
Approach:	They propose a hierarchical multi-label text classification dataset with keyword labeling as an auxiliary task.
Outcome:	The proposed model achieves a Macro-F1 score of 34.57% and is publicly available.

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Jiacheng Liu, Skyler Hallinan, Ximing Lu, Pengfei He, Sean Welleck, Hannaneh Hajishirzi, Yejin Choi

A Major Obstacle for NLP Research: Let’s Talk about Time Allocation! (2022.emnlp-main)

Challenge:	Recent research shows that relevant knowledge can provide useful context for commonsense tasks.
Approach:	They propose a method that learns to generate contextually relevant knowledge in response to given questions.
Outcome:	The proposed method shows consistent gains over 9 commonsense benchmarks.

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Katharina Kann, Shiran Dudy, Arya D. McCarthy

Towards Inter-character Relationship-driven Story Generation (2022.emnlp-main)

Challenge:	Subpar time allocation has been a major obstacle for natural language processing research in recent years, argues a new position paper .
Approach:	They propose to identify the biggest traps the NLP community falls into and suggest solutions to solve them.
Outcome:	The authors outline multiple concrete problems together with their negative consequences and suggest remedies to improve the status quo.

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Anvesh Rao Vijjini, Faeze Brahman, Snigdha Chaturvedi

Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking (2022.emnlp-main)

Challenge:	Recent story generation methods can generate stories based on open-ended prompts and planners but can neither encode character relationships nor give explicit control over the characters and their relationships.
Approach:	They propose a model that uses relationships as latent variables for story generation and propose 'relationship-driven' story generation.
Outcome:	The proposed model generates stories sentence by sentence with relationships that are more faithful to desired relationships while maintaining the content quality.

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Tim Baumgärtner, Leonardo F. R. Ribeiro, Nils Reimers, Iryna Gurevych

ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples (2022.emnlp-main)

Challenge:	Extensive experiments demonstrate that integrating relevance feedback directly into neural re-ranking models improves their performance.
Approach:	They propose a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant.
Outcome:	The proposed approach outperforms all other methods by 5.2% on large-scale information retrieval datasets.

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Yilun Zhao, Linyong Nan, Zhenting Qi, Rui Zhang, Dragomir Radev

Few-shot Learning with Multilingual Generative Language Models (2022.emnlp-main)

Challenge:	Existing models with table-specific architectures and pre-training methods perform well on understanding table structures but lack table reasoning skills.
Approach:	They propose to pre-train tables with table reasoning skills without complex architectures . they define 7 table reasoning skill, and then pre-teach them to generate tables .
Outcome:	The proposed model improves on four tasks and is available on github.

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Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li

Are representations built from the ground up? An empirical examination of local composition in language models (2022.emnlp-main)

Challenge:	Large-scale generative language models such as GPT-3 are competitive few-shot learners.
Approach:	They train multilingual generative language models on a corpus covering a diverse set of languages and study their few- and zero-shot learning capabilities.
Outcome:	The proposed model outperforms GPT-3 on 171 out of 182 directions with 32 training examples and surpasses the official supervised baseline in 45 directions.

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Emmy Liu, Graham Neubig

Detecting Label Errors by Using Pre-Trained Language Models (2022.emnlp-main)

Challenge:	Compositionality is a hallmark of human language, but many phrases are non-compositional . a study by a team of researchers shows that LMs may not be able to distinguish between compositional and non-composable phrases.
Approach:	They propose to predict LM-internal representations of longer phrases given their constituents . they find that the representation of a parent phrase can be predicted with some accuracy .
Outcome:	The proposed model can predict a parent phrase with some accuracy given its children's transformations, but this is not the case.

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Derek Chong, Jenny Hong, Christopher Manning

Intriguing Properties of Compression on Multilingual Models (2022.emnlp-main)

Challenge:	Existing methods for label error detection focus on label errors in training data.
Approach:	They propose a method for introducing realistic, human-originated label noise into existing crowdsourced datasets such as SNLI and TweetNLP.
Outcome:	The proposed method outperforms existing methods for detecting label errors in natural language datasets.

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Kelechi Ogueji, Orevaoghene Ahia, Gbemileke Onilude, Sebastian Gehrmann, Sara Hooker, Julia Kreutzer

Sequence Models for Document Structure Identification in an Undeciphered Script (2022.emnlp-main)

Challenge:	Multilingual models are dependent on scaling to generalize to a growing number of languages . compression techniques can have disparate effects on model performance for low-resource languages if used sparsely .
Approach:	They propose to characterize the impact of sparsifying multilingual pre-trained language models during fine-tuning.
Outcome:	The proposed framework characterizes the impact of sparsifying multilingual pre-trained language models during fine-tuning.

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Logan Born, M. Monroe, Kathryn Kelley, Anoop Sarkar

Challenge:	a systematic analysis of “header” signs in proto-Elamite provides new evidence for their existence . experts have hypothesized that headers are a sign which qualifies transactions .
Approach:	They provide unsupervised neural and statistical sequence modeling techniques to identify “header” signs in proto-Elamite, an undeciphered script from 3100-2900 BCE.
Outcome:	The authors provide new evidence for the existence of "header" signs in proto-Elamite . they examine which features predict their presence and identify correlations between features and other document properties .

English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings (2022.emnlp-main)

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Yaushian Wang, Ashley Wu, Graham Neubig

Active Example Selection for In-Context Learning (2022.emnlp-main)

Challenge:	mSimCSE can learn high-quality universal cross-lingual sentence embeddings without any parallel data.
Approach:	They propose a new language-based sentence embedding system that extends SimCSE to multilingual settings.
Outcome:	The proposed method improves existing methods on retrieval and multilingual STS tasks.

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Yiming Zhang, Shi Feng, Chenhao Tan

Improving Factual Consistency in Summarization with Compression-Based Post-Editing (2022.emnlp-main)

Challenge:	In-context learning performance is unstable across samples of examples, suggesting the idiosyncrasies of how language models acquire information.
Approach:	They propose a reinforcement learning algorithm for identifying generalizable policies to select demonstration examples and propose 'in-context learning' performance can be highly unstable across samples of examples, suggesting the idiosyncrasies of how language models acquire information.
Outcome:	The proposed model can perform tasks with examples with a 5.8% improvement on GPT-2 and GPT-3, but the improvement diminishes on larger models, suggesting emerging capabilities of large language models.

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Alex Fabbri, Prafulla Kumar Choubey, Jesse Vig, Chien-Sheng Wu, Caiming Xiong

Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing (2022.emnlp-main)

Challenge:	Existing methods to improve factual consistency of summarization models fail to remove entity errors if a suitable input entity replacement is not available or insert erroneous content.
Approach:	They propose to remove extrinsic entity errors, or entities not in the source, to improve consistency while retaining the summary’s essential information and form.
Outcome:	The proposed model improves factual consistency while maintaining ROUGE, improving entity precision by up to 30% on XSum, and can be applied on top of another post-editor, improving accuracy by 38%.

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Linlu Qiu, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, Kristina Toutanova

“I’m sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset (2022.emnlp-main)

Challenge:	Pre-trained language models struggle on out-of-distribution compositional generalization . recent work shows considerable improvements on many NLP tasks from model scaling .
Approach:	They evaluate encoder-decoder models up to 11B parameters and decoder-only models up 540B parameters . they compare scaling curves for fine-tuning, prompt tuning, and in-context learning methods .
Outcome:	The proposed scaling methods improve compositional generalization on many tasks . fine-tuning generally has flat or negative scaling curves on out-of-distribution compositional . larger models are better at modeling the syntax of the output space, the study finds .

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Eric Michael Smith, Melissa Hall, Melanie Kambadur, Eleonora Presani, Adina Williams

Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense (2022.emnlp-main)

Challenge:	Language models are increasingly important to measure all possible demographic markers of identity . many datasets for measuring bias are limited in their coverage of demographic axes .
Approach:	They propose a bias measurement dataset that includes nearly 600 descriptor terms across 13 demographic axes.
Outcome:	The proposed dataset explores, detects, and reduces biases in language models.

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Zhecan Wang, Haoxuan You, Yicheng He, Wenhao Li, Kai-Wei Chang, Shih-Fu Chang

Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities (2022.emnlp-main)

Challenge:	Existing models that understand image and text but also cross-reference in-between are lacking in evaluation data resources.
Approach:	They propose a multimodal evaluation pipeline to automatically generate question-answer pairs to test models’ understanding of the visual scene, text, and related knowledge.
Outcome:	The proposed model can answer the highly semantic VCR question correctly but fails to answer related visual question (Q2), textual question (q3), and background knowledge question ( Q4) as shallow mappings with language priors and unbalanced utilization of information between modalities.

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Nianzu Ma, Sahisnu Mazumder, Alexander Politowicz, Bing Liu, Eric Robertson, Scott Grigsby

CN-AutoMIC: Distilling Chinese Commonsense Knowledge from Pretrained Language Models (2022.emnlp-main)

Challenge:	Existing topic-based novelty detection methods do not perform semantic reasoning involving relations between named entities in text and their background knowledge.
Approach:	They propose a model to detect whether a text is novel or not . they propose to use a factual text to characterize novelty.
Outcome:	The proposed model outperforms 10 baselines by large margins on the novelty detection task.

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Chenhao Wang, Jiachun Li, Yubo Chen, Kang Liu, Jun Zhao

Calibrating Student Models for Emotion-related Tasks (2022.emnlp-main)

Challenge:	Existing commonsense knowledge graphs are limited to English, hindering research in non-English languages.
Approach:	They propose a Chinese CKG generated from multilingual PLMs that is translated into Chinese . they propose 'generate-by-category' strategy to reduce invalid generation .
Outcome:	The proposed CKG has high quality and diversity, surpassing the direct translation version of similar English CKGs.

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Mahshid Hosseini, Cornelia Caragea

Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation (2022.emnlp-main)

Challenge:	Knowledge distillation is an effective method to transfer knowledge from one network (a.k.a. teacher) to another (as student).
Approach:	They propose to use a mixup data augmentation technique to increase the accuracy of the model by providing better training signals to the student models.
Outcome:	The proposed method improves the calibration of student models while providing better training signals to the student models using training dynamics.

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Tu Vu, Aditya Barua, Brian Lester, Daniel Cer, Mohit Iyyer, Noah Constant

Improving Large-scale Paraphrase Acquisition and Generation (2022.emnlp-main)

Challenge:	generative multilingual models fine-tuned on English forget to generate non-English data when labeled data is only available in English . generative models fine tuned on English fail to generate multilingual summarization tasks when labeling data is available in other languages .
Approach:	They propose to use prompt tuning to overcome catastrophic forgetting in a generative task in . they assume a strict setting with no parallel data or machine translation .
Outcome:	The proposed method can overcome catastrophic forgetting to enable zero-shot cross-lingual generation.

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Yao Dou, Chao Jiang, Wei Xu

Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal (2022.emnlp-main)

Challenge:	Existing Twitter-based paraphrase datasets lack quality definitions for identification and generation tasks.
Approach:	They propose to use two separate definitions of paraphrase for identification and generation tasks in existing Twitter-based paraphrase datasets.
Outcome:	The proposed model achieves state-of-the-art performance of 84.2 F1 for automatic paraphrase identification compared to other models fine-tuned on other corpora such as Quora, MSCOCO, and ParaNMT.

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Byung-Doh Oh, William Schuler

A Survey of Computational Framing Analysis Approaches (2022.emnlp-main)

Challenge:	Transformer-based large language models are trained to make predictions about the next word by aggregating representations of previous tokens through their self-attention mechanism.
Approach:	They propose an entropy-based predictor that quantifies the diffuseness of self-attention and a distance-based one that captures the incremental change in attention patterns across timesteps.
Outcome:	The proposed models perform better over a rigorous baseline including GPT-2 surprisal than previous models that used entropy-based predictors and distance-based ones.

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Mohammad Ali, Naeemul Hassan

Challenge:	Existing computational methods for framing analysis are limited . a lack of a comprehensive understanding of framability is limiting the research .
Approach:	They propose to combine existing approaches to analyze large-scale datasets using computational methods.
Outcome:	The proposed methods will help scholars better understand how frames are being explored computationally, the authors argue .

Learning Cross-Task Dependencies for Joint Extraction of Entities, Events, Event Arguments, and Relations (2022.emnlp-main)

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Minh Van Nguyen, Bonan Min, Franck Dernoncourt, Thien Nguyen

Don’t Copy the Teacher: Data and Model Challenges in Embodied Dialogue (2022.emnlp-main)

Challenge:	Existing work on IE tasks that use two types of dependencies is not optimal . emr, event trigger detection, event argument extraction, and relation extraction are challenging .
Approach:	They propose a model that learns cross-task dependencies from data . they treat each task instance as a node in a dependency graph .
Outcome:	The proposed model outperforms strong baselines over four datasets with different languages.

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So Yeon Min, Hao Zhu, Ruslan Salakhutdinov, Yonatan Bisk

ALFRED-L: Investigating the Role of Language for Action Learning in Interactive Visual Environments (2022.emnlp-main)

Challenge:	Embodied dialogue instruction following requires an agent to complete a complex sequence of tasks from a natural language exchange.
Approach:	They argue that imitation learning and low-level metrics are misleading . they compare existing models with IL and argue evaluation should focus on higher-level semantic goals .
Outcome:	The proposed model evaluations are based on three models and compare them with benchmarks . they show that existing models fail to ground query utterances, which are essential for task completion .

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Arjun Akula, Spandana Gella, Aishwarya Padmakumar, Mahdi Namazifar, Mohit Bansal, Jesse Thomason, Dilek Hakkani-Tur

Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence (2022.emnlp-main)

Challenge:	Embodied Vision and Language Task Completion requires an embodied agent to interpret natural language instructions and egocentric visual observations to navigate through and interact with environments.
Approach:	They propose to use ALFRED to test whether models can adapt to tasks not seen during training that require the same types of language understanding as ALFred.
Outcome:	The proposed model can adapt to tasks that require the same types of language understanding as ALFRED-L.

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Chris Callison-Burch, Gaurav Singh Tomar, Lara Martin, Daphne Ippolito, Suma Bailis, David Reitter

Unsupervised Entity Linking with Guided Summarization and Multiple-Choice Selection (2022.emnlp-main)

Challenge:	researchers have posited Dungeons and Dragons as a challenge problem to test systems on various language-related capabilities.
Approach:	They frame Dungeons and Dragons specifically as a dialogue system challenge . they train a large language model to generate the next game turn, conditioning it on different information.
Outcome:	The proposed game generates the next conversational turn and predicts the state of the game given the dialogue history.

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Young Min Cho, Li Zhang, Chris Callison-Burch

Weakly-Supervised Temporal Article Grounding (2022.emnlp-main)

Challenge:	Entity linking is an important task for language understanding.
Approach:	They propose a fully unsupervised model that generates a guided summary of the contexts conditioning on a mention and then casts the task to a multiple-choice problem.
Outcome:	The proposed model achieves state-of-the-art performance on existing datasets and exiting datasets.

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Long Chen, Yulei Niu, Brian Chen, Xudong Lin, Guangxing Han, Christopher Thomas, Hammad Ayyubi, Heng Ji, Shih-Fu Chang

Exploring Dual Encoder Architectures for Question Answering (2022.emnlp-main)

Challenge:	Existing VG models make unrealistic assumptions about how to ground video segments . a recent study has shown that video grounding can be useful for downstream applications .
Approach:	They propose a new task: Weakly-Supervised temporal Article Grounding (WSAG) given an article and a relevant video, WSAG aims to localize all "groundable" sentences to the video.
Outcome:	The proposed method is simple but effective, and it can be used in real-world applications.

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Zhe Dong, Jianmo Ni, Dan Bikel, Enrique Alfonseca, Yuan Wang, Chen Qu, Imed Zitouni

arXivEdits: Understanding the Human Revision Process in Scientific Writing (2022.emnlp-main)

Challenge:	Dual encoders have been used for question-answering and information retrieval tasks with good results.
Approach:	They propose to use two different versions of dual encoders for QA retrieval tasks . they propose to share parameters in projection layers between two encoder towers .
Outcome:	The proposed architectures outperform SDE and ADE on QA retrieval tasks.

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Chao Jiang, Wei Xu, Samuel Stevens

Challenge:	a new computational framework is developed to study text revision in scientific writing . authors propose a method to extract revision at document-, sentence-, and word-levels .
Approach:	They propose a computational framework for studying text revision in scientific writing . arXivEdits is an annotated corpus of 751 full papers from arX . authors propose to use sentence alignment, fine-grained edits and intents to extract revision .
Outcome:	The proposed framework can be used to study revision in scientific writing.

Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts (2022.emnlp-main)

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Hongli Zhan, Tiberiu Sosea, Cornelia Caragea, Junyi Jessy Li

Analogical Math Word Problems Solving with Enhanced Problem-Solution Association (2022.emnlp-main)

Challenge:	Large-scale crises such as the COVID-19 pandemic cause emotional turmoil worldwide.
Approach:	They propose a method to jointly detect emotions and summarize emotion triggers in social media posts related to COVID-19.
Outcome:	The proposed method can detect emotions and summarize emotions in long social media posts.

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Zhenwen Liang, Jipeng Zhang, Xiangliang Zhang

Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement (2022.emnlp-main)

Challenge:	Analogical reasoning has long been used in mathematical education, as it enables students to apply common relational structures of mathematical situations to solve new problems.
Approach:	They propose to leverage analogical MWPs to advance the solver’s generalization ability across different kinds of MWps.
Outcome:	The proposed model has a stronger generalization ability in solving difficult MWPs due to the analogical learning from easy MWPS.

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Bhavana Dalvi Mishra, Oyvind Tafjord, Peter Clark

Knowledge Transfer from Answer Ranking to Answer Generation (2022.emnlp-main)

Challenge:	Using simulated feedback, our system (called TeachMe) continually improves with time, and without model retraining.
Approach:	They propose to augment a QA model with a dynamic memory of user feedback, containing user-supplied corrections toerroneous model beliefs that users identify during interaction.
Outcome:	The proposed system improves with time and without model retraining, and with real users, by 15% on a hidden test set after teaching.

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Matteo Gabburo, Rik Koncel-Kedziorski, Siddhant Garg, Luca Soldaini, Alessandro Moschitti

Perturbation Augmentation for Fairer NLP (2022.emnlp-main)

Challenge:	Recent studies show that Question Answering (QA) based on Answer Sentence Selection (AS2) can be improved by generating an improved answer from the top-k ranked answer sentences.
Approach:	They propose to train a GenQA model by transferring knowledge from a trained AS2 model . they use top ranked candidate as the generation target and next k top rated candidates as context .
Outcome:	The proposed model outperforms existing models on public and industrial datasets.

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Rebecca Qian, Candace Ross, Jude Fernandes, Eric Michael Smith, Douwe Kiela, Adina Williams

Automatic Document Selection for Efficient Encoder Pretraining (2022.emnlp-main)

Challenge:	Unwanted and often harmful social biases are becoming more salient in NLP research.
Approach:	They propose to train a neural perturbation model that rewrites demographic references in text to make them more fair.
Outcome:	The proposed model outperforms heuristic alternatives on a large dataset of human annotated text perturbations.

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Yukun Feng, Patrick Xia, Benjamin Van Durme, João Sedoc

The Aligned Multimodal Movie Treebank: An audio, video, dependency-parse treebank (2022.emnlp-main)

Challenge:	Pretraining language models is expensive and data-intensive, but can it be improved? Several studies have found that directly pretraining on task data is more effective .
Approach:	They propose to automatically identify smaller yet domain-representative subsets by pretraining a model on a target domain.
Outcome:	The proposed method outperforms random selection on perplexity and downstream tasks with 20x less data and 3x fewer training iterations and 2x less estimated cloud compute cost.

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Adam Yaari, Jan DeWitt, Henry Hu, Bennett Stankovits, Sue Felshin, Yevgeni Berzak, Helena Aparicio, Boris Katz, Ignacio Cases, Andrei Barbu

DEMETR: Diagnosing Evaluation Metrics for Translation (2022.emnlp-main)

Challenge:	Existing treebanks derived from text include only text and are based on single-modality texts.
Approach:	They propose to use audio-visual transcripts and part of speech tags to create an English language treebank based on dialog in Hollywood movies.
Outcome:	The proposed treebank is the 3rd largest UD English treebank and the only multimodal treebank in UD.

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Marzena Karpinska, Nishant Raj, Katherine Thai, Yixiao Song, Ankita Gupta, Mohit Iyyer

Empowering Language Models with Knowledge Graph Reasoning for Open-Domain Question Answering (2022.emnlp-main)

Challenge:	BLEU scores are based on string overlap, but they are opaque in comparison to newer learned metrics.
Approach:	They propose a dataset to evaluate MT evaluation metrics based on linguistic perturbations in English . they find learned metrics perform substantially better than string-based metrics .
Outcome:	The proposed dataset shows that learned metrics perform better than string-based metrics . the dataset contains 31K English examples that cover 35 different linguistic phenomena .

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Ziniu Hu, Yichong Xu, Wenhao Yu, Shuohang Wang, Ziyi Yang, Chenguang Zhu, Kai-Wei Chang, Yizhou Sun

Debiasing Pretrained Text Encoders by Paying Attention to Paying Attention (2022.emnlp-main)

Challenge:	Existing Language Models lack the power to store all required knowledge, resulting in a lack of ability to infer out-of-context knowledge.
Approach:	They propose a Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively.
Outcome:	The proposed model can be plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively.

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Yacine Gaci, Boualem Benatallah, Fabio Casati, Khalid Benabdeslem

MEE: A Novel Multilingual Event Extraction Dataset (2022.emnlp-main)

Challenge:	Recent research has exposed text encoders for replicating discriminatory social biases which may cause unintended and undesired model behaviors with respect to social groups.
Approach:	They propose a method to reduce social stereotypes by redistributing attention scores of a text encoder so it forgets any preference to historically advantaged groups and attends to all social classes with the same intensity.
Outcome:	The proposed method reduces stereotypes and inflicts no semantic damage on pre-trained encoders.

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Amir Pouran Ben Veyseh, Javid Ebrahimi, Franck Dernoncourt, Thien Nguyen

RobustLR: A Diagnostic Benchmark for Evaluating Logical Robustness of Deductive Reasoners (2022.emnlp-main)

Challenge:	Existing methods for Event Extraction are limited for non-English languages . lack of high-quality multilingual datasets has been the main hindrance .
Approach:	They propose a multilingual event extraction dataset that provides annotation for more than 50K event mentions in 8 typologically different languages.
Outcome:	The proposed dataset provides annotation for more than 50K event mentions in 8 languages . the proposed dataset will be publicly available to foster future research .

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Soumya Sanyal, Zeyi Liao, Xiang Ren

Evaluating and Improving Factuality in Multimodal Abstractive Summarization (2022.emnlp-main)

Challenge:	Existing models that perform deductive reasoning on inputs containing rules and statements in the English natural language do not perform consistently on the RobustLR test set.
Approach:	They propose a diagnostic benchmark that evaluates the robustness of language models to minimal logical edits in inputs and different logical equivalence conditions.
Outcome:	The proposed models do not perform consistently on the RobustLR test set.

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David Wan, Mohit Bansal

Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation (2022.emnlp-main)

Challenge:	Current factuality metrics do not account for vision modality, thus are not adequate for vision-and-language summarization.
Approach:	They propose a weighted combination of CLIPScore and BERTScore to evaluate factuality for abstractive document summarization.
Outcome:	The proposed metric outperforms existing factuality metrics on four factuity metric-evaluation benchmarks and is robust to human judgments.

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Melanie Sclar, Peter West, Sachin Kumar, Yulia Tsvetkov, Yejin Choi

Algorithms for Weighted Pushdown Automata (2022.emnlp-main)

Challenge:	a new framework for sentence summarization is available that can be trained reference-free . a high-quality dataset of sentence-summary pairs with varying degrees of compression ratios is obtained .
Approach:	They propose a framework for sentence summarization that can be trained reference-free . they propose 'referee' that iteratively distills latent knowledge into better models .
Outcome:	The proposed framework outperforms existing models in the use of explicit examples from teacher models without compromising the quality of the summarization.

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Alexandra Butoi, Brian DuSell, Tim Vieira, Ryan Cotterell, David Chiang

MABEL: Attenuating Gender Bias using Textual Entailment Data (2022.emnlp-main)

Challenge:	Existing dynamic programming algorithms for PDAs often resort to a PDA-to-CFG conversion.
Approach:	They propose to use a pushdown automaton to reduce the space requirements by a factor of \|Gamma\| or the runtime by reducing the number of states.
Outcome:	The proposed algorithms reduce the space requirements by a factor of \|Gamma\| or reduce the runtime by fewer states.

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Jacqueline He, Mengzhou Xia, Christiane Fellbaum, Danqi Chen

Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs (2022.emnlp-main)

Challenge:	Existing methods for mitigating gender bias in language models are insufficient or inconsistent.
Approach:	They propose a method for attenuating gender bias using entailment labels . they use a contrastive learning objective on counterfactually augmented enanglement pairs .
Outcome:	The proposed method outperforms previous task-agnostic debiasing approaches on intrinsic and extrinsic metrics and preserves task performance after fine-tuning on downstream tasks.

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Kyle Richardson, Ronen Tamari, Oren Sultan, Dafna Shahaf, Reut Tsarfaty, Ashish Sabharwal

Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis (2022.emnlp-main)

Challenge:	a framework for language understanding models to track and improve beliefs through intermediate points in text is needed . breakpoint modeling is an efficient and end-to-end learning approach that trains models to train beliefs . understanding the behavior of models remains a formidable challenge for model safety, authors say .
Approach:	They propose a framework that trains models to track beliefs through intermediate points in text . their framework allows for efficient and robust learning of this type of model .
Outcome:	The proposed model outperforms strong representation learning approaches on a variety of NLU tasks.

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Changyuan Qiu, Winston Wu, Xinliang Frederick Zhang, Lu Wang

Leveraging QA Datasets to Improve Generative Data Augmentation (2022.emnlp-main)

Challenge:	Prior work on ideology prediction has focused on single modalities, i.e., text or images.
Approach:	They propose a task where a model predicts binary or five-point scale ideological leanings given a text-image pair with political content.
Outcome:	The proposed model outperforms the state-of-the-art model by almost 4% and a strong multimodal baseline with no pretraining by over 3%.

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Dheeraj Mekala, Tu Vu, Timo Schick, Jingbo Shang

Meta-Learning Fast Weight Language Models (2022.emnlp-main)

Challenge:	Recent advances in NLP have substantially improved the capability of pretrained language models to generate high-quality text.
Approach:	They propose to reformulate data generation as context generation for a given question-answer (QA) pair and leverage QA datasets for training context generators.
Outcome:	The proposed approach improves performance for few-shot and zero-shot classification datasets on multiple classification dataset.

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Kevin Clark, Kelvin Guu, Ming-Wei Chang, Panupong Pasupat, Geoffrey Hinton, Mohammad Norouzi

CTL++: Evaluating Generalization on Never-Seen Compositional Patterns of Known Functions, and Compatibility of Neural Representations (2022.emnlp-main)

Challenge:	Dynamic evaluation of language models (LMs) adapts model parameters at test time using gradient information from previous tokens.
Approach:	They propose a neural component that uses gradient updates as linear attention to improve model performance.
Outcome:	The proposed model can be applied at training time and learn to make good use of gradient updates.

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Róbert Csordás, Kazuki Irie, Juergen Schmidhuber

Learning with Rejection for Abstractive Text Summarization (2022.emnlp-main)

Challenge:	Existing neural nets fail to generalize systematically due to superficial differences in training data.
Approach:	They propose a new diagnostic dataset based on compositions of unary symbolic functions that tests systematicity of NNs.
Outcome:	The proposed dataset shows that recent CTL-solving Transformer variants fail on CTL++.

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Meng Cao, Yue Dong, Jingyi He, Jackie Chi Kit Cheung

Adaptive Label Smoothing with Self-Knowledge in Natural Language Generation (2022.emnlp-main)

Challenge:	Existing abstractive summarization systems produce non-factual summaries due to noise in the training dataset.
Approach:	They propose a training objective for abstractive summarization based on rejection learning that learns whether or not to reject potentially noisy tokens.
Outcome:	The proposed method significantly improves the factuality of generated summaries in automatic and human evaluations when compared to baseline models.

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Dongkyu Lee, Ka Chun Cheung, Nevin Zhang

Hard Gate Knowledge Distillation - Leverage Calibration for Robust and Reliable Language Model (2022.emnlp-main)

Challenge:	Overconfidence in model generalization and calibration has been shown to impair model generalisation and calibration.
Approach:	They propose a regularization scheme that takes model probability into account and takes it into account . they use a prior label distribution to smooth target labels .
Outcome:	The proposed model improves model generalization and calibration by taking model probability into account.

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Dongkyu Lee, Zhiliang Tian, Yingxiu Zhao, Ka Chun Cheung, Nevin Zhang

Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens (2022.emnlp-main)

Challenge:	Existing knowledge distillation schemes focus on a teacher as a source of knowledge and a gauge to detect miscalibration of a student.
Approach:	They propose a method that uses a teacher model as a source of knowledge and a model as an error detector to detect miscalibration of a student.
Outcome:	The proposed scheme improves model generalization and significantly lowers calibration error.

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Nitish Joshi, Xiang Pan, He He

Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling (2022.emnlp-main)

Challenge:	'spurious correlations' have been used in NLP to informally denote any undesirable feature-label correlations.
Approach:	They formalize this distinction using a causal model and probabilities of necessity and sufficiency, which delineates causal relations between a feature and a label.
Outcome:	The proposed model is invariant to the feature, but not sufficient for prediction.

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Vidhisha Balachandran, Hannaneh Hajishirzi, William Cohen, Yulia Tsvetkov

Coordinated Topic Modeling (2022.emnlp-main)

Challenge:	Abstractive summarization models often generate inconsistent summaries containing factual errors or fabricated content.
Approach:	They propose to generate representative examples of non-factual summaries through infilling language models and train a robust fact-correction model to post-edit them to improve factual consistency.
Outcome:	The proposed model outperforms previous methods in correcting factual errors on two popular summarization datasets.

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Pritom Saha Akash, Jie Huang, Kevin Chen-Chuan Chang

Topic Modeling

Challenge:	Rather than finding arbitrary topics, people often want to explore the text based on some welldefined topics.
Approach:	They propose a problem called coordinated topic modeling that imitates human behavior while describing a text corpus.
Outcome:	The proposed model is superior to baseline models on multiple domains.

Large Dual Encoders Are Generalizable Retrievers (2022.emnlp-main)

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Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, Yinfei Yang

CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question Answering (2022.emnlp-main)

Challenge:	Experimental results show that dual encoders outperform sparse and dense retrievers on the BEIR dataset significantly.
Approach:	They challenge belief that bottleneck layer is too limited for out-of-domain generalization . they scale up the model while keeping bottleneck as a single dot-product with a fixed size .
Outcome:	The proposed model outperforms sparse and dense retrievers on the BEIR dataset significantly.

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Maitreya Patel, Tejas Gokhale, Chitta Baral, Yezhou Yang

Entity-centered Cross-document Relation Extraction (2022.emnlp-main)

Challenge:	Videos often capture objects, their visible properties, their motion, and the interactions between different objects.
Approach:	They propose a video question answering dataset for reasoning about the implicit physical properties of objects in a scene.
Outcome:	The proposed dataset enables evaluation under several out-of-distribution settings – videos with objects with masses, coefficients of friction, and initial velocities that are not observed in the training distribution.

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Fengqi Wang, Fei Li, Hao Fei, Jingye Li, Shengqiong Wu, Fangfang Su, Wenxuan Shi, Donghong Ji, Bo Cai

Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature (2022.emnlp-main)

Challenge:	Existing methods for relation extraction only use text snippets surrounding target entities in multiple documents.
Approach:	They propose a relation-extraction model that uses cross-path entity relation attention to detect the semantic relations between entities in a given text.
Outcome:	The proposed method outperforms the state-of-the-art methods in the dataset CodRED by 10%.

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Katherine Thai, Marzena Karpinska, Kalpesh Krishna, Bill Ray, Moira Inghilleri, John Wieting, Mohit Iyyer

Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding (2022.emnlp-main)

Challenge:	Literary translation is a culturally significant task, but it is bottlenecked by the small number of qualified literary translators . a dataset of non-English language novels is used to study literary MT .
Approach:	They use a dataset of non-English language novels aligned to human and automatic English translations to study literary MT.
Outcome:	The proposed model prefers human translations over machine translations at a rate of 84% . state-of-the-art MT metrics do not correlate with preferences, the study finds .

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Shining Liang, Linjun Shou, Jian Pei, Ming Gong, Wanli Zuo, Xianglin Zuo, Daxin Jiang

Polyglot Prompt: Multilingual Multitask Prompt Training (2022.emnlp-main)

Challenge:	Existing approaches to translate spoken language understanding into low-resource languages are limited to implicit alignment and disregard the inherent semantic structure in SLU.
Approach:	They propose to model utterance-slot-word structure by a multi-level contrastive learning framework . they also propose a label-aware joint model leveraging label semantics to enhance alignment .
Outcome:	The proposed model improves performance on two zero-shot cross-lingual datasets.

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Jinlan Fu, See-Kiong Ng, Pengfei Liu

VisToT: Vision-Augmented Table-to-Text Generation (2022.emnlp-main)

Challenge:	a monolithic framework for multilingual learning can be used without any task/language-specific module.
Approach:	They propose a framework to exploit prompting methods for learning a unified semantic space for different languages and tasks with multilingual prompt engineering.
Outcome:	The proposed framework can learn tasks from different languages in a monolithic framework without any task/language-specific module.

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Prajwal Gatti, Anand Mishra, Manish Gupta, Mithun Das Gupta

Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation (2022.emnlp-main)

Challenge:	Existing models for data-to-text generation are wrongly generating estate in the output text.
Approach:	They propose a task that incorporates visual cues from tables and associated images to generate relevant text.
Outcome:	The proposed task incorporates visual cues from tables and associated images to generate relevant text.

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Xinliang Frederick Zhang, Nick Beauchamp, Lu Wang

Symptom Identification for Interpretable Detection of Multiple Mental Disorders on Social Media (2022.emnlp-main)

Challenge:	a new task is needed to understand the interaction between entities when inferring stances.
Approach:	They propose a task that primes models to identify entities in their canonical names and discern stances jointly.
Outcome:	The proposed model outperforms strong comparisons by large margins.

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Zhiling Zhang, Siyuan Chen, Mengyue Wu, Kenny Zhu

Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks (2022.emnlp-main)

Challenge:	Mental disease detection (MDD) from social media has suffered from poor generalizability and interpretability due to lack of symptom modeling.
Approach:	They propose to annotate a social media corpus of symptom classes related to 7 mental disorders using a knowledge graph and a new annotation framework to facilitate further research.
Outcome:	The proposed model outperforms strong pure-text baselines and provides convincing MDD explanations with case studies.

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Zae Myung Kim, Wanyu Du, Vipul Raheja, Dhruv Kumar, Dongyeop Kang

CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning (2022.emnlp-main)

Challenge:	Iterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness.
Approach:	They propose to build an end-to-end text revision system that can iteratively generate helpful edits by explicitly detecting editable spans with their corresponding edit intents.
Outcome:	The proposed system outperforms baselines on other text revision tasks and human evaluations.

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Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf, Gaurav Singh Tomar

Specializing Multi-domain NMT via Penalizing Low Mutual Information (2022.emnlp-main)

Challenge:	Existing models for conversational question answering require specific retrievers to understand user questions.
Approach:	They develop a query rewriting model CONQRR that rewrites a conversational question into a standalone question.
Outcome:	The proposed model achieves state-of-the-art on an open-domain conversational question answering dataset and is effective for two different off-the shelf retrievers.

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Jiyoung Lee, Hantae Kim, Hyunchang Cho, Edward Choi, Cheonbok Park

A Simple Contrastive Learning Framework for Interactive Argument Pair Identification via Argument-Context Extraction (2022.emnlp-main)

Challenge:	Multi-domain Neural Machine Translation (NMT) trains a single model with multiple domains.
Approach:	They propose a method that penalizes low MI to be higher for domain-specific NMTs.
Outcome:	The proposed method achieves state-of-the-art performance among current models . it also promotes low MI to be higher resulting in domain-specialized multi-domain NMT.

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Lida Shi, Fausto Giunchiglia, Rui Song, Daqian Shi, Tongtong Liu, Xiaolei Diao, Hao Xu

Sentence-level Media Bias Analysis Informed by Discourse Structures (2022.emnlp-main)

Challenge:	Existing work on argument mining uses context-based methods to identify whether two arguments are interactively related.
Approach:	They propose a contrastive learning framework to extract valuable information from the context.
Outcome:	The proposed framework achieves state-of-the-art performance on the benchmark dataset and visually displays more compact representations.

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Yuanyuan Lei, Ruihong Huang, Lu Wang, Nick Beauchamp

Towards Efficient Dialogue Pre-training with Transferable and Interpretable Latent Structure (2022.emnlp-main)

Challenge:	Recent work on detecting media bias at the level of individual articles is limited to single sentences.
Approach:	They propose to use a news discourse structure and PDTB discourse relations to identify bias sentences within an article that can illuminate and explain the overall bias of the entire article.
Outcome:	The proposed model can detect bias at the level of individual articles and a single sentence can explain it.

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Xueliang Zhao, Lemao Liu, Tingchen Fu, Shuming Shi, Dongyan Zhao, Rui Yan

An Empirical Revisiting of Linguistic Knowledge Fusion in Language Understanding Tasks (2022.emnlp-main)

Challenge:	Existing models that use millions of parameters on massive data are inefficient and lack interpretability.
Approach:	They propose a model with a latent structure that is easily transferable from the general domain to downstream tasks in a lightweight and transparent way.
Outcome:	The proposed model performs better than four strong baseline models in terms of automatic and human evaluations and is 5x faster than the strongest baseline model.

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Changlong Yu, Tianyi Xiao, Lingpeng Kong, Yangqiu Song, Wilfred Ng

Unsupervised Non-transferable Text Classification (2022.emnlp-main)

Challenge:	Recent work attempts to explicitly incorporate human-defined linguistic priors into fine-tuning tasks.
Approach:	They replace parsed graphs or trees with trivial ones to investigate linguistic priors . they propose to use trivial graphs as baselines to design advanced knowledge fusion methods .
Outcome:	The use of trivial graphs improves performance in fully-supervised and few-shot settings.

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Guangtao Zeng, Wei Lu

Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Prediction (2022.emnlp-main)

Challenge:	Existing methods to train a good deep learning model require labeled data for the target domain which can be difficult to obtain.
Approach:	They propose an unsupervised non-transferable learning method that does not require annotated target domain data and introduce a secret key component for recovering the model’s access to the target domain.
Outcome:	The proposed method reduces model generalization ability in specific target domains while still recovering access to the target domain.

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Thong Nguyen, Xiaobao Wu, Anh Tuan Luu, Zhen Hai, Lidong Bing

Adaptive Token-level Cross-lingual Feature Mixing for Multilingual Neural Machine Translation (2022.emnlp-main)

Challenge:	Modern review helpfulness prediction systems focus on polishing cross-modal representations and suffer from inferior optimization.
Approach:	They propose a method to polish cross-modal relation representations by learning mutual information through contrastive learning.
Outcome:	The proposed framework outperforms baselines and achieves state-of-the-art results on two publicly available datasets.

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Junpeng Liu, Kaiyu Huang, Jiuyi Li, Huan Liu, Jinsong Su, Degen Huang

A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach (2022.emnlp-main)

Challenge:	Multilingual neural machine translation models can translate multiple language pairs in a single model but lacks ability to capture language-specific features.
Approach:	They propose a token-level feature mixing method that captures different features and dynamically determines feature sharing across languages.
Outcome:	The proposed method outperforms baselines and can be extended to zero-shot translation.

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Yew Ken Chia, Lidong Bing, Sharifah Mahani Aljunied, Luo Si, Soujanya Poria

Knowledge Representation

Challenge:	Existing methods do not consider qualifier attributes for each relation triplet, such as time, quantity or location.
Approach:	They propose a hyper-relational extraction task to extract more specific facts from text using qualifiers.
Outcome:	The proposed model outperforms baselines and reveal possible directions for future research.

Low-resource Neural Machine Translation with Cross-modal Alignment (2022.emnlp-main)

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Zhe Yang, Qingkai Fang, Yang Feng

Prompt-based Distribution Alignment for Domain Generalization in Text Classification (2022.emnlp-main)

Challenge:	Existing neural machine translation techniques rely on large monolingual corpus, which is costly for some low-resource languages.
Approach:	They propose a cross-modal contrastive learning method to learn a shared space for all languages by additional visual modality.
Outcome:	The proposed method can learn cross-modal and cross-lingual alignment with small amount of image-text pairs and achieves significant improvements over the text-only baseline.

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Chen Jia, Yue Zhang

Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering (2022.emnlp-main)

Challenge:	Pretrained language models (PLMs) have achieved competitive performance on a range of NLP tasks.
Approach:	They propose to learn distributional invariance across source domains via alignment regularization loss functions to improve domain generalization by prompting.
Outcome:	Experiments on sentiment analysis and natural language inference show the effectiveness of the proposed method and achieve state-of-the-art results.

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Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, Soujanya Poria

HEGEL: Hypergraph Transformer for Long Document Summarization (2022.emnlp-main)

Challenge:	Existing approaches to multi-choice question answering are based on binary classifications instead of scoring each answer as a single class.
Approach:	They propose a simple refactoring of multi-choice question answering tasks as a series of binary classifications and propose re-framing to make them more efficient.
Outcome:	The proposed approach is significantly more effective across different tasks and models.

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Haopeng Zhang, Xiao Liu, Jiawei Zhang

Adapting a Language Model While Preserving its General Knowledge (2022.emnlp-main)

Challenge:	Abstract: Extractive summarization for long documents is challenging due to the extended structured input context.
Approach:	They propose a hypergraph neural network for extractive summarization by capturing cross-sentence relations.
Outcome:	The proposed model can capture cross-sentence relations and latent topics and keywords coreference, and section structure, and can be applied to scientific papers.

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Zixuan Ke, Yijia Shao, Haowei Lin, Hu Xu, Lei Shu, Bing Liu

Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation (2022.emnlp-main)

Challenge:	Existing DA-training methods do not explicitly identify what knowledge should be preserved and what should be changed by the domain corpus.
Approach:	They propose to use an unlabeled corpus of aparticular domain to train a pre-trained general-purpose language model to adapt the LM so that end-tasks in the domain can give improved performances.
Outcome:	The proposed method improves the performance of pre-trained general-purpose language models by contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and specific knowledge.

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Raymond Li, Wen Xiao, Linzi Xing, Lanjun Wang, Gabriel Murray, Giuseppe Carenini

Continual Training of Language Models for Few-Shot Learning (2022.emnlp-main)

Challenge:	Existing studies have investigated the multi-head self-attention mechanism of transformers.
Approach:	They propose to use a human-in-the-loop pipeline to discover task-specific attention patterns and inject them into transformer models to improve their accuracy.
Outcome:	The proposed methods improve the performance of transformer models by incorporating predefined patterns into their attention matrices.

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Zixuan Ke, Haowei Lin, Yijia Shao, Hu Xu, Lei Shu, Bing Liu

Dictionary-Assisted Supervised Contrastive Learning (2022.emnlp-main)

Challenge:	Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications.
Approach:	They propose to continuously post-train an LM with unlabeled domains to expand its knowledge without forgetting previous skills.
Outcome:	The proposed system improves few-shot end-task learning in these domains.

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Patrick Y. Wu, Richard Bonneau, Joshua A. Tucker, Jonathan Nagler

Fine-Tuning Pre-trained Transformers into Decaying Fast Weights (2022.emnlp-main)

Challenge:	specialized dictionaries are used in text analysis to reason with abstract concepts . recent supervised machine learning approaches do not use these dictionary .
Approach:	They propose a dictionary-assisted contrastive learning objective that leverages dictionaries when fine-tuning pretrained language models.
Outcome:	The proposed approach improves classification performance metrics in few-shot learning settings and social science applications compared to cross-entropy alone and alternative contrastive and data augmentation methods.

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Huanru Henry Mao

PRO-CS : An Instance-Based Prompt Composition Technique for Code-Switched Tasks (2022.emnlp-main)

Challenge:	Autoregressive Transformers incur O(T) complexity during per-token generation due to the self-attention mechanism.
Approach:	They propose a kernel-based method to approximate causal self-attention by replacing it with recurrent formulations with various update rules and feature maps to achieve O(1) time and memory complexity.
Outcome:	The proposed method outperforms prior methods and retains 99% of attention’s performance on WikiText-103 against more complex attention substitutes.

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Srijan Bansal, Suraj Tripathi, Sumit Agarwal, Teruko Mitamura, Eric Nyberg

SentBS: Sentence-level Beam Search for Controllable Summarization (2022.emnlp-main)

Challenge:	Code-switching (CS) data is ubiquitous in today’s globalized world, but the dearth of annotated datasets in code-switch tasks poses a significant challenge for transfer learning in limited-resource setups.
Approach:	They propose a prompt composition technique that outperforms prompt-tuning and fine-tuned prompt-based prompt composition techniques for CS tasks that combine language and task knowledge.
Outcome:	The proposed approach outperforms prompt-tuning and fine-tuned approaches on 10 datasets across 4 languages and achieves competitive results in low-resource cross-lingual and cross-task setting.

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Chenhui Shen, Liying Cheng, Lidong Bing, Yang You, Luo Si

A Fine-grained Chinese Software Privacy Policy Dataset for Sequence Labeling and Regulation Compliant Identification (2022.emnlp-main)

Challenge:	Structure-controlled summarization is a useful and interesting research direction . current structure-controlling methods have limited effectiveness in enforcing the desired structure.
Approach:	They propose a sentence-level beam search generation method to select suitable sentences for subsequent generations.
Outcome:	The proposed method significantly reduces structural discrepancies by 68% on a structure-controlled dataset.

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Kaifa Zhao, Le Yu, Shiyao Zhou, Jing Li, Xiapu Luo, Yat Fei Aemon Chiu, Yutong Liu

Saving Dense Retriever from Shortcut Dependency in Conversational Search (2022.emnlp-main)

Challenge:	Existing datasets that ignore law requirements are limited to English.
Approach:	They construct a Chinese privacy policy dataset that can be used to analyze software privacy policies.
Outcome:	The proposed dataset includes 483 Chinese Android privacy policies, over 11K sentences, and 52K fine-grained annotations.

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Sungdong Kim, Gangwoo Kim

Graph-Induced Transformers for Efficient Multi-Hop Question Answering (2022.emnlp-main)

Challenge:	a retrieval shortcut in conversational search (CS) relies on partial history to retrieve relevant passages . naively trained dense retrievers heavily exploit the shortcut and perform poorly when asked to answer history-independent questions.
Approach:	They propose to exploit a retrieval shortcut in conversational search (CS) that allows models to only use partial history to retrieve relevant passages while disregarding the latest question.
Outcome:	The proposed model outperforms the previous state-of-the-art model by 11.0 on QReCC.

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Giwon Hong, Jeonghwan Kim, Junmo Kang, Sung-Hyon Myaeng

DiscoSense: Commonsense Reasoning with Discourse Connectives (2022.emnlp-main)

Challenge:	Recent MHQA tasks that require inter-paragraph/sentence linkages use graphs to model internal structural information within text.
Approach:	They propose a graph-induced transformer that applies graph-derived attention patterns directly into a PLM without external graph modules.
Outcome:	The proposed model can replace external graph modules while preserving model performance.

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Prajjwal Bhargava, Vincent Ng

Boosting Document-Level Relation Extraction by Mining and Injecting Logical Rules (2022.emnlp-main)

Challenge:	DiscoSense is a benchmark for commonsense reasoning using a wide variety of discourse connectives.
Approach:	They propose a benchmark for commonsense reasoning by understanding a wide variety of discourse connectives.
Outcome:	The proposed benchmark outperforms existing benchmarks on commonsense reasoning tasks.

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Shengda Fan, Shasha Mo, Jianwei Niu

MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective (2022.emnlp-main)

Challenge:	Document-level relation extraction (DocRE) aims to extract relations of all entity pairs in document.
Approach:	They propose a logic enhanced framework that boosts DocRE by mining and injecting logic rules.
Outcome:	The proposed framework outperforms LogiRE on two benchmarks.

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Zhe Hu, Hou Pong Chan, Lifu Huang

Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation (2022.emnlp-main)

Challenge:	Recent pre-trained language models have produced impressive results, but there is still a gap between human written texts and machine-generated outputs.
Approach:	They propose a multi-task training strategy for long text generation grounded on the cognitive theory of writing.
Outcome:	The proposed model achieves better results on three open-ended generation tasks than baselines.

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Zhuang Li, Lizhen Qu, Qiongkai Xu, Tongtong Wu, Tianyang Zhan, Gholamreza Haffari

CISLR: Corpus for Indian Sign Language Recognition (2022.emnlp-main)

Challenge:	Existing models for task-specific natural language generation do not contain any labeled examples.
Approach:	They propose a variational autoencoder with disentanglement priors for task-specific natural language generation with none or a handful of task-related labeled examples.
Outcome:	The proposed model outperforms baseline models in terms of data augmentation and text style transfer in the few-shot setting.

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Abhinav Joshi, Ashwani Bhat, Pradeep S, Priya Gole, Shashwat Gupta, Shreyansh Agarwal, Ashutosh Modi

Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction (2022.emnlp-main)

Challenge:	Existing work on natural language processing has shown promising improvements in text classification, translation and generation in widely used spoken languages.
Approach:	They propose a new Indian Sign Language corpus for word-level recognition using videos . they propose CISLR model that leverages resource rich American Sign Language to learn generalized features for improving Indian Sign language predictions.
Outcome:	The proposed model improves word recognition in Indian Sign Language using video . it leverages resource rich American Sign Language to learn generalized features .

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Kai Shen, Yichong Leng, Xu Tan, Siliang Tang, Yuan Zhang, Wenjie Liu, Edward Lin

AMAL: Meta Knowledge-Driven Few-Shot Adapter Learning (2022.emnlp-main)

Challenge:	Text error correction methods usually use the source (incorrect) sentence as encoder input and generate the target (correct) sentences through the decoder.
Approach:	They propose a method to correct errors in text sequences by randomly masking out the correct tokens in the source sentence.
Outcome:	The proposed method improves accuracy on Mandarin and English datasets with autoregressive and non-autoregressive generation models.

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S. K. Hong, Tae Young Jang

Discourse Context Predictability Effects in Hindi Word Order (2022.emnlp-main)

Challenge:	Existing methods for fine-tuning pre-trained language models fail to yield meaningful results in the few-shot regime.
Approach:	They propose a meta-learning-driven low-rank adapter pooling method for leveraging pre-trained language models even with just a few data points.
Outcome:	The proposed method outperforms previous few-shot learning methods on five text classification benchmark datasets.

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Sidharth Ranjan, Marten van Schijndel, Sumeet Agarwal, Rajakrishnan Rajkumar

“Covid vaccine is against Covid but Oxford vaccine is made at Oxford!” Semantic Interpretation of Proper Noun Compounds (2022.emnlp-main)

Challenge:	Prior work has shown that information status, dependency length, and syntactic surprisal influence word order preferences, but the role of discourse predictability is underexplored in the literature.
Approach:	They propose to use Hindi-Urdu Treebank corpus to build a classifier to predict which sentences actually occurred in the corpus against artificially generated distractors.
Outcome:	The proposed classifier predicts which sentences occur in the Hindi-Urdu Treebank corpus against artificial distractors.

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Keshav Kolluru, Gabriel Stanovsky, null Mausam

Context Limitations Make Neural Language Models More Human-Like (2022.emnlp-main)

Challenge:	Proper noun compounds are used in short-form domains but are largely ignored in information-seeking applications.
Approach:	They propose to annotate a manually annotated dataset of 22.5K proper noun compounds . they use supervised learning to generate interpretations from the compounds based on target knowledge .
Outcome:	The proposed dataset is 60 times larger than prior noun compound datasets and includes non-compositional examples.

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Tatsuki Kuribayashi, Yohei Oseki, Ana Brassard, Kentaro Inui

A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism (2022.emnlp-main)

Challenge:	Language models (LMs) have been used in cognitive modeling and engineering studies to simulate human cognitive load during reading.
Approach:	They propose to constrain LMs' context access to improve their simulation of human reading behavior by incorporating syntactic biases into their context access.
Outcome:	The proposed model improves the simulation of human reading behavior by incorporating syntactic biases into their context access.

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Jianzhu Bao, Yuhang He, Yang Sun, Bin Liang, Jiachen Du, Bing Qin, Min Yang, Ruifeng Xu

Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality (2022.emnlp-main)

Challenge:	Argument mining (AM) is a challenging task as it requires recognizing complex argumentation structures involving multiple subtasks.
Approach:	They propose a generative framework where expected outputs of AM are framed as a simple target sequence.
Outcome:	The proposed framework achieves state-of-the-art on two AM benchmarks.

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Pei Zhou, Hyundong Cho, Pegah Jandaghi, Dong-Ho Lee, Bill Yuchen Lin, Jay Pujara, Xiang Ren

FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows (2022.emnlp-main)

Challenge:	Currently, human communication models fail to explicitly model common ground (CG) . less than half of the responses in current data is rated as high quality .
Approach:	They propose a dataset that annotates dialogues with explicit CG and solicits 9k diverse responses each following one common ground.
Outcome:	The proposed dataset annotates dialogues with explicit CG and solicits 9k diverse responses each following one common ground.

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Jianqiao Zhao, Yanyang Li, Wanyu Du, Yangfeng Ji, Dong Yu, Michael Lyu, Liwei Wang

FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning (2022.emnlp-main)

Challenge:	Despite recent progress in dialogue evaluation, how to develop automatic metrics remains an open problem.
Approach:	They propose a consensus-based framework for dialog evaluation using segment act flows . they propose to crowdsource a large-scale dataset for it to be evaluated .
Outcome:	The proposed framework can reach the best or comparable correlation with human evaluation.

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Suvir Mirchandani, Licheng Yu, Mengjiao Wang, Animesh Sinha, Wenwen Jiang, Tao Xiang, Ning Zhang

MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences (2022.emnlp-main)

Challenge:	Prior work on multimodal fashion tasks has been limited by the data in individual benchmarks or has leveraged generic vision-and-language pre-training but have not taken advantage of the characteristics of fashion data.
Approach:	They propose a fashion-specific pre-training framework based on weakly-supervised triplets constructed from fashion image-text pairs.
Outcome:	The proposed framework is based on weakly-supervised triplets constructed from fashion image-text pairs and is competitive on a diverse set of fashion tasks.

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Wei Han, Hui Chen, Min-Yen Kan, Soujanya Poria

Evaluating the Knowledge Dependency of Questions (2022.emnlp-main)

Challenge:	Existing approaches to multimodal learning assume a complete input modality setting, i.e., each modality is either complete or completely missing in both training and test sets.
Approach:	They propose an alignment dynamics learning module based on the theory of optimal transport for missing data imputation and a denoising training algorithm to enhance the quality of iputation and accuracy of model predictions.
Outcome:	The proposed method performs faster and more accurate inferences under different missing conditions and alleviates the overfitting issue.

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Hyeongdon Moon, Yoonseok Yang, Hangyeol Yu, Seunghyun Lee, Myeongho Jeong, Juneyoung Park, Jamin Shin, Minsam Kim, Seungtaek Choi

Challenge:	Existing evaluation metrics for MCQ generation focus on the n-gram based similarity of the generated MCq to the gold sample and disregard their educational value.
Approach:	They propose to use a human survey to measure the MCQ’s answerability given knowledge of the target fact.
Outcome:	The proposed methods measure the MCQ’s answerability given knowledge of the target fact.

MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion (2022.emnlp-main)

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Yu Zhao, Xiangrui Cai, Yike Wu, Haiwei Zhang, Ying Zhang, Guoqing Zhao, Ning Jiang

Entropy-Based Vocabulary Substitution for Incremental Learning in Multilingual Neural Machine Translation (2022.emnlp-main)

Challenge:	Existing methods to predict missing entities share relation representation across modalities, which results in mutual interference between modality.
Approach:	They propose a framework for multimodal knowledge graph completion that learns modality-split relation embeddings for each modality instead of a single modality shared one.
Outcome:	The proposed framework outperforms state-of-the-art methods on three KG datasets.

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Kaiyu Huang, Peng Li, Jin Ma, Yang Liu

Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation (2022.emnlp-main)

Challenge:	Existing methods to update a multilingual model with new language pairs are expensive and time-consuming.
Approach:	They propose an entropy-based vocabulary substitution method that walks through new language pairs for incremental learning while remaining the size of the original vocabulary.
Outcome:	The proposed method achieves better performance and saves excess overhead in a multilingual machine translation task.

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Yanyang Li, Jianqiao Zhao, Michael Lyu, Liwei Wang

An Unsupervised, Geometric and Syntax-aware Quantification of Polysemy (2022.emnlp-main)

Challenge:	Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text.
Approach:	They propose a posterior-based reweighing and noisy training strategy to exploit generated knowledge in dialogue generation.
Outcome:	Empirical results show that the proposed methods outperform the state-of-the-art methods in unsupervised knowledge-grounded conversation.

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Anmol Goel, Charu Sharma, Ponnurangam Kumaraguru

Reorder and then Parse, Fast and Accurate Discontinuous Constituency Parsing (2022.emnlp-main)

Challenge:	Polysemy is the phenomenon where a single word form possesses two or more related senses.
Approach:	They propose an unsupervised framework to quantify polysemy for words in multiple languages . they use syntactic knowledge to infuse the framework with syntaktic knowledge .
Outcome:	The proposed framework is based on syntactic knowledge and is compared with existing methods in English, French and Spanish.

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Kailai Sun, Zuchao Li, Hai Zhao

Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature (2022.emnlp-main)

Challenge:	Discontinuous constituency parsing is still being developed for its efficiency and accuracy are far behind its continuous counterparts.
Approach:	They propose to transform a discontinuous constituent tree into a pseudo-continuous one by reordering words in the sentence.
Outcome:	The proposed method can transform a discontinuous constituent tree into a pseudo-continuous one by parsing and performing actions on three classical discontinuous constituency treebanks.

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Tomas Goldsack, Zhihao Zhang, Chenghua Lin, Carolina Scarton

Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language Inference (2022.emnlp-main)

Challenge:	Existing datasets for lay summarisation are limited in size and scope, hindering the development of data-driven approaches.
Approach:	They propose to use two new datasets for the lay summarisation of biomedical research articles to characterise their lay summaries.
Outcome:	The proposed datasets are compared with existing datasets and show they can be leveraged to support different audiences and applications.

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Sara Rajaee, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar

An Empirical Study on the Transferability of Transformer Modules in Parameter-efficient Fine-tuning (2022.emnlp-main)

Challenge:	Existing methods for debiasing are ineffective in addressing the reverse word-overlap bias.
Approach:	They propose to investigate the reverse word-overlap bias in NLI models . they find that existing debiasing methods are generally ineffective .
Outcome:	The proposed model is biased towards the non-entailment label on instances with low overlap . the proposed model does not have minority examples, the authors show .

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Mohammad AkbarTajari, Sara Rajaee, Mohammad Taher Pilehvar

CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking (2022.emnlp-main)

Challenge:	Parameter-efficient fine-tuning is a computationally expensive process . introducing new parameters to an already-large model can be considered a drawback.
Approach:	They investigate the capability of different transformer modules in transferring knowledge from a pre-trained model to a downstream task.
Outcome:	The proposed methods show that each transformer module is a winning ticket . they show that with only 0.003% updateable parameters, they can show acceptable performance on target tasks.

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George Zerveas, Navid Rekabsaz, Daniel Cohen, Carsten Eickhoff

AdapterShare: Task Correlation Modeling with Adapter Differentiation (2022.emnlp-main)

Challenge:	Contextual document embedding reranking is an efficient and efficient retrieval framework.
Approach:	They propose a highly efficient retrieval framework that uses contextual document embedding reranking to incorporate ranking context into training.
Outcome:	The proposed framework reduces the computational overhead of a first-stage method and can be used as stand-alone retrieval models.

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Zhi Chen, Bei Chen, Lu Chen, Kai Yu, Jian-Guang Lou

Rethinking Task-Specific Knowledge Distillation: Contextualized Corpus as Better Textbook (2022.emnlp-main)

Challenge:	AdapterShare is an adapter differentiation method to explicitly model the task correlation among multiple tasks.
Approach:	They propose an adapter differentiation method to explicitly model the task correlation among multiple tasks.
Outcome:	The proposed method achieves 1.90 points improvement on five dialogue understanding tasks and 2.33 points gain on NLU tasks.

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Chang Liu, Chongyang Tao, Jianxin Liang, Tao Shen, Jiazhan Feng, Quzhe Huang, Dongyan Zhao

Recovering Gold from Black Sand: Multilingual Dense Passage Retrieval with Hard and False Negative Samples (2022.emnlp-main)

Challenge:	Existing methods for knowledge distillation use a two-stage paradigm: general distillation with a task-agnostic general corpus and task-specific distillation using augmented task- specific corpus.
Approach:	They propose a contextualized corpus that contextualizes task corpus with large-scale general corpus through relevance-based text retrieval to improve student learning.
Outcome:	The proposed model improves on the GLUE benchmark and shows that it is better than generalized corpus and augmented task-specific corpus.

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Tianhao Shen, Mingtong Liu, Ming Zhou, Deyi Xiong

The “Problem” of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation (2022.emnlp-main)

Challenge:	Existing approaches to recover and utilize negative samples have not been effective on all languages.
Approach:	They propose a multilingual dense passage retrieval framework to recover and utilize negative samples.
Outcome:	The proposed framework outperforms strong sparse, dense and hybrid baselines on a high-quality dataset covering eleven typologically diverse languages.

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Barbara Plank

Quality Scoring of Source Words in Neural Translation Models (2022.emnlp-main)

Challenge:	a paper argues that human label variation impacts all stages of the ML pipeline . human label variations are often considered noise due to disagreement, subjectivity in annotation or multiple plausible answers.
Approach:	They propose to reconcile different notions of human label variation and propose a repository of publicly-available datasets with un-aggregated labels.
Outcome:	The proposed approaches are compared with publicly available datasets with un-aggregated labels and identify gaps.

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Priyesh Jain, Sunita Sarawagi, Tushar Tomar

Pneg: Prompt-based Negative Response Generation for Dialogue Response Selection Task (2022.emnlp-main)

Challenge:	Recent approaches to improving word-level quality scores on input source sentences require training special word-scoring models or require repeated invocation of the translation model.
Approach:	They propose to reason how well each word is explained by the target sentence as against the source language model and use it to translate into an unfamiliar target language.
Outcome:	The proposed method provides up to five points higher F1 scores and is significantly faster than the state of the art methods on three language pairs.

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Nyoungwoo Lee, ChaeHun Park, Ho-Jin Choi, Jaegul Choo

Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations (2022.emnlp-main)

Challenge:	Existing methods for synthesizing adversarial negative responses are limited by their scalability and cost.
Approach:	They propose a method for generating adversarial negative responses using a large-scale language model.
Outcome:	The proposed method outperforms other methods on dialogue selection tasks.

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Wanqiu Long, Bonnie Webber

Simplified Graph Learning for Inductive Short Text Classification (2022.emnlp-main)

Challenge:	Recent studies on implicit discourse relation recognition have shown great success.
Approach:	They incorporate the sense hierarchy into the recognition process and use it to select negative examples.
Outcome:	The proposed approach achieves state-of-the-art performance on the task with no additional effort.

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Kaixin Zheng, Yaqing Wang, Quanming Yao, Dejing Dou

Don’t Stop Fine-Tuning: On Training Regimes for Few-Shot Cross-Lingual Transfer with Multilingual Language Models (2022.emnlp-main)

Challenge:	Existing methods for short text classification are limited and lack of labeled data is not enough.
Approach:	They propose a novel short text classification algorithm which leverages words to handle the lack of labeled data.
Outcome:	The proposed model performs better with lower memory consumption and faster inference speed than previous models.

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Fabian David Schmidt, Ivan Vulić, Goran Glavaš

Towards Compositional Generalization in Code Search (2022.emnlp-main)

Challenge:	Recent work highlights the fallacies of zero-shot cross-lingual transfer with large multilingual models.
Approach:	They propose to replace sequential fine-tuning with joint fine-uning on source and target language instances.
Outcome:	The proposed techniques yield improved and more stable FS-XLT across the board.

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Hojae Han, Seung-won Hwang, Shuai Lu, Nan Duan, Seungtaek Choi

Towards relation extraction from speech (2022.emnlp-main)

Challenge:	Existing code search models that focus on code as an unstructured sequence fail to generalize when the lexical perturbation without changing structures and labels is applied in test codes.
Approach:	They propose a compositional generalization model that extracts structural elements and a code template that targets compositional genericization.
Outcome:	The proposed model is complementary to flow graphs in GraphCodeBERT, by enhancing structural context around variables.

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Tongtong Wu, Guitao Wang, Jinming Zhao, Zhaoran Liu, Guilin Qi, Yuan-Fang Li, Gholamreza Haffari

Structural Constraints and Natural Language Inference for End-to-End Flowchart Grounded Dialog Response Generation (2022.emnlp-main)

Challenge:	Existing methods for extracting relations from speech have been neglected due to the nature of speech.
Approach:	They propose a listening information extraction task that uses speech to extract relation extraction from speech . they use a text-to-speech system and crowd-sourced native English speakers to test the task .
Outcome:	The proposed task extracts semantic relationships from speech data using a new model . the proposed task is more challenging than the existing method due to the characteristics of speech .

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Dinesh Raghu, Suraj Joshi, Sachindra Joshi, null Mausam

SLICER: Sliced Fine-Tuning for Low-Resource Cross-Lingual Transfer for Named Entity Recognition (2022.emnlp-main)

Challenge:	Existing approaches to learn flowchart grounded dialogs have two limitations . Flowchart-based systems require only the chat transcripts and no additional annotations .
Approach:	They propose a structure-aware approach to learn flowchart grounded dialogs . it uses structural constraints derived from connectivity structure of flowchartes into a RAG framework .
Outcome:	The proposed approach outperforms existing approaches with a success rate of 68% and 123%.

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Fabian David Schmidt, Ivan Vulić, Goran Glavaš

EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation (2022.emnlp-main)

Challenge:	Large multilingual models fail to successfully transfer to low-resource languages for zero-shot cross-lingual transfer . sliced fine-tuning for named entity recognition (SLICER) forces stronger token contextualization in the Transformer.
Approach:	They propose a simple yet highly effective approach for improving zero-shot cross-lingual transfer for named entity recognition to low-resource languages.
Outcome:	The proposed approach reduces decontextualization of token representations and classifiers . it yields consistent transfer gains for low-resource languages, the authors show .

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Tao Ge, Si-Qing Chen, Furu Wei

End-to-End Unsupervised Vision-and-Language Pre-training with Referring Expression Matching (2022.emnlp-main)

Challenge:	Extensive experiments show EdgeFormer can effectively outperform previous parameter-efficient Transformer baselines and achieve competitive results under both the computation and memory constraints.
Approach:	They propose a parameter-efficient Transformer for on-device seq2seq generation that uses two novel principles for cost-effective parameterization.
Outcome:	Extensive experiments show that EdgeFormer outperforms the previous parameter-efficient Transformers and achieves competitive results under both the computation and memory constraints.

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Chi Chen, Peng Li, Maosong Sun, Yang Liu

Faithful Knowledge Graph Explanations in Commonsense Question Answering (2022.emnlp-main)

Challenge:	Existing unsupervised vision-and-language pre-training methods take pre-extracted region-based visual features from external object detectors, which limits flexibility and reduces computational efficiency.
Approach:	They propose an unsupervised vision-and-language pre-training task that predicts which patches contain an object referred to in natural language from the encoded visual features.
Outcome:	The proposed approach outperforms existing methods and obtains state-of-the-art results on four vision-and-language tasks.

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Guy Aglionby, Simone Teufel

KOLD: Korean Offensive Language Dataset (2022.emnlp-main)

Challenge:	Knowledge graphs are used to express explanations for the model's answer choice.
Approach:	They propose to use knowledge graphs to encode facts separately from the question and combine them to select an answer.
Outcome:	The proposed architectures can be used to express the facts used to answer a question in a graph-based explanation, but they will not include reasoning done by the transformer encoding the question, and will be incomplete.

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Younghun Jeong, Juhyun Oh, Jongwon Lee, Jaimeen Ahn, Jihyung Moon, Sungjoon Park, Alice Oh

Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating Attention (2022.emnlp-main)

Challenge:	Recent directions for offensive language detection focus on English and do not transfer well to other languages because of cultural and linguistic differences.
Approach:	They present a Korean offensive language dataset annotated with offensive language comments . they use the comments as training data for Korean BERT and RoBERTa models .
Outcome:	The proposed model improves offensiveness detection, target classification, and span detection while having room for improvement for target group classification and span prediction.

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Wenhao Li, Xiaoyuan Yi, Jinyi Hu, Maosong Sun, Xing Xie

The better your Syntax, the better your Semantics? Probing Pretrained Language Models for the English Comparative Correlative (2022.emnlp-main)

Challenge:	Recent studies have shown that powerful Transformer architectures produce dull high-frequency phrases, severely hurting the diversity and novelty of generated text.
Approach:	They propose a method to control the sharpness of the attention distribution by python code and use it to learn a Bayesian approximation of posterior attention.
Outcome:	The proposed method improves diversity and novelty while maintaining comparable quality on conditional and unconditional generation tasks.

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Leonie Weissweiler, Valentin Hofmann, Abdullatif Köksal, Hinrich Schütze

ProofInfer: Generating Proof via Iterative Hierarchical Inference (2022.emnlp-main)

Challenge:	Construction Grammar posits constructions as the central building blocks of language . human-like performance of pretrained language models on many NLP tasks has been alleged .
Approach:	They propose to use construction grammar to posit constructions as the central building blocks of language . they conduct experiments with three pretrained language models to examine their ability to classify and understand English comparative correlative .
Outcome:	The proposed models are able to recognise the English comparative correlative (CC) but fail to use its meaning.

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Zichu Fei, Qi Zhang, Xin Zhou, Tao Gui, Xuanjing Huang

ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts (2022.emnlp-main)

Challenge:	Existing proof generation models focus on generating several proof paths instead of a whole tree.
Approach:	They propose a method that generates the proof tree via iterative hierarchical inference . they propose coding the proof as plain text without losing structure information .
Outcome:	The proposed proof generation model significantly improves performance on widely-used datasets.

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Rajdeep Mukherjee, Abhinav Bohra, Akash Banerjee, Soumya Sharma, Manjunath Hegde, Afreen Shaikh, Shivani Shrivastava, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal

Cross-domain Generalization for AMR Parsing (2022.emnlp-main)

Challenge:	ECTSum is a dataset for bullet-point summarization of earnings calls hosted by publicly traded companies.
Approach:	They propose a dataset with transcripts of earnings calls and bullet point summaries derived from Reuters articles.
Outcome:	The proposed dataset compares transcripts of earnings calls hosted by publicly traded companies with experts-written bullet point summaries derived from Reuters articles .

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Xuefeng Bai, Sen Yang, Leyang Cui, Linfeng Song, Yue Zhang

CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision (2022.emnlp-main)

Challenge:	Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input.
Approach:	They evaluate five representative AMR parsers on five domains and analyze challenges to cross-domain parsing.
Outcome:	The proposed method reduces the domain distribution divergence of text and AMR features on two out-of-domain sets.

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Yuning Mao, Ming Zhong, Jiawei Han

FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue (2022.emnlp-main)

Challenge:	Scientific extreme summarization (TLDR) aims to form ultra-short summaries of scientific papers . previous attempts failed to scale up due to heavy human annotation and domain expertise .
Approach:	They propose a method to automatically extract TLDR summaries from scientific papers . they propose 'citeSum' with no human annotation, which is 30 times larger than SciTLDR .
Outcome:	The proposed approach outperforms most fully-supervised methods on SciTLDR without fine-tuning and achieves state-of-the-art results with only 128 examples.

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Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang

Do Children Texts Hold The Key To Commonsense Knowledge? (2022.emnlp-main)

Challenge:	Prior studies of task transfer in dialogue consider only 2-4 tasks, focus on multitasks.
Approach:	They propose a benchmark for FEw-sample TAsk transfer in open-domain dialogue.
Outcome:	The proposed benchmark analyzes the transferability between 132 source-target task pairs and provides a baseline for future work.

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Julien Romero, Simon Razniewski

Commonsense Reasoning

Challenge:	Existing approaches to compiling commonsense knowledge (CSK) struggle with reporting bias, i.e., frequency in text sources is not a good proxy for relevance or truth.
Approach:	They propose that children's texts make fewer assumptions on the reader's knowledge and therefore spell out commonsense more explicitly.
Outcome:	The proposed approach can be leveraged in language-model-based commonsense knowledge extraction settings, where task-unspecific fine-tuning on small amounts of children texts yields significant improvements.

On the Limitations of Reference-Free Evaluations of Generated Text (2022.emnlp-main)

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Daniel Deutsch, Rotem Dror, Dan Roth

Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translation (2022.emnlp-main)

Challenge:	a recent study has shown that evaluation metrics which accurately estimate the quality of generated text are limited in their ability to evaluate generated text.
Approach:	They argue that reference-free metrics are limited in their ability to evaluate generated text . they recommend that they be used as diagnostic tools for analyzing and understanding model behavior .
Outcome:	The proposed evaluation metrics are limited in their ability to evaluate generated text . they can be optimized at test time, can be biased against models with similar outputs .

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Bryan Eikema, Wilker Aziz

IndicXNLI: Evaluating Multilingual Inference for Indian Languages (2022.emnlp-main)

Challenge:	Existing methods to improve beam search quality are inadequate in many ways . a new approximation to the beam search curse has been proposed .
Approach:	They propose an approximation to minimum Bayes risk decoding that would solve the beam search curse.
Outcome:	The proposed approximation has no equivalent to the beam search curse.

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Divyanshu Aggarwal, Vivek Gupta, Anoop Kunchukuttan

Model Cascading: Towards Jointly Improving Efficiency and Accuracy of NLP Systems (2022.emnlp-main)

Challenge:	Indic NLP has made rapid advances in terms of corpora and pre-trained models, but benchmark datasets on standard NLU tasks are limited.
Approach:	They propose to use an NLI dataset for 11 Indic languages to test their accuracy.
Outcome:	The proposed dataset provides useful insights into the behaviour of pre-trained models for a diverse set of languages.

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Neeraj Varshney, Chitta Baral

Semantic Simplification for Sentiment Classification (2022.emnlp-main)

Challenge:	Pre-trained language models such as RoBERTa, ELECTRA, and T5 have a large number of parameters which makes them slow and computationally expensive.
Approach:	They propose a technique that utilizes a collection of models of varying capacities to accurately yet efficiently output predictions.
Outcome:	The proposed technique saves up to 88.93% computation cost and consistently achieves superior prediction accuracy with an improvement of up to 2.18%.

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Xiaotong Jiang, Zhongqing Wang, Guodong Zhou

XPrompt: Exploring the Extreme of Prompt Tuning (2022.emnlp-main)

Challenge:	Recent work on document-level sentiment classification has shown that the sentiment in the original text is often hard to capture . previous studies focus on predicting the overall sentiment from original text using statistical or neural models, but these methods either heavily rely on human knowledge or suffer from the complex structure of the text.
Approach:	They propose a document-level sentiment classification model that enhances the original text with a simplified clause to intensify its sentiment.
Outcome:	Empirical studies show that the proposed model over strong baselines is effective over several strong baseline models.

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Fang Ma, Chen Zhang, Lei Ren, Jingang Wang, Qifan Wang, Wei Wu, Xiaojun Quan, Dawei Song

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? (2022.emnlp-main)

Challenge:	Prompt tuning learns soft prompts to condition pre-trained Language Models for performing downstream tasks in a parameter-efficient manner.
Approach:	They propose a Prompt tuning model with an eXtremely small scale that learns soft prompts to condition the frozen Pre-trained Language Models for performing downstream tasks in a parameter-efficient manner.
Outcome:	The proposed model outperforms the vanilla Prompt-Tuning and can significantly improve across tasks and model scales.

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Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, Luke Zettlemoyer

The Curious Case of Control (2022.emnlp-main)

Challenge:	Large language models can in-context learn by conditioning on a few input-label pairs and making predictions for new inputs.
Approach:	They propose to use ground truth demonstrations to replace labels in demonstrations . they also show that other aspects of the demonstrations are key drivers of endtask performance .
Outcome:	The proposed model outperforms zeroshot inference on a wide range of tasks using ground truth demonstrations.

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Elias Stengel-Eskin, Benjamin Van Durme

Challenge:	Normally-developing children struggle with subject control clauses long after they have acquired the components to understand them.
Approach:	They examine whether heuristics based on semantic roles are consistent with children's English . they find that models are more sensitive to agent-patient information .
Outcome:	The results show that models fail on subject control but fail on object control . the authors show that raising salience of agent and patient relations results in significant changes .

SHARE: a System for Hierarchical Assistive Recipe Editing (2022.emnlp-main)

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Shuyang Li, Yufei Li, Jianmo Ni, Julian McAuley

IM^2: an Interpretable and Multi-category Integrated Metric Framework for Automatic Dialogue Evaluation (2022.emnlp-main)

Challenge:	Existing recipe websites do not provide options for users with dietary restrictions . a growing population follows some form of dietary restriction, with many people following it for a variety of reasons .
Approach:	They propose a system for hierarchical assistive recipe editing that performs simultaneous ingredient substitution before generating natural-language steps using the edited ingredients.
Outcome:	The proposed system can adapt a recipe to satisfy a user-specified dietary constraint.

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Zhihua Jiang, Guanghui Ye, Dongning Rao, Di Wang, Xin Miao

PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models (2022.emnlp-main)

Challenge:	Evaluation metrics for dialogue systems are expensive and time-consuming . current evaluation metrics focus on a single quality or several qualities .
Approach:	They propose an interpretable, multi-faceted, and controllable framework to combine dialogue metrics which are good at measuring different qualities.
Outcome:	The proposed framework integrates a large number of evaluation metrics to improve the performance of the model.

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Yuan Yao, Qianyu Chen, Ao Zhang, Wei Ji, Zhiyuan Liu, Tat-Seng Chua, Maosong Sun

Pre-training Language Models with Deterministic Factual Knowledge (2022.emnlp-main)

Challenge:	Recent advances on self-supervised learning have led to powerful vision-language pre-training models that achieve state-of-the-art performance on a wide range of cross-modal tasks.
Approach:	They propose a vision-language pre-training framework that reformulates discretized object positions and language in a unified language modeling framework.
Outcome:	The proposed model improves performance on position-sensitive vision-language (VL) tasks and also improves on position insensitive tasks.

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Shaobo Li, Xiaoguang Li, Lifeng Shang, Chengjie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, Qun Liu

Finding Skill Neurons in Pre-trained Transformer-based Language Models (2022.emnlp-main)

Challenge:	Existing studies show that Pre-trained Language Models fail to capture factual knowledge robustly.
Approach:	They propose to let PLMs learn the deterministic relationship between context and masked content to improve their ability to capture factual knowledge.
Outcome:	The proposed methods improve accuracy and consistency of factual knowledge capturing and boost performance of other knowledge-intensive tasks.

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Xiaozhi Wang, Kaiyue Wen, Zhengyan Zhang, Lei Hou, Zhiyuan Liu, Juanzi Li

Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue (2022.emnlp-main)

Challenge:	Pre-trained language models have demonstrated superior performance on various natural language processing tasks.
Approach:	They find that after prompt tuning, some neurons encode task-specific skills . they also show that skill neurons are most likely generated in pre-training .
Outcome:	The neurons are highly predictive of task labels after prompt tuning for specific tasks.

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Yingxiu Zhao, Yinhe Zheng, Zhiliang Tian, Chang Gao, Jian Sun, Nevin L. Zhang

PreQuEL: Quality Estimation of Machine Translation Outputs in Advance (2022.emnlp-main)

Challenge:	Existing generative replay methods use only a single task-specific token to control their models.
Approach:	They propose a method to capture task-specific distributions with a conditional variational autoencoder, conditioned on natural language prompts to guide the pseudo-sample generation.
Outcome:	The proposed method outperforms baselines on natural language understanding tasks of advanced task-oriented dialogue (ToD) systems.

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Shachar Don-Yehiya, Leshem Choshen, Omri Abend

Can Transformers Reason in Fragments of Natural Language? (2022.emnlp-main)

Challenge:	A PreQuEL system predicts how well a given sentence will be translated without recourse to the actual translation.
Approach:	They propose a task that uses a model to predict how well a given sentence will be translated . they show that the model is sensitive to syntactic and semantic distinctions .
Outcome:	The proposed model improves on the Quality-Estimation task and on challenge sets and languages.

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Viktor Schlegel, Kamen Pavlov, Ian Pratt-Hartmann

Textless Speech Emotion Conversion using Discrete & Decomposed Representations (2022.emnlp-main)

Challenge:	Recent work on natural language inference has identified two strands of research .
Approach:	They investigate whether neural networks have acquired logical principles from natural language . they use transformer-based models to detect valid inferences in controlled fragments of natural language.
Outcome:	The proposed model overfits to superficial patterns in the data rather than acquiring the logical principles governing reasoning in natural language fragments.

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Felix Kreuk, Adam Polyak, Jade Copet, Eugene Kharitonov, Tu Anh Nguyen, Morgan Rivière, Wei-Ning Hsu, Abdelrahman Mohamed, Emmanuel Dupoux, Yossi Adi

Speech & Audio in NLP

Challenge:	Existing methods for modifying emotion of speech are difficult because emotion affects all levels simultaneously.
Approach:	They propose a method to convert a spoken language speech into a model of emotion . they use phonetic-content units, prosodic features, speaker, and emotion to modify the emotion a speech utterance has.
Outcome:	The proposed method beats text-based systems in terms of perceived emotion and audio quality.

Textual Backdoor Attacks Can Be More Harmful via Two Simple Tricks (2022.emnlp-main)

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Yangyi Chen, Fanchao Qi, Hongcheng Gao, Zhiyuan Liu, Maosong Sun

Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP (2022.emnlp-main)

Challenge:	Existing textual backdoor attacks are vulnerable to backdoors . researchers add extra training task to distinguish poisoned and clean data .
Approach:	They propose two tricks that make existing backdoor attacks much more harmful . first trick is to add an extra task to distinguish poisoned and clean data . second trick is using all the clean training data rather than the original clean data.
Outcome:	The proposed tricks can significantly improve attack performance in three tough situations including clean data fine-tuning, low-poisoning-rate, and label-consistent attacks.

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Yangyi Chen, Hongcheng Gao, Ganqu Cui, Fanchao Qi, Longtao Huang, Zhiyuan Liu, Maosong Sun

Retrieval Augmented Visual Question Answering with Outside Knowledge (2022.emnlp-main)

Challenge:	Textual adversarial samples are often misrepresented in research on security, evaluation, explainability, and data augmentation.
Approach:	They propose to use adversarial samples to evaluate their methods on security tasks to demonstrate the real-world concerns rather than developing impractical methods.
Outcome:	The proposed method has higher practical value than the current benchmark.

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Weizhe Lin, Bill Byrne

Instance Regularization for Discriminative Language Model Pre-training (2022.emnlp-main)

Challenge:	Recent systems that use Dense Passage Retrieval (DPR) for visual question answering use Denses trained separately from answer generation.
Approach:	They propose a joint training scheme that integrates DPR with answer generation to improve the performance of OK-VQA systems.
Outcome:	The proposed model outperforms existing systems with strong Dense Passage Retrieval (DPR) it yields significant improvements in answer quality and computation required for training.

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Zhuosheng Zhang, Hai Zhao, Ming Zhou

GuoFeng: A Benchmark for Zero Pronoun Recovery and Translation (2022.emnlp-main)

Challenge:	Existing studies have optimized independent strategies of ennoising or denosing . Existing methods treat training instances equally throughout the training process .
Approach:	They propose to use ennoising and denoising to train discriminative pre-trained language models . they propose to model the complexity of restoring the original sentences from corrupted ones .
Outcome:	Experimental results show that the proposed method improves pre-training efficiency, effectiveness, and robustness.

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Mingzhou Xu, Longyue Wang, Derek F. Wong, Hongye Liu, Linfeng Song, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

ScienceWorld: Is your Agent Smarter than a 5th Grader? (2022.emnlp-main)

Challenge:	ZPs are often omitted when they can be pragmatically or grammatically inferred from intraand inter-sentential contexts.
Approach:	They propose a benchmark testset for target evaluation on Chinese-English ZP translation.
Outcome:	The proposed testset covers five genres and identifies current challenges for evaluation.

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Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, Prithviraj Ammanabrolu

Improving Embeddings Representations for Comparing Higher Education Curricula: A Use Case in Computing (2022.emnlp-main)

Challenge:	Existing models cannot reason about or explain learned science concepts in novel contexts, despite transformer-based progress in question-answering and scientific text processing .
Approach:	They propose a benchmark to test agents’ scientific reasoning abilities in a new interactive text environment at the level of a standard elementary school science curriculum.
Outcome:	The proposed model outperforms a model trained for 100k steps in a standard elementary school science curriculum.

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Jeffri Murrugarra-Llerena, Fernando Alva-Manchego, Nils Murrugarra-LLerena

Mitigating Spurious Correlation in Natural Language Understanding with Counterfactual Inference (2022.emnlp-main)

Challenge:	Existing methods to represent study programs using bag-of-words and clustering algorithms are prone to biases due to personal beliefs and perspectives.
Approach:	They propose to use pre-trained word embeddings to fine-tune a study program classification task to obtain more accurate curriculum representations than strong baselines.
Outcome:	The proposed method is compared to existing methods on a study program classification task and on comparing computing curricula from USA and Latin America.

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Can Udomcharoenchaikit, Wuttikorn Ponwitayarat, Patomporn Payoungkhamdee, Kanruethai Masuk, Weerayut Buaphet, Ekapol Chuangsuwanich, Sarana Nutanong

End-to-End Neural Discourse Deixis Resolution in Dialogue (2022.emnlp-main)

Challenge:	Existing approaches to debias NLU models rely on superficial patterns to produce correct predictions . lexical overlap and annotation artifacts can be used to make shortcuts .
Approach:	They propose a causal analysis framework to help debias NLU models by defining causal relationships and utilizing counterfactual inference to mitigate bias.
Outcome:	The proposed framework can improve robustness across three NLU tasks while maintaining high in-distribution performance.

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Shengjie Li, Vincent Ng

Balancing out Bias: Achieving Fairness Through Balanced Training (2022.emnlp-main)

Challenge:	Lexical overlap is a strong indicator of entity coreference, both among names and in the resolution of nominals.
Approach:	They propose to extend their span-based entity coreference model to exploit task-specific characteristics of discourse deixis resolution in dialogue.
Outcome:	The proposed model achieves state-of-the-art results on the four datasets in the CODI-CRAC 2021 shared task.

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Xudong Han, Timothy Baldwin, Trevor Cohn

Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models (2022.emnlp-main)

Challenge:	Existing approaches to reducing group bias do not account for correlations between author demographics and linguistic variables, limiting their effectiveness.
Approach:	They extend a method for countering group bias using balanced training by balancing each demographic group in training and using protected attributes as input.
Outcome:	The proposed model outperforms all other methods when combined with balanced training.

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Mengzhou Xia, Mikel Artetxe, Jingfei Du, Danqi Chen, Veselin Stoyanov

Identifying Physical Object Use in Sentences (2022.emnlp-main)

Challenge:	Pre-trained masked language models perform few-shot learning, but discriminative models like ELECTRA do not fit into the paradigm.
Approach:	They propose to use ELECTRA to train pre-trained models to score originality of target options without introducing new parameters.
Outcome:	The proposed model outperforms masked language models in a wide range of tasks without adding new parameters.

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Tianyu Jiang, Ellen Riloff

CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation (2022.emnlp-main)

Challenge:	Prior research has focused on learning the prototypical functions of physical objects . but many sentences refer to objects even when they are not used .
Approach:	They propose a task that determines whether a physical object mentioned in a sentence was used or likely will be used.
Outcome:	The proposed model exploits data augmentation methods and FrameNet to fine-tune a pre-trainedmodel.

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Deeksha Varshney, Aizan Zafar, Niranshu Behera, Asif Ekbal

Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals (2022.emnlp-main)

Challenge:	Prior research has provided a single poorly graded label for the entire utterance, which may mislead model training and/or lead to erroneous assessment.
Approach:	They propose to use telemedicine to carry on a natural conversation and understand the meanings of words to respond with a coherent dialog.
Outcome:	telemedicine has been shown to be effective in carrying on a natural conversation and understanding the meanings of words to respond with a coherent dialog.

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Maarten De Raedt, Fréderic Godin, Chris Develder, Thomas Demeester

Data-Efficient Playlist Captioning With Musical and Linguistic Knowledge (2022.emnlp-main)

Challenge:	a recent study found that finetuned language models rely on spurious patterns in training data . this limitation limits their performance on out-of-distribution (OOD) test data.
Approach:	They propose a method that only requires annotation of a small fraction of training data . they add 1% manual counterfactuals to training data and generate extra counterfacts in vector space .
Outcome:	The proposed approach improves sentiment classification using IMDb data and other sets for OOD tests.

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Giovanni Gabbolini, Romain Hennequin, Elena Epure

Improved grammatical error correction by ranking elementary edits (2022.emnlp-main)

Challenge:	Music streaming services feature billions of playlists created by users, professional editors or algorithms.
Approach:	They propose a multi-modal encoder-decoder model for automatic playlist captioning that leverages linguistic and musical knowledge to generate correct and thematic captions.
Outcome:	The proposed model yields 2x-3x higher BLEU@4 and CIDEr than state-of-the-art captioning algorithms on a new playlists dataset from two major streaming services.

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Alexey Sorokin

Improving Tokenisation by Alternative Treatment of Spaces (2022.emnlp-main)

Challenge:	a new study shows that grammatical error correction models are far from perfect for English . reranking allows for a better classification of edits, but it can be difficult for other languages .
Approach:	They propose a two-stage reranking method for grammatical error correction using a model as edit generator and a sequence labeling model as second step.
Outcome:	The proposed method surpasses existing methods on BEA 2019 English dataset by 2-3%.

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Edward Gow-Smith, Harish Tayyar Madabushi, Carolina Scarton, Aline Villavicencio

GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation (2022.emnlp-main)

Challenge:	Subword tokenisation is a key initial step in processing natural language . it uses a number of different methods to tokenise text, including a stringsearching technique and a word-matching technique.
Approach:	They propose to use a vocabulary-based approach to tokenise text using a numerical ID and a mathematical function to manipulate it.
Outcome:	The method is based on a set of training data and learning from it to build a vocabulary and tokenise it at inference time using this vocabulary and learnt parameters.

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Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, Daniel Weld

The Architectural Bottleneck Principle (2022.emnlp-main)

Challenge:	Effective human evaluation of text generation tasks remains an important, open area for research.
Approach:	They propose a system for running standardized human evaluations across different generation tasks.
Outcome:	The proposed system produces standardized human evaluations across tasks . it crowdsources predictions and ranks systems on leaderboards . the proposed system is not reproducible over time and different annotator populations .

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Tiago Pimentel, Josef Valvoda, Niklas Stoehr, Ryan Cotterell

When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems (2022.emnlp-main)

Challenge:	a recent study examined how much information a model's representations contain . a new approach to probing is to look exactly like the component .
Approach:	They propose a new probing principle that aims to estimate how much information a model could extract from its representations.
Outcome:	The proposed probes extract syntactic information from the representations of a neural network . the proposed probe is based on the architectural bottleneck principle .

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Elias Stengel-Eskin, Emmanouil Antonios Platanios, Adam Pauls, Sam Thomson, Hao Fang, Benjamin Van Durme, Jason Eisner, Yu Su

Challenge:	In natural language understanding systems, users’ evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space.
Approach:	They propose to use a small set of new symbols to build broad-coverage NLU systems.
Outcome:	The proposed model is based on two prototypical NLU tasks: intent recognition and semantic parsing.

Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt (2022.emnlp-main)

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Lianzhe Huang, Shuming Ma, Dongdong Zhang, Furu Wei, Houfeng Wang

Three Real-World Datasets and Neural Computational Models for Classification Tasks in Patent Landscaping (2022.emnlp-main)

Challenge:	Existing work focuses on monolingual prompts, but we study multilingual prompt for multilingual models.
Approach:	They propose a model that uses a unified prompt for all languages, called UniPrompt, to alleviate the effort of designing different prompts for multiple languages.
Outcome:	The proposed model outperforms baseline models in the zero-shot cross-lingual setting.

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Subhash Pujari, Jannik Strötgen, Mark Giereth, Michael Gertz, Annemarie Friedrich

Topic Modeling With Topological Data Analysis (2022.emnlp-main)

Challenge:	Patent Landscaping is one of the central tasks of intellectual property management and involves selecting and grouping patents according to user-defined technical or application-oriented criteria.
Approach:	They propose to use a novel model that takes into account textual information from the patents’ full texts as well as embeddings created based on the patent’s CPC labels.
Outcome:	The proposed model takes into account textual information from the patents’ full texts as well as embeddings created based on the patent’s CPC labels.

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Ciarán Byrne, Danijela Horak, Karo Moilanen, Amandla Mabona

Topic Modeling

Challenge:	Recent topic modelling approaches that use clustering on word, token or document embeddings can ex-tract coherent topics.
Approach:	They propose an unsupervised topic mod-elling method which uses TopologicalData Analysis to extract a topologicalskeleton of the manifold upon which word embeddings lie.
Outcome:	The proposed method performs on par with a baseline and can construct a network of coherent topics with meaningful relationships between them.

Predicting Fine-Tuning Performance with Probing (2022.emnlp-main)

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Zining Zhu, Soroosh Shahtalebi, Frank Rudzicz

Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers (2022.emnlp-main)

Challenge:	Large-scale neural models have recently demonstrated impressive performance in language understanding tasks, typically evaluated by their fine-tuned performance.
Approach:	They propose to use probing to extract a proxy signal widely used in model development to predict fine-tuning performance.
Outcome:	The proposed method predicts fine-tuning performance with errors 40% - 80% smaller than baselines.

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Abhijeet Awasthi, Ashutosh Sathe, Sunita Sarawagi

Programming Languages in NLP

Challenge:	Adapting Text-to-SQL parsers to new database schemas is a challenging task owing to a vast diversity of schemas and zero availability of natural language queries in new schemas.
Approach:	They propose a framework for synthesizing parallel datasets for adapting Text-to-SQL parsers.
Outcome:	The proposed framework outperforms existing methods on databases with diverse schemas and zero availability of natural language queries.

Agent-Specific Deontic Modality Detection in Legal Language (2022.emnlp-main)

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Abhilasha Sancheti, Aparna Garimella, Balaji Vasan Srinivasan, Rachel Rudinger

Challenge:	a contract is a legal document executed by two or more parties.
Approach:	They propose a corpus of English contracts annotated with deontic modalities . they benchmark this corpus on agent-specific deontics and trigger span detection .
Outcome:	The proposed dataset can detect red flags with high recall.

COLD: A Benchmark for Chinese Offensive Language Detection (2022.emnlp-main)

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Jiawen Deng, Jingyan Zhou, Hao Sun, Chujie Zheng, Fei Mi, Helen Meng, Minlie Huang

Ethical NLP

Challenge:	Offensive language detection is crucial for maintaining a civilized social media platform and deploying pre-trained language models.
Approach:	They propose a benchmark benchmark for Chinese offensive language analysis including a Chinese Offensive Language Dataset and a baseline detector which is trained on the dataset.
Outcome:	The proposed benchmark contributes to Chinese offensive language detection which is challenging for existing resources.

Fixing Model Bugs with Natural Language Patches (2022.emnlp-main)

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Shikhar Murty, Christopher Manning, Scott Lundberg, Marco Tulio Ribeiro

WeDef: Weakly Supervised Backdoor Defense for Text Classification (2022.emnlp-main)

Challenge:	a growing body of research focused on using language to give instructions, supervision and even inductive biases to models instead of relying exclusively on labeled examples.
Approach:	They explore natural language patches that provide corrective feedback at the right level of abstraction.
Outcome:	The proposed model improves accuracy on real data by 1–4 accuracy points on different slices of a sentiment analysis dataset and F1 by 7 points on a relation extraction dataset.

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Lesheng Jin, Zihan Wang, Jingbo Shang

Interventional Training for Out-Of-Distribution Natural Language Understanding (2022.emnlp-main)

Challenge:	Existing backdoor defense methods are only effective for limited trigger types . Existing methods only work against certain types of triggers .
Approach:	They propose a weakly supervised backdoor defense framework WeDef to defend different trigger types at once . they define reliability of samples based on whether predictions agree with their labels .
Outcome:	The proposed framework outperforms existing backdoor defense methods against popular trigger-based attacks.

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Sicheng Yu, Jing Jiang, Hao Zhang, Yulei Niu, Qianru Sun, Lidong Bing

Challenge:	Existing methods for NLU training use only known and single confounders, but in many NLU tasks the confounder can be unknown and multifactorial.
Approach:	They propose a method that performs multi-granular intervention with identified multifactorial confounders by using a bottom-up automatic intervention method.
Outcome:	The proposed method performs multi-granular intervention with identified multifactorial confounders on three NLU tasks, namely, natural language inference, fact verification and paraphrase identification.

Pseudo-Relevance for Enhancing Document Representation (2022.emnlp-main)

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Jihyuk Kim, Seung-won Hwang, Seoho Song, Hyeseon Ko, Young-In Song

ZeroGen: Efficient Zero-shot Learning via Dataset Generation (2022.emnlp-main)

Challenge:	a novel approach to document retrieval can be used to encode documents as vectors . a few query-relevant terms can be pruned out to reduce index overhead .
Approach:	They propose to enhance the document representation for the bi-encoder approach in dense document retrieval.
Outcome:	The proposed solution reduces latency and memory footprint up to 8- and 3-fold . it is validated on MSMARCO and real-world search query logs .

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Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, Lingpeng Kong

Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings (2022.emnlp-main)

Challenge:	Existing approaches to generate training data with pre-trained language models have been found effective in various scenarios.
Approach:	They propose an unsupervised zero-shot learning method that generates a dataset from scratch and trains a tiny task model under supervision of the synthesized dataset.
Outcome:	The proposed method is annotated-free and efficient, but can provide useful insights from the perspective of data-free model-agnostic knowledge distillation and unreferenced text generation evaluation.

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Malte Ostendorff, Nils Rethmeier, Isabelle Augenstein, Bela Gipp, Georg Rehm

SPE: Symmetrical Prompt Enhancement for Fact Probing (2022.emnlp-main)

Challenge:	Prior work relies on discrete citation relations to generate contrast samples, but discrete ones enforce a hard cut-off to similarity.
Approach:	They propose to use nearest neighbor sampling to learn continuous similarity and to sample hard-to-learn negatives and positives by controlling the sampling margin between them.
Outcome:	The proposed method outperforms the state-of-the-art on the SciDocs benchmark and can train (or tune) language models sample-efficiently.

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Yiyuan Li, Tong Che, Yezhen Wang, Zhengbao Jiang, Caiming Xiong, Snigdha Chaturvedi

MedJEx: A Medical Jargon Extraction Model with Wiki’s Hyperlink Span and Contextualized Masked Language Model Score (2022.emnlp-main)

Challenge:	Recent work probes PLMs for the extent of factual knowledge through prompts . however, these methods do not consider symmetry of the task: object and subject prediction.
Approach:	They propose a continuous prompt-based method that leverages symmetry of the task by constructing symmetrical prompts for subject and object prediction.
Outcome:	The proposed method improves on a popular factual probing dataset on lAMA.

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Sunjae Kwon, Zonghai Yao, Harmon Jordan, David Levy, Brian Corner, Hong Yu

Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections (2022.emnlp-main)

Challenge:	Existing natural language processing (NLP) methods for identifying medical jargon terms are difficult for patients to understand.
Approach:	They propose a natural language processing application for identifying medical jargon terms from electronic health record notes.
Outcome:	The proposed model outperforms state-of-the-art models on an auxiliary Wikipedia hyperlink span dataset and on the annotated MedJ dataset.

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Wei-Jen Ko, Cutter Dalton, Mark Simmons, Eliza Fisher, Greg Durrett, Junyi Jessy Li

Learning to Generate Overlap Summaries through Noisy Synthetic Data (2022.emnlp-main)

Challenge:	Existing systems for text comprehension are inadequate for more holistic comprehension of a discourse.
Approach:	They propose a new paradigm that captures both discourse and semantic links between sentences in the form of free-form, open-ended questions.
Outcome:	The proposed model captures discourse and semantic links between sentences in the form of free-form, open-ended questions.

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Naman Bansal, Mousumi Akter, Shubhra Kanti Karmaker Santu

Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality (2022.emnlp-main)

Challenge:	Existing training data for seq-to-seq models is limited due to the lack of available training data.
Approach:	They propose a data augmentation technique which allows to create large amount of synthetic data for training a seq-to-seq model.
Outcome:	The proposed technique performs better than pre-trained models on news domains and is close to the existing methods on golden training data.

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Yichen Jiang, Xiang Zhou, Mohit Bansal

Directions for NLP Practices Applied to Online Hate Speech Detection (2022.emnlp-main)

Challenge:	Recent datasets expose the lack of systematic generalization ability in standard sequence-to-sequence models.
Approach:	They propose two techniques to address the lack of systematic generalization ability in standard sequence-to-sequence models by mutual exclusivity training and prim2primX data augmentation.
Outcome:	The proposed methods improve on two widely-used compositionality datasets.

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Paula Fortuna, Monica Dominguez, Leo Wanner, Zeerak Talat

Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection (2022.emnlp-main)

Challenge:	Existing approaches to address hate speech in online spaces have relied on conventions and practices from NLP.
Approach:	They argue that many conventions in NLP are poorly suited for the problem and encourage researchers to develop methods that are more appropriate for the task.
Outcome:	The proposed methods are poorly suited for the problem and should be adapted to address the propagation of online harms.

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Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti

OpenCQA: Open-ended Question Answering with Charts (2022.emnlp-main)

Challenge:	Existing models for answer sentence selection (AS2) are not yet available for AS2 .
Approach:	They propose to incorporate paragraph-level semantics within and across documents to improve transformers for AS2 . they propose to use a dataset to predict whether two sentences are extracted from the same paragraph .
Outcome:	The proposed model outperforms baseline models on public and industrial datasets on three public and one industrial dataset.

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Shankar Kantharaj, Xuan Long Do, Rixie Tiffany Leong, Jia Qing Tan, Enamul Hoque, Shafiq Joty

A Systematic Investigation of Commonsense Knowledge in Large Language Models (2022.emnlp-main)

Challenge:	OpenCQA is a task to answer open-ended questions about charts with descriptive texts.
Approach:	They propose a task to answer open-ended questions about charts with descriptive texts.
Outcome:	The proposed task is to answer an open-ended question about a chart with descriptive texts.

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Xiang Lorraine Li, Adhiguna Kuncoro, Jordan Hoffmann, Cyprien de Masson d’Autume, Phil Blunsom, Aida Nematzadeh

Transforming Sequence Tagging Into A Seq2Seq Task (2022.emnlp-main)

Challenge:	Recent large language models (LMs) have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup.
Approach:	They conduct a systematic and rigorous zero-shot and few-shot commonsense evaluation of large pre-trained language models to better understand their ability to capture commonsensical knowledge.
Outcome:	The proposed model can exploit surface cues and annotation artefacts without task-specific supervision and is insufficient to achieve human-level commonsense performance.

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Karthik Raman, Iftekhar Naim, Jiecao Chen, Kazuma Hashimoto, Kiran Yalasangi, Krishna Srinivasan

CycleKQR: Unsupervised Bidirectional Keyword-Question Rewriting (2022.emnlp-main)

Challenge:	Pretrained, large, generative language models have had great success in a wide range of sequence tagging and structured prediction tasks.
Approach:	They propose to use a new format for casting input text sentences and their output labels into the input and target of a Seq2Seq model and introduce it to test their hypothesis.
Outcome:	The proposed format shows to be both simpler and more effective and devoid of hallucination.

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Andrea Iovine, Anjie Fang, Besnik Fetahu, Jie Zhao, Oleg Rokhlenko, Shervin Malmasi

Paraphrasing

Challenge:	Existing approaches to query paraphrases are based on encoderdecoder architectures, but they do not support the two important functionalities beyond questions.
Approach:	They propose a keyword-question rewriting task to improve query understanding capabilities of NLU systems for all surface forms.
Outcome:	Empirically, we show that CycleKQR significantly improves QA performance by rewriting queries into the appropriate form while retaining the original semantic meaning of input queries.

Model Criticism for Long-Form Text Generation (2022.emnlp-main)

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Yuntian Deng, Volodymyr Kuleshov, Alexander Rush

Improving Faithfulness by Augmenting Negative Summaries from Fake Documents (2022.emnlp-main)

Challenge:	Language models generate fluent text, but it remains unclear whether output retains coherent high-level structure.
Approach:	They propose to use a statistical tool to evaluate high-level structure of text . they compare distributions between real and generated data in latent space .
Outcome:	The proposed model criticism compares distributions between real and generated data in a latent space . different generative processes identify specific failure modes of the underlying model .

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Tianshu Wang, Faisal Ladhak, Esin Durmus, He He

Challenge:	Current abstractive summarization systems tend to hallucinate unfaithful content . however, the most common method does not disentangle factual errors from other errors.
Approach:	They propose a back-translation-style approach to augment negative samples that mimic factual errors made by the model.
Outcome:	The proposed method improves faithfulness without sacrificing informativeness . it incorporates negative samples into training, and produces faithful/unfaithful summaries .

Joint Completion and Alignment of Multilingual Knowledge Graphs (2022.emnlp-main)

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Soumen Chakrabarti, Harkanwar Singh, Shubham Lohiya, Prachi Jain, null Mausam

Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates (2022.emnlp-main)

Challenge:	Existing methods for knowledge graph completion are incomplete, as curators struggle to keep up with the real world.
Approach:	They propose a multitask approach to solve missing facts in incomplete Knowledge Graphs . they add a relation representation to the existing KG embedding scheme .
Outcome:	The proposed system outperforms existing models in seven languages compared to existing models . it also outperformed existing models, underscoring the value of joint alignment and completion.

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Suzanna Sia, Kokil Jaidka, Hansin Ahuja, Niyati Chhaya, Kevin Duh

A Federated Approach to Predicting Emojis in Hindi Tweets (2022.emnlp-main)

Challenge:	Existing work on persuasion in online forums focuses on identifying debate winners and winning negotiation games.
Approach:	They adopt a hierarchical generative Variational Autoencoder model to model winning arguments . they propose competing hypotheses about the nature of argumentation .
Outcome:	The proposed model predicts winning arguments in reddit debates . it uses a hierarchical generative Variational Autoencoder to model argumentation .

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Deep Gandhi, Jash Mehta, Nirali Parekh, Karan Waghela, Lynette D’Mello, Zeerak Talat

Injecting Domain Knowledge in Language Models for Task-oriented Dialogue Systems (2022.emnlp-main)

Challenge:	emojis are a visual modality to, often private, textual communication, but their use tends to cluster into the frequently used and the rarely used eojis.
Approach:	They propose to use 118k tweets to predict emojis in Hindi and a federated learning algorithm to achieve a balance between model performance and user privacy.
Outcome:	The proposed approach achieves comparative scores with more complex centralised models while minimising risks to user privacy.

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Denis Emelin, Daniele Bonadiman, Sawsan Alqahtani, Yi Zhang, Saab Mansour

TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack (2022.emnlp-main)

Challenge:	Pre-trained language models lack domain-specific knowledge that does not naturally occur in pre-training data.
Approach:	They propose to inject domain-specific knowledge prior to fine-tuning on TOD tasks by using adapters that can be easily integrated with PLMs.
Outcome:	The proposed methods show that they can inject domain-specific knowledge prior to fine-tuning on TOD tasks.

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Yu Cao, Dianqi Li, Meng Fang, Tianyi Zhou, Jun Gao, Yibing Zhan, Dacheng Tao

Improving Low-Resource Languages in Pre-Trained Multilingual Language Models (2022.emnlp-main)

Challenge:	Existing adversarial models rely on keyword matching and ignore relevant contextual relations for answer prediction.
Approach:	They propose to use keyword matching to attack model with two biases that rely on a perturbed answer sentence and a distracting answer sentence to misguide model.
Outcome:	The proposed method produces fluent and grammatical adversarial contexts while maintaining gold answers.

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Viktor Hangya, Hossain Shaikh Saadi, Alexander Fraser

SCROLLS: Standardized CompaRison Over Long Language Sequences (2022.emnlp-main)

Challenge:	Pre-trained multilingual language models are the foundation of many NLP approaches, but are often not well-supported by these models due to small available monolingual corpora.
Approach:	They propose an unsupervised approach to improve cross-lingual representations of low-resource languages by bootstrapping word translation pairs from monolingual corpora and using them to improve language alignment.
Outcome:	The proposed approach improves cross-lingual representations on low-resource languages using word retrieval and zero-shot named entity recognition.

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Uri Shaham, Elad Segal, Maor Ivgi, Avia Efrat, Ori Yoran, Adi Haviv, Ankit Gupta, Wenhan Xiong, Mor Geva, Jonathan Berant, Omer Levy

Challenge:	Standard NLP benchmarks focus on short texts, but long texts are produced in the context of longer discourses.
Approach:	They propose a new benchmark that places models in context of long texts that require reasoning over long texts.
Outcome:	The proposed task sets are based on a set of long-text datasets and host a live leaderboard to facilitate research on model architecture and pretraining methods.

PAR: Political Actor Representation Learning with Social Context and Expert Knowledge (2022.emnlp-main)

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Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Ningnan Wang, Peisheng Yu, Qinghua Zheng, Xiaojun Chang, Minnan Luo

JDDC 2.1: A Multimodal Chinese Dialogue Dataset with Joint Tasks of Query Rewriting, Response Generation, Discourse Parsing, and Summarization (2022.emnlp-main)

Challenge:	Existing approaches focus on textual data and voting records to induce political actors' stances.
Approach:	They propose a Political Actor Representation learning framework that leverages social context and expert knowledge to model ideological stances.
Outcome:	The proposed framework improves political text understanding and improves roll call vote prediction and political perspective detection.

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Nan Zhao, Haoran Li, Youzheng Wu, Xiaodong He

PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings (2022.emnlp-main)

Challenge:	e-commerce users express their needs using text, images, or videos . but detailed information provided by images is limited, and customer service systems cannot understand the intent of users without the input text.
Approach:	They construct a large-scale multimodal multi-turn dialogue dataset from a mainstream Chinese E-commerce platform . the dataset contains about 246K dialogue sessions, 3M utterances, and 507K images .
Outcome:	The proposed dataset contains 246K dialogue sessions, 3M utterances, 507K images . it also includes product knowledge bases and image category annotations .

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Qiyu Wu, Chongyang Tao, Tao Shen, Can Xu, Xiubo Geng, Daxin Jiang

Digging Errors in NMT: Evaluating and Understanding Model Errors from Partial Hypothesis Space (2022.emnlp-main)

Challenge:	Existing approaches to learning sentence embeddings in unsupervised manner depend on mono-augmenting . existing approaches depend on augmenting biases and thus corrupt the quality of sentence embeds.
Approach:	They propose a method to augment a sentence with a semantically-close positive instance to construct contrastive pairs in unsupervised manner.
Outcome:	The proposed method improves performance on STS benchmarks and compares with existing methods.

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Jianhao Yan, Chenming Wu, Fandong Meng, Jie Zhou

DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection (2022.emnlp-main)

Challenge:	Current evaluation of neural machine translation systems is limited by one best hypothesis and search errors brought by heuristic decoding algorithms.
Approach:	They propose a new evaluation protocol which defines model errors with model’s ranking capability over hypothesis space and Monte Carlo sampling evaluation to tackle the problem of exponentially large space.
Outcome:	The proposed evaluation protocol is consistent with what is currently used in the field and is consistent to what is being proposed.

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Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang