Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Analyzing Wrap-Up Effects through an Information-Theoretic Lens (2022.acl-short)

Challenge:	Existing models favor extracting the shortest possible rationales to explain model predictions . however, this assumption has yet to be validated .
Approach:	They propose a model that extracts rationales at any target length from text inputs . they show that rationale lengths too short do not help humans predict labels better .
Outcome:	The proposed model achieves compatible end-task performance and human-annotated rationale agreement compared to baseline models .

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Clara Meister, Tiago Pimentel, Thomas Clark, Ryan Cotterell, Roger Levy

Challenge:	a lack of studies targeting naturalistic sentence-final reading behavior is likely to explain the lack of data on reading time (RT) data is omitted due to the confounding factors introduced by so-called "wrap-up effects"
Approach:	They propose to look for a link between “wrap-up effects” and information theoretic quantities such as word and context information content.
Outcome:	The proposed model omits data on words at the end of sentences or clauses to control for the confounding factors introduced by wrap-up effects.

Have my arguments been replied to? Argument Pair Extraction as Machine Reading Comprehension (2022.acl-short)

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Jianzhu Bao, Jingyi Sun, Qinglin Zhu, Ruifeng Xu

Reasoning

Challenge:	Existing studies identify argument pairs indirectly by predicting sentence-level relations between two documents, neglecting the holistic argument-level interactions.
Approach:	They propose to use machine reading comprehension to extract argument pairs from two documents . they propose to employ an AM query to identify all arguments in two documents, then an APE query to extract its paired arguments from another document.
Outcome:	The proposed method outperforms the state-of-the-art method by 7.11% in F1 score.

On the probability–quality paradox in language generation (2022.acl-short)

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Clara Meister, Gian Wiher, Tiago Pimentel, Ryan Cotterell

Text Generation

Challenge:	a recent study suggests that mode-seeking decoding methods produce unnatural language . a higher probability text is perceived as more human-like, but this is not the case with stochastic decoding .
Approach:	They propose to explain why higher-probability text is perceived as more human-like . they argue that human-type language should contain an amount of information close to entropy .
Outcome:	The proposed model produces lower-probability text, which is perceived as more human-like . elucidation of this hypothesis supports the hypothesis .

Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning (2022.acl-short)

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Yutao Mou, Keqing He, Yanan Wu, Zhiyuan Zeng, Hong Xu, Huixing Jiang, Wei Wu, Weiran Xu

Voxel-informed Language Grounding (2022.acl-short)

Challenge:	Existing methods to find out out-of-domain (OOD) intents do not take prior knowledge of in-domain data into account.
Approach:	They propose a disentangled knowledge transfer method to bridge the gap between IND pre-training and OOD clustering by using a unified multi-head contrastive learning framework.
Outcome:	The proposed method is able to group new unknown intents into different clusters, enabling future development of the system.

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Rodolfo Corona, Shizhan Zhu, Dan Klein, Trevor Darrell

Challenge:	Embodied robotic agents can be used to ground objects using 3D geometry . despite typically being paired with 2D images, natural language describes a fundamentally 3D world .
Approach:	They propose a model that leverages 3D geometric information to ground natural language . they show that VLG significantly improves grounding accuracy on SNARE .
Outcome:	The proposed model significantly improves grounding accuracy on SNARE, an object reference game task.

P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks (2022.acl-short)

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Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, Jie Tang

On Efficiently Acquiring Annotations for Multilingual Models (2022.acl-short)

Challenge:	Existing methods of prompt tuning cannot handle hard sequence labeling tasks.
Approach:	They propose to optimize prompt tuning to tune continuous prompts with a frozen language model.
Outcome:	The proposed method matches finetuning with prompt tuning while having only 0.1%-3% tuned parameters.

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Joel Ruben Antony Moniz, Barun Patra, Matthew Gormley

Green & Sustainable NLP

Challenge:	a recent study shows that joint learning across multiple languages performs better than the aforementioned approaches . traditional approaches to support NLP tasks require a lot of annotations to perform . a new approach is to train a model for each language with annotation budget divided equally among them .
Approach:	They propose a method for joint learning across multiple languages using a single model . they show that active learning provides additional, complementary benefits .
Outcome:	The proposed method outperforms other models on a diverse set of tasks . it can arbitrate its annotation budget to query languages it is less certain on .

Automatic Detection of Entity-Manipulated Text using Factual Knowledge (2022.acl-short)

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Ganesh Jawahar, Muhammad Abdul-Mageed, Laks Lakshmanan

Does BERT Know that the IS-A Relation Is Transitive? (2022.acl-short)

Challenge:	Current fake news detectors that exploit stylometric signals from the text are insufficient for distinguishing manipulated text from human written text.
Approach:	They propose a neural network detector that detects manipulated news articles by reasoning about the facts mentioned in the article.
Outcome:	The proposed detector outperforms the state-of-the-art detector in accuracy.

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Ruixi Lin, Hwee Tou Ng

Buy Tesla, Sell Ford: Assessing Implicit Stock Market Preference in Pre-trained Language Models (2022.acl-short)

Challenge:	Recent studies suggest pre-trained BERT can capture lexico-semantic clues from words in context.
Approach:	They examine word senses and the transitive property of IS-A relation . they aim to quantify how much BERT agrees with transitivity property .
Outcome:	The proposed model can capture lexico-semantic clues from words in context . but to what extent it captures transitive nature of some lexical relations is unclear .

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Chengyu Chuang, Yi Yang

Pixie: Preference in Implicit and Explicit Comparisons (2022.acl-short)

Challenge:	Pretrained language models such as BERT have been used in many NLP tasks . however, there are still significant differences in their implicit preferences in the stock market .
Approach:	They assess the implicit stock market preferences in pretrained language models such as BERT . they find that there are significant differences in preferences between industry sectors .
Outcome:	The proposed model is more positive towards the stock market, but there are significant differences between industry sectors or within a sector.

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Amanul Haque, Vaibhav Garg, Hui Guo, Munindar Singh

Text Classification

Challenge:	Existing studies on preference classification focus on explicit comparisons, but recent work has focused on indirect comparisons which lack comparative quantifiers and adjectives.
Approach:	They propose a manual annotated dataset for preference classification that includes 8,890 app reviews.
Outcome:	The proposed model outperforms the state-of-the-art model and achieves a weighted average F1 score of 83.34%.

Counterfactual Explanations for Natural Language Interfaces (2022.acl-short)

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George Tolkachev, Stephen Mell, Stephan Zdancewic, Osbert Bastani

Explainability & Interpretability in NLP

Challenge:	Semantic parsing is a promising technique for enabling natural language interfaces, but human language can encode concepts that do not exist in the underlying system or are encoded using different language.
Approach:	They propose a novel approach for generating explanations of a natural language interface based on semantic parsing by providing a user with an utterance and a demonstration of their desired goal.
Outcome:	The proposed approach significantly improves user performance and generates explanations that more closely match the user’s intent compared to two ablations.

Predicting Difficulty and Discrimination of Natural Language Questions (2022.acl-short)

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Matthew Byrd, Shashank Srivastava

Challenge:	Item Response Theory (IRT) has been used to numerically characterize question difficulty and discrimination for human subjects in domains including cognitive psychology and education.
Approach:	They explore the relationship between difficulty and discrimination in question-answering contexts by using IRT to characterize item difficulty and item discrimination.
Outcome:	The proposed models can predict difficulty and discrimination parameters for new questions and explain them with features of questions, answers, and associated contexts.

How does the pre-training objective affect what large language models learn about linguistic properties? (2022.acl-short)

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Ahmed Alajrami, Nikolaos Aletras

The Power of Prompt Tuning for Low-Resource Semantic Parsing (2022.acl-short)

Challenge:	Several pre-training objectives have been proposed to pre-train language models . but, to our knowledge, no studies have investigated how different pre- training objectives affect what BERT learns about linguistic properties.
Approach:	They propose to use masked language modeling to pre-train language models . they propose to optimize a mangled language modeling objective to learn linguistic information .
Outcome:	The proposed objectives improve BERT's learning of linguistic properties compared to non-linguistically motivated objectives.

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Nathan Schucher, Siva Reddy, Harm de Vries

Data Contamination: From Memorization to Exploitation (2022.acl-short)

Challenge:	Prompt tuning is an effective method for adapting pre-trained language models to downstream tasks.
Approach:	They propose to use prompt tuning for semantic parsing to map natural language utterances onto formal meaning representations.
Outcome:	The proposed method outperforms the fine-tuned model on low-resource splits of Overnight and TOPv2 on language representations with increasing model scale and target representations.

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Inbal Magar, Roy Schwartz

Challenge:	Pretrained language models are typically trained on web-based datasets that are often "contaminated" with downstream test sets.
Approach:	They propose a method to pretrain BERT models on Wikipedia and labeled downstream datasets and fine-tune them on the relevant task.
Outcome:	The proposed method compares models on Wikipedia and labeled downstream datasets on two models and three downstream tasks.

Detecting Annotation Errors in Morphological Data with the Transformer (2022.acl-short)

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Ling Liu, Mans Hulden

Estimating the Entropy of Linguistic Distributions (2022.acl-short)

Challenge:	Annotation errors that stem from various sources are usually unavoidable when performing large-scale annotation of linguistic data.
Approach:	They evaluate the feasibility of using a deep learning model to detect annotator errors in morphological data sets that contain inflected word forms.
Outcome:	The proposed model detects typographic errors, linguistic confusion errors and self-adversarial errors on four languages.

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Aryaman Arora, Clara Meister, Ryan Cotterell

Challenge:	Shannon entropy is a quantity of interest for linguists studying the communicative capacity of human language.
Approach:	They propose to use entropy estimators to estimate linguistic effects from observed data . they propose to recommend better entropic estimators for future linguistic studies .
Outcome:	The proposed estimators are over-estimated due to poor entropy estimators, the authors argue . they recommend the same estimators be used in future linguistic studies.

Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study (2022.acl-short)

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David Guriel, Omer Goldman, Reut Tsarfaty

DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization (2022.acl-short)

Challenge:	morphological annotations are a common problem in some languages, but the flat structure of the current schema makes it impossible to treat them.
Approach:	They propose a general solution for polypersonal agreement in Georgian language . they extend the existing UniMorph annotation schema to address this problem .
Outcome:	The proposed framework covers all possible variants of argument marking, and is accurate and balanced.

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Zheng Li, Zijian Wang, Ming Tan, Ramesh Nallapati, Parminder Bhatia, Andrew Arnold, Bing Xiang, Dan Roth

Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension (2022.acl-short)

Challenge:	Empirical analyses show that pre-trained sequence-to-sequence models can achieve a 16.5x model footprint compression ratio with little performance drop relative to full-precision counterparts.
Approach:	They propose to distill and quantize pre-trained sequence-to-sequence models to reduce memory and latency requirements.
Outcome:	Empirical results show that the proposed model achieves 16.5x model footprint compression ratio with little performance drop relative to full-precision counterparts on multiple summarization and QA datasets.

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Chao Zhao, Wenlin Yao, Dian Yu, Kaiqiang Song, Dong Yu, Jianshu Chen

Kronecker Decomposition for GPT Compression (2022.acl-short)

Challenge:	Existing models for dialogue comprehension are not available for the pre-training of such a model.
Approach:	They propose a narrative-guided pre-training strategy that learns by narrating key information from a dialogue input.
Outcome:	The proposed model performs better on four dialogue-based tasks and is comparable to existing models.

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Ali Edalati, Marzieh Tahaei, Ahmad Rashid, Vahid Nia, James Clark, Mehdi Rezagholizadeh

Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction (2022.acl-short)

Challenge:	GPT is an auto-regressive Transformer-based pre-trained language model . but its huge size can be prohibitive for deploying on low capacity devices .
Approach:	They use a Kronecker decomposition technique to compress GPT models . they use ILKD to refine the model on downstream tasks .
Outcome:	The proposed model outperforms the existing DistilGPT2 model on language modeling and general language understanding evaluation benchmark tasks.

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Keiji Shinzato, Naoki Yoshinaga, Yandi Xia, Wei-Te Chen

Event-Event Relation Extraction using Probabilistic Box Embedding (2022.acl-short)

Challenge:	Existing approaches to extract value from product data for a large number of attributes are not effective for rare and ambiguous attributes.
Approach:	They propose to use attributes as knowledge to expand AVE queries by retrieving possible answers from training data.
Outcome:	The proposed model improves on a cleaned version of AliExpress dataset for rare and ambiguous attributes, especially for rare attributes.

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EunJeong Hwang, Jay-Yoon Lee, Tianyi Yang, Dhruvesh Patel, Dongxu Zhang, Andrew McCallum

Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation (2022.acl-short)

Challenge:	Existing frameworks of event relation extraction do not guarantee coherence between different relation types, such as anti-symmetry.
Approach:	They propose to modify existing ERE framework to guarantee coherence by representing each event as a box representation without applying explicit constraints.
Outcome:	The proposed model shows stronger conjunctive constraint satisfaction compared to previous models with constraint injection.

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Tsz Kin Lam, Shigehiko Schamoni, Stefan Riezler

Predicting Sentence Deletions for Text Simplification Using a Functional Discourse Structure (2022.acl-short)

Challenge:	End-to-end speech translation relies on data that pair source-language speech inputs with corresponding translations.
Approach:	They propose a method that augments transcriptions by sampling from suffix memory and translating them into target languages.
Outcome:	The proposed method delivers up to 0.9 and 1.1 BLEU points on top of augmentation with knowledge distillation on languages on CoVoST 2 and Europarl-ST.

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Bohan Zhang, Prafulla Kumar Choubey, Ruihong Huang

Text Generation

Challenge:	Document-level text simplification often deletes some sentences to reduce text complexity.
Approach:	They use a news genre-specific functional discourse structure to predict sentence deletions . they incorporate sentence categories into a neural net model to improve recall .
Outcome:	The proposed model improves the recall of deletion prediction by 6.5% and 10.7%, and the overall F1 score by 3.6% and 4.3% respectively.

Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer (2022.acl-short)

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Huiyuan Lai, Antonio Toral, Malvina Nissim

When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning (2022.acl-short)

Challenge:	Text style transfer is a text generation task where a given sentence must be rewritten changing its style while preserving its meaning.
Approach:	They propose a modular approach for multilingual formality transfer using machine translated data and gold aligned English sentences.
Outcome:	The proposed approach achieves competitive performance without monolingual task-specific parallel data and can be applied to other style transfer tasks as well as to other languages.

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Orion Weller, Kevin Seppi, Matt Gardner

Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing (2022.acl-short)

Challenge:	Transfer learning (TL) in natural language processing has seen a surge of interest in recent years . pre-trained models have shown impressive ability to transfer to novel tasks .
Approach:	They compare two different methods of transfer learning in natural language processing to find out which is better.
Outcome:	The proposed methods perform better when the target task has fewer instances than the supporting task and vice versa.

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Runxin Sun, Shizhu He, Chong Zhu, Yaohan He, Jinlong Li, Jun Zhao, Kang Liu

Programming Languages in NLP

Challenge:	Text-to-SQL parsing aims to parse natural language questions into SQL queries . current attention-based approaches can only model alignments at the token level .
Approach:	They propose a method to leverage explicit lexico-logical alignments by identifying possible phrase-level alignments and injecting them as additional contexts into the parsing procedure.
Outcome:	The proposed approach improves performance by 3.4% on Squall.

Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning (2022.acl-short)

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Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, Xueqi Cheng

Structured Data in NLP

Challenge:	Existing models for TKG reasoning focus on modeling fact sequences of a fixed length, which cannot discover complex evolutional patterns that vary in length.
Approach:	They propose to use a length-aware Convolutional Neural Network to handle evolutional patterns of different lengths via an easy-to-difficult curriculum learning strategy.
Outcome:	The proposed model improves performance under both offline and online learning strategies.

Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking (2022.acl-short)

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Takyoung Kim, Hoonsang Yoon, Yukyung Lee, Pilsung Kang, Misuk Kim

LM-BFF-MS: Improving Few-Shot Fine-tuning of Language Models based on Multiple Soft Demonstration Memory (2022.acl-short)

Challenge:	Existing evaluation metrics for dialog state tracking are limited for belief states accumulated as dialog proceeds . relative slot accuracy allows intuitive evaluation by assigning relative scores according to the turn of each dialog .
Approach:	They propose to use relative slot accuracy to complement existing evaluation metrics . joint goal accuracy and slot accuracy are used to evaluate accumulated belief states .
Outcome:	The proposed metrics focus on "penalizing states that fail to predict," not "reward for well-predicted states" the proposed metrics do not depend on the number of predefined slots, and allow intuitive evaluation .

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Eunhwan Park, Donghyeon Jeon, Seonhoon Kim, Inho Kang, Seung-Hoon Na

Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level Performances (2022.acl-short)

Challenge:	LM-BFF (CITATION) achieves significant few-shot performance by using auto-generated prompts and adding demonstrations similar to an input example.
Approach:	They propose to use auto-generated prompts and add demonstrations to LM-BFF to improve few-shot fine-tuning of language models with multiple soft demonstrations.
Outcome:	The proposed method improves few-shot fine-tuning on eight NLP tasks.

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Suvodip Dey, Ramamohan Kummara, Maunendra Desarkar

Exploiting Language Model Prompts Using Similarity Measures: A Case Study on the Word-in-Context Task (2022.acl-short)

Challenge:	Dialogue State Tracking (DST) is a task-oriented conversational agent that keeps track of key information exchanged during a conversation.
Approach:	They propose a new evaluation metric called Flexible Goal Accuracy to address shortcomings of JGA.
Outcome:	The proposed metric improves on existing metrics and improves performance of turn-level and non-cumulative belief state models.

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Mohsen Tabasi, Kiamehr Rezaee, Mohammad Taher Pilehvar

Hierarchical Curriculum Learning for AMR Parsing (2022.acl-short)

Challenge:	Existing few-shot approaches fail on the semantic distinction task of the Word-in-Context dataset.
Approach:	They propose a prompt-based approach which boosts few-shot performance to the level of fully supervised methods by using similarity metrics.
Outcome:	The proposed technique boosts few-shot performance to the level of fully supervised methods.

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Peiyi Wang, Liang Chen, Tianyu Liu, Damai Dai, Yunbo Cao, Baobao Chang, Zhifang Sui

PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction (2022.acl-short)

Challenge:	Abstract Meaning Representation (AMR) parsing aims to translate sentences to semantic representation with a hierarchical structure, but there is a gap between their flat training objective and the hierarchic structure, which limits the model generalization.
Approach:	They propose a Hierarchical Curriculum Learning framework with Structure-level (SC) and Instance-level curricula (IC) that aims to translate sentences to semantic representation with a hierarchical structure.
Outcome:	Experiments on AMR2.0, AMR3.0, structure-complex and out-of-distribution situations confirm the effectiveness of the proposed framework.

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Vipul Rathore, Kartikeya Badola, Parag Singla, null Mausam

To Find Waldo You Need Contextual Cues: Debiasing Who’s Waldo (2022.acl-short)

Challenge:	Recent approaches to distantly supervised relation extraction (DS-RE) encode each sentence in an entity-pair bag separately.
Approach:	They propose a simple baseline approach where sentences of a bag are concatenated into a passage of sentences and encoded jointly using BERT.
Outcome:	The proposed approach outperforms state-of-the-art models in monolingual and multilingual datasets.

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Yiran Luo, Pratyay Banerjee, Tejas Gokhale, Yezhou Yang, Chitta Baral

Translate-Train Embracing Translationese Artifacts (2022.acl-short)

Challenge:	Existing datasets for Person-centric visual grounding (PCVG) are biased and over-estimated .
Approach:	They propose to use a dataset for the Person-centric Visual Grounding task that requires pairing up a person’s name mentioned in a caption with a bounding box that points to the person in the image.
Outcome:	The proposed dataset offers a more practical benchmark for future improvements and better performance than the original dataset.

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Sicheng Yu, Qianru Sun, Hao Zhang, Jing Jiang

Multilinguality

Challenge:	Existing approaches to train multilingual tasks are based on translationese and translatetrain.
Approach:	They propose to use translationese to mitigate the gap between the source and target languages to train the translator.
Outcome:	The proposed method outperforms baselines on the multilingual QA dataset TyDiQA.

C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References (2022.acl-short)

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Xiang Yue, Xiaoman Pan, Wenlin Yao, Dian Yu, Dong Yu, Jianshu Chen

k-Rater Reliability: The Correct Unit of Reliability for Aggregated Human Annotations (2022.acl-short)

Challenge:	Existing approaches to pretrain open-domain question answering systems lack task-specific annotations.
Approach:	They propose to pretrain a two-stage open-domain question answering system with strong transfer capabilities by using a dictionary and a large-scale corpus.
Outcome:	The proposed approach leads to 2%-10% gains in top-20 accuracy and improves with reader.

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Ka Wong, Praveen Paritosh

Challenge:	aggregation has been a common strategy for dealing with unreliable data since the inception of crowdsourcing . however, many applications that rely on aggregate ratings only report the reliability of individual ratings, which is the incorrect unit of analysis.
Approach:	They propose to use k-rater reliability (kRR) to determine the correct data reliability for crowdsourced datasets.
Outcome:	The proposed method produces similar results on WordSim-353 datasets.

An Embarrassingly Simple Method to Mitigate Undesirable Properties of Pretrained Language Model Tokenizers (2022.acl-short)

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Valentin Hofmann, Hinrich Schuetze, Janet Pierrehumbert

SCD: Self-Contrastive Decorrelation of Sentence Embeddings (2022.acl-short)

Challenge:	a standard tokenizer does not cover all characters of a word but preserves key aspects of its morphological structure . a novel method to improve tokenization of pretrained language models is proposed .
Approach:	They propose a method to improve the tokenization of pretrained language models . they use the vocabulary of a standard tokenizer but preserves morphological structure .
Outcome:	The proposed method improves tokenization of pretrained language models on morphological gold segmentations and text classification tasks.

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Tassilo Klein, Moin Nabi

Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words (2022.acl-short)

Challenge:	Existing methods for self-supervised learning of representations are based on contrastive learning.
Approach:	They propose a self-supervised approach that optimizes a joint decorrelation and self-contrastive objective by leveraging the contrast arising from standard dropout at different rates.
Outcome:	The proposed method achieves comparable results with state-of-the-art methods on multiple benchmarks without using contrastive pairs.

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Kaitlyn Zhou, Kawin Ethayarajh, Dallas Card, Dan Jurafsky

Revisiting the Compositional Generalization Abilities of Neural Sequence Models (2022.acl-short)

Challenge:	We find that word similarities estimated by cosine over contextual embeddings are understated and trace this effect to training data frequency.
Approach:	They propose to use cosine similarity to estimate word similarities in contextual embeddings to trace this effect to training data frequency.
Outcome:	The proposed model underestimates similarity between frequent and low frequency words even after controlling for polysemy and other factors.

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Arkil Patel, Satwik Bhattamishra, Phil Blunsom, Navin Goyal

Challenge:	Existing studies have suggested that standard seq-to-seq models lack the ability to generalize compositionally.
Approach:	They propose to use one-shot primitive generalization as introduced by the popular SCAN benchmark to modify the training distribution in simple and intuitive ways to achieve near-perfect generalization performance.
Outcome:	The proposed model achieves near-perfect generalization performance despite a lack of training data .

A Copy-Augmented Generative Model for Open-Domain Question Answering (2022.acl-short)

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Shuang Liu, Dong Wang, Xiaoguang Li, Minghui Huang, Meizhen Ding

Information Retrieval

Challenge:	Existing open-domain question answering approaches follow a two-stage paradigm retriever then reader.
Approach:	They propose a novel reader-based generative approach that incorporates extractive and generative readers.
Outcome:	The proposed model improves on two benchmark datasets, Natural Questions and TriviaQA.

Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation (2022.acl-short)

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Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong Park

Representation Learning

Challenge:	Existing sparse retrieval models rely on term-based matching to retrieve relevant documents.
Approach:	They propose a framework which augments the representations of documents with interpolation and perturbation.
Outcome:	The proposed framework significantly outperforms baselines on the dense retrieval of both the labeled and unlabeled documents.

WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language (2022.acl-short)

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Federico Tavella, Viktor Schlegel, Marta Romeo, Aphrodite Galata, Angelo Cangelosi

Investigating person-specific errors in chat-oriented dialogue systems (2022.acl-short)

Challenge:	Signed Language Processing (SLP) is a major form of NLP, but has been overlooked by the NLP community.
Approach:	They leverage existing resources to construct a large-scale dataset of American Sign Language signs annotated with six different phonological properties.
Outcome:	The proposed model outperforms existing approaches on signs unobserved during training.

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Koh Mitsuda, Ryuichiro Higashinaka, Tingxuan Li, Sen Yoshida

Natural Language Interfaces

Challenge:	Errors in general chatbots and chatbot that follow a rough persona have been studied . but those in chatbot based on real people have not been thoroughly investigated .
Approach:	They analyze dialogue data of a generation-based chatbot trained from dialogue data . they find errors in attributes and relations can be divided into two levels: self and other .
Outcome:	The results show that errors in chatbots can be divided into two types . the correspondence with an existing taxonomy of errors was also investigated .

Direct parsing to sentiment graphs (2022.acl-short)

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David Samuel, Jeremy Barnes, Robin Kurtz, Stephan Oepen, Lilja Øvrelid, Erik Velldal

XDBERT: Distilling Visual Information to BERT from Cross-Modal Systems to Improve Language Understanding (2022.acl-short)

Challenge:	Existing methods for structured sentiment analysis (SSA) focus on subcomponents of sentiment graphs without explicitly expressing their relations or the polarity.
Approach:	They propose a graph-based semantic parser which directly predicts sentiment graphs from text without reliance on lossy conversions to intermediate dependency representations.
Outcome:	The proposed model performs on 4 out of 5 standard benchmark sets and compares with dependency-based models on the more structurally complex datasets.

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Chan-Jan Hsu, Hung-yi Lee, Yu Tsao

As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning (2022.acl-short)

Challenge:	XDBERT (cross-modal distilled BERT) outperforms pretrained-BERT in general language understanding evaluation (GLUE), situations with adversarial generations (SWAG) benchmarks, and readability benchmarks.
Approach:	They propose to distill visual information from pretrained multimodal transformers to pretrained language encoders to cater to the language-heavy characteristics of NLU.
Outcome:	The proposed framework outperforms pretrained-BERT in general language understanding evaluation (GLUE), situations with adversarial generations (SWAG), and readability benchmarks.

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Jannis Vamvas, Rico Sennrich

How Distributed are Distributed Representations? An Observation on the Locality of Syntactic Information in Verb Agreement Tasks (2022.acl-short)

Challenge:	Neural machine translation is susceptible to coverage errors such as the addition of superfluous target words or the omission of important source content.
Approach:	They propose a method for detecting Omission and addition errors with off-the-shelf translation models.
Outcome:	The proposed method is comparable to a supervised method that requires a custom quality estimation model.

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Bingzhi Li, Guillaume Wisniewski, Benoit Crabbé

Representation Learning

Challenge:	Using probing, causal analysis and feature selection, we find that syntactic information is encoded locally in the transformers representations consistent with the French grammar.
Approach:	They address the question of the localization of syntactic information encoded in transformers representations by probing, causal analysis and feature selection methods.
Outcome:	The proposed representations are consistent with the object-past participle agreement in French and are consistent in both languages.

Machine Translation for Livonian: Catering to 20 Speakers (2022.acl-short)

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Matīss Rikters, Marili Tomingas, Tuuli Tuisk, Valts Ernštreits, Mark Fishel

Multilinguality

Challenge:	Livonian is one of the most endangered languages in Europe with just a tiny handful of speakers and virtually no publicly available corpora.
Approach:	They aim to develop machine translation between Livonian and English using a linguistic similarity test and a dataset of parallel and monolingual data.
Outcome:	The proposed systems and the collected data, including a manually translated and verified translation benchmark, are publicly released via OPUS and Huggingface repositories.

Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games (2022.acl-short)

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Dongwon Ryu, Ehsan Shareghi, Meng Fang, Yunqiu Xu, Shirui Pan, Reza Haf

A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models (2022.acl-short)

Challenge:	Existing RL agents are far away from solving text-based games due to their combinatorially large action spaces that hinders efficient exploration.
Approach:	They propose an exploration technique that injects external commonsense knowledge, via a pretrained language model, into the agent during training when the agent is the most uncertain about its next action.
Outcome:	The proposed method exhibits improvement on the collected game scores during the training in four out of nine games from Jericho.

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Deming Ye, Yankai Lin, Peng Li, Maosong Sun, Zhiyuan Liu

S4-Tuning: A Simple Cross-lingual Sub-network Tuning Method (2022.acl-short)

Challenge:	Existing pre-trained language models cannot recall factual knowledge of entities exhibited in large-scale corpora, especially those rare entities.
Approach:	They propose to build a pluggable Entity Lookup Table (PELT) on demand by aggregating the entity’s output representations of multiple occurrences in the corpora.
Outcome:	The proposed model can transfer entity knowledge from out-of-domain corpora into PLMs with different architectures.

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Runxin Xu, Fuli Luo, Baobao Chang, Songfang Huang, Fei Huang

Cross-Lingual Transfer

Challenge:	Existing multilingual pre-trained language models allow to adapt to target languages with only few labeled examples.
Approach:	They propose a simple cross-lingual sub-network tuning method that detects the most essential sub-netzwork for each target language and updates it during fine-tuning.
Outcome:	The proposed method improves on three multi-lingual tasks involving 37 different languages.

Region-dependent temperature scaling for certainty calibration and application to class-imbalanced token classification (2022.acl-short)

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Hillary Dawkins, Isar Nejadgholi

Developmental Negation Processing in Transformer Language Models (2022.acl-short)

Challenge:	Existing calibration errors are insensitive to large errors in low and mid-range certainty regions.
Approach:	They propose a calibration error metric that weights all certainty regions equally.
Outcome:	The proposed calibration error metric reduces calibration errors over existing baselines by reducing low and mid certainty estimates.

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Antonio Laverghetta Jr., John Licato

Canary Extraction in Natural Language Understanding Models (2022.acl-short)

Challenge:	Negation is an important construct in language for reasoning over the truth of propositions, garnering interest from philosophy (Horn, 1989) and psycholinguistics (Zwaan, 2012).
Approach:	They propose to frame a natural language inference task as a problem and examine how well transformers can process negation categories.
Outcome:	The proposed models perform better on certain categories, suggesting clear differences in how they are processed.

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Rahil Parikh, Christophe Dupuy, Rahul Gupta

Robustness in NLP

Challenge:	Recent literature has focused on Model Inversion Attacks (ModIvA) that can extract training data from model parameters.
Approach:	They propose an attack that extracts canaries from NLU training data and reconstructs them using non-sensitive tokens.
Outcome:	The proposed attack can reconstruct a four digit code in the training dataset with a probability of 0.5 in its best configuration.

On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations (2022.acl-short)

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Yang Trista Cao, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta, Varun Kumar, Jwala Dhamala, Aram Galstyan

Sequence-to-sequence AMR Parsing with Ancestor Information (2022.acl-short)

Challenge:	Recent natural language processing systems use large language models as the backbone . however, societal biases are encoded in these models and transferred to downstream applications .
Approach:	They propose to use two categories to measure fairness in natural language processing tasks . they find intrinsic and extrinsic metrics do not correlate in their original setting .
Outcome:	The proposed metrics do not correlate in their original setting, the authors show . they find that they are not accurate when correcting for metric misalignments and noise .

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Chen Yu, Daniel Gildea

Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning (2022.acl-short)

Challenge:	Abstract Meaning Representation (AMR) is a graph that encodes the semantic meaning of a sentence.
Approach:	They propose several strategies to add important ancestor information into a Transformer Decoder.
Outcome:	The proposed methods improve performance for both AMR 2.0 and AMR 3.0 datasets and achieve new state-of-the-art results.

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Miryam de Lhoneux, Sheng Zhang, Anders Søgaard

PriMock57: A Dataset Of Primary Care Mock Consultations (2022.acl-short)

Challenge:	Large multilingual pretrained language models such as mBERT and XLM-RoBERTa have been found to be effective for cross-lingual transfer of syntactic parsing models but only between related languages.
Approach:	They propose to use multi-task learning to dynamically optimize for parsing performance on outlier languages by using a multi-level learning approach.
Outcome:	The proposed method significantly outperforms uniform and size-proportional sampling in the zero-shot setting.

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Alex Papadopoulos Korfiatis, Francesco Moramarco, Radmila Sarac, Aleksandar Savkov

UniGDD: A Unified Generative Framework for Goal-Oriented Document-Grounded Dialogue (2022.acl-short)

Challenge:	Recent advances in Automatic Speech Recognition (ASR) have made it possible to reliably produce automatic transcripts of clinician-patient conversations.
Approach:	They present a public access, high quality dataset of 57 mocked primary care consultations . they aim to offer a benchmark for conversational medical ASR and consultation note generation from transcripts.
Outcome:	The proposed dataset can be used as a benchmark for conversational medical ASR and consultation note generation from transcripts.

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Chang Gao, Wenxuan Zhang, Wai Lam

DMix: Adaptive Distance-aware Interpolative Mixup (2022.acl-short)

Challenge:	Existing studies tackle the problem of error propagation by decomposing the goal-oriented document-grounded dialogue into two sub-tasks.
Approach:	They propose to unify knowledge identification and response generation into two sub-tasks by sequentially generating grounding knowledge and response.
Outcome:	The proposed framework unifies knowledge identification and response generation and models their characteristics using a prompt-connected multi-task learning strategy.

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Ramit Sawhney, Megh Thakkar, Shrey Pandit, Ritesh Soun, Di Jin, Diyi Yang, Lucie Flek

Challenge:	Interpolation-based regularisation methods such as Mixup have shown to be effective for various tasks and modalities.
Approach:	They propose an adaptive distance-aware interpolative Mixup that selects samples based on their diversity in the embedding space.
Outcome:	The proposed method achieves state-of-the-art on sentence classification over existing methods on 8 benchmark datasets across English, Arabic, Turkish, and Hindi languages while achieving benchmark F1 scores in 3 times less number of iterations.

Sub-Word Alignment is Still Useful: A Vest-Pocket Method for Enhancing Low-Resource Machine Translation (2022.acl-short)

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Minhan Xu, Yu Hong

HYPHEN: Hyperbolic Hawkes Attention For Text Streams (2022.acl-short)

Challenge:	Low-resource machine translation (MT) is challenging due to the scarcity of parallel data and lack of bilingual dictionaries.
Approach:	They propose to leverage embedding duplication between aligned sub-words to extend the Parent-Child transfer learning method to improve low-resource machine translation.
Outcome:	The proposed method achieves BLEU scores of 22.5, 28.0 and 18.1 respectively.

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Shivam Agarwal, Ramit Sawhney, Sanchit Ahuja, Ritesh Soun, Sudheer Chava

Challenge:	Existing methods for text stream modeling ignore fine-grained timing irregularities and time-varying scale-free properties of texts.
Approach:	They propose a hyperbolic Hawkes Attention Network which learns a data-driven hyperbolical space and models irregular powerlaw excitations using a Hawke's process.
Outcome:	The proposed model can model online text sequences in a geometry agnostic manner.

A Risk-Averse Mechanism for Suicidality Assessment on Social Media (2022.acl-short)

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Ramit Sawhney, Atula Neerkaje, Manas Gaur

Challenge:	Social media has become a platform for users to express suicidal thoughts outside traditional clinical settings.
Approach:	They propose a risk-averse hierarchical attention classifier that refrains from making uncertain predictions on real-world Reddit data.
Outcome:	The proposed system can refrain from 83% of incorrect predictions on real-world Reddit data.

When classifying grammatical role, BERT doesn’t care about word order... except when it matters (2022.acl-short)

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Isabel Papadimitriou, Richard Futrell, Kyle Mahowald

Triangular Transfer: Freezing the Pivot for Triangular Machine Translation (2022.acl-short)

Challenge:	Recent work has shown large language models are surprisingly word order invariant . however, word order knowledge is crucial in defining later-layer representations of words .
Approach:	They probe grammatical role representations in English BERT and GPT-2 to find word order crucial . they find word orders are crucial in defining later-layer representations of words in non-prototypical positions .
Outcome:	The proposed model is based on natural prototypical inputs where word order is crucial for correct classification.

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Meng Zhang, Liangyou Li, Qun Liu

Can Visual Dialogue Models Do Scorekeeping? Exploring How Dialogue Representations Incrementally Encode Shared Knowledge (2022.acl-short)

Challenge:	Existing approaches to triangular machine translation have not fully exploited all types of auxiliary data.
Approach:	They propose a transfer-learning-based approach that utilizes all types of auxiliary data.
Outcome:	The proposed approach outperforms existing approaches with a series of experiments.

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Brielen Madureira, David Schlangen

Focus on the Target’s Vocabulary: Masked Label Smoothing for Machine Translation (2022.acl-short)

Challenge:	Existing evaluation methods for visual dialogue models are not consistent .
Approach:	They propose a theory-based evaluation method to examine to what degree visual dialogue models incrementally build up representations that do scorekeeping.
Outcome:	The proposed method aims to determine to what degree models build up representations that are appropriate to do scorekeeping of shared commitments throughout a visual dialogue.

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Liang Chen, Runxin Xu, Baobao Chang

Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification (2022.acl-short)

Challenge:	Label smoothing and vocabulary sharing are widely used in neural machine translation models, but they can be conflicting and lead to suboptimal performance.
Approach:	They propose a mechanism that masks the soft label probability of source-side words to zero and integrates label smoothing with vocabulary sharing to improve translation quality.
Outcome:	The proposed mechanism improves translation quality and model calibration on bilingual and multilingual datasets, while retaining the original smoothing method.

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Xi’ao Su, Ran Wang, Xinyu Dai

NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better (2022.acl-short)

Challenge:	Existing methods for multi-label text classification neglect the knowledge from the existing similar instances when predicting labels of a specific text.
Approach:	They propose a k nearest neighbor mechanism which retrieves several neighbor instances and interpolates the model output with their labels.
Outcome:	Extensive experiments show that the proposed method can bring significant performance improvements to multiple MLTC models including state-of-the-art pretrained and non-pretrained ones.

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Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang

Adjusting the Precision-Recall Trade-Off with Align-and-Predict Decoding for Grammatical Error Correction (2022.acl-short)

Challenge:	Existing methods for finetuning pretrained language models (PLMs) have risks in overfitting the pretraining tasks and data, which may lead to suboptimal performance.
Approach:	They propose a method which adds noise to parameters of PLMs before fine-tuning.
Outcome:	The proposed method can be used on GLUE English and XTREME multilingual benchmarks.

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Xin Sun, Houfeng Wang

On the Effect of Isotropy on VAE Representations of Text (2022.acl-short)

Challenge:	Modern writing assistance applications always contain a Grammatical Error Correction (GEC) model to correct errors in user-entered sentences.
Approach:	They propose a simple yet effective approach to Align-and-Predict Decoding for most popular sequence-to-sequence models to offer more flexibility for the precision-recall trade-off.
Outcome:	The proposed model can be used in both English and Chinese GEC models and achieve state-of-the-art results.

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Lan Zhang, Wray Buntine, Ehsan Shareghi

Representation Learning

Challenge:	Injecting desired geometric properties into text representations has attracted a lot of attention due to its better utilisation of representation space.
Approach:	They propose to use an isotropic Gaussian posterior instead of the ellipsoidal Gausssian priori to inject isotropy into text representations.
Outcome:	The proposed method improves classification performance, robustness to input perturbation, and generative behavior compared to the ellipsoidal Gaussian posterior.

Efficient Classification of Long Documents Using Transformers (2022.acl-short)

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Hyunji Park, Yogarshi Vyas, Kashif Shah

Information Retrieval

Challenge:	Several methods have been proposed for classifying long textual documents using Transformers, but there is a lack of consensus on a benchmark to enable a fair comparison among different approaches.
Approach:	They propose to use a dataset to evaluate the relative efficacy of various models for long document classification using Transformers.
Outcome:	The proposed models outperform simple baseline models and yield inconsistent performance across datasets.

Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation (2022.acl-short)

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Lisa Jin, Daniel Gildea

An Analysis of Negation in Natural Language Understanding Corpora (2022.acl-short)

Challenge:	Automatic evaluation metrics score natural language generation systems based on how well they lexically align to humanannotated references.
Approach:	They propose to replace greedy alignments in BERTScore with optimized ones that replace the n-gram matching BERTAcore metrics with a token embedding to prevent domain mismatch.
Outcome:	The proposed metrics outperform cross-entropy and BLEU reward baselines on AMR-to-text generation.

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Md Mosharaf Hossain, Dhivya Chinnappa, Eduardo Blanco

Challenge:	Using annotator-generated examples, one can evaluate systems with synthetic language that is not representative of language in the wild.
Approach:	They analyze negation in eight popular corpora spanning six natural language understanding tasks.
Outcome:	The proposed corpora have few negations compared to general-purpose English and are often unimportant . state-of-the-art transformers obtain significantly worse results with instances that contain negation, especially if the negations are important.

Primum Non Nocere: Before working with Indigenous data, the ACL must confront ongoing colonialism (2022.acl-short)

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Lane Schwartz

Unsupervised multiple-choice question generation for out-of-domain Q&A fine-tuning (2022.acl-short)

Challenge:	aCL researchers must acknowledge that Indigenous languages are not merely low resource languages . authors propose that the ACL draft and adopt an ethical framework for NLP research involving Indigenous languages based on the legacy of colonialism .
Approach:	They propose that the ACL draft and adopt an ethical framework for NLP researchers . they propose to draw on best practices drawn from the Indigenous studies literature .
Outcome:	The proposed ethical framework is drawn from the Indigenous studies literature . it would be ethical for researchers to engage with Indigenous languages .

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Guillaume Le Berre, Christophe Cerisara, Philippe Langlais, Guy Lapalme

Low-Resource NLP

Challenge:	Pre-trained models have shown very good performances on a number of question answering benchmarks especially when fine-tuned on multiple question answering datasets at once.
Approach:	They propose an approach to fine-tune a question-answering dataset using a rule-based algorithm that generates questions and answers from unannotated sentences.
Outcome:	The proposed model can generate questions and answers from unannotated sentences on a multiple-choice physics, biology and chemistry benchmark.

Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models (2022.acl-short)

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Ling Liu, Mans Hulden

Probing the Robustness of Trained Metrics for Conversational Dialogue Systems (2022.acl-short)

Challenge:	morphological inflection models have been successful with shared tasks . but they fail at generalizing inflation patterns when trained on a limited number of lemmata .
Approach:	They find that standard models fail at generalizing inflection patterns when trained on a limited number of lemmata and asked to inflect previously unseen lemma.
Outcome:	The proposed model can perform well on morphological inflection tasks if training data covers a diversity of lemmata or some variant of the input lemma has been witnessed during training.

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Jan Deriu, Don Tuggener, Pius Von Däniken, Mark Cieliebak

Robustness in NLP

Challenge:	Existing methods for evaluating conversational dialogue systems have been shown to be inefficient and instabile.
Approach:	They propose an adversarial method to stress-test trained metrics for evaluation of conversational dialogue systems using Reinforcement Learning.
Outcome:	The proposed method outperforms existing methods and can be applied to stress-test trained metrics for conversational dialogue systems.

Rethinking and Refining the Distinct Metric (2022.acl-short)

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Siyang Liu, Sahand Sabour, Yinhe Zheng, Pei Ke, Xiaoyan Zhu, Minlie Huang

Challenge:	Existing methods for calculating distinct scores have evident biases that assign higher penalties to longer sequences.
Approach:	They propose to scale the number of distinct tokens based on their expectations.
Outcome:	The proposed metric removes evident biases in the original distinct score . the proposed meter correlates better with human judgment in evaluating response diversity .

How reparametrization trick broke differentially-private text representation learning (2022.acl-short)

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Ivan Habernal

Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution (2022.acl-short)

Challenge:	Differential privacy (DP) is a formal mathematical treatment of privacy protection . it guarantees how much privacy can be lost in the worst case . adapting DP mechanisms to NLP properly is largely non-trivial task .
Approach:	They propose to use differential privacy to learn text representations using DPText to quantify and guarantee how much privacy can be lost in the worst case.
Outcome:	The proposed methods are falsely claimed to be differentially private and violate privacy loss guarantees.

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Klim Zaporojets, Johannes Deleu, Yiwei Jiang, Thomas Demeester, Chris Develder

A Flexible Multi-Task Model for BERT Serving (2022.acl-short)

Challenge:	Existing approaches to solve entity linking (EL) jointly with coreference resolution (coref) a coreferenced cluster can only be linked to a single entity or NIL (i.e., a nonlinkable entity)
Approach:	They propose to join entity linking and coreference resolution in a single structured prediction task over directed trees and use a globally normalized model to solve it.
Outcome:	The proposed model improves on two datasets with a +5% boost in accuracy compared to standalone models . the proposed model is based on current models that predict a single antecedent for each span to resolve .

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Tianwen Wei, Jianwei Qi, Shenghuan He

Understanding Game-Playing Agents with Natural Language Annotations (2022.acl-short)

Challenge:	a proposed BERT-based multi-task framework is suitable for iterative and incremental development of the tasks.
Approach:	They propose an efficient BERT-based multi-task framework that is suitable for iterative and incremental development of the tasks.
Outcome:	The proposed framework achieves 99.6% of performance of the full fine-tuning method while reducing up to two thirds of its overhead.

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Nicholas Tomlin, Andre He, Dan Klein

Challenge:	a new dataset contains 10K human-annotated Go games.
Approach:	They propose to use natural language annotations to predict game terms from Go models . they use linear probing to predict domain-specific terms from the model's intermediate state representations .
Outcome:	The proposed model can predict domain-specific terms from human-annotated Go games . the proposed model is based on a dataset containing 10K human-rannotated games based in the game .

Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding (2022.acl-short)

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Zheng Yuan, Chuanqi Tan, Songfang Huang

Challenge:	Existing methods for automatic ICD coding use label attention to match related text snippets.
Approach:	They propose to use code synonyms to leverage for better code representation learning.
Outcome:	The proposed method outperforms previous state-of-the-art methods on the MIMIC-III dataset.

CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment (2022.acl-short)

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Lütfi Kerem Senel, Timo Schick, Hinrich Schuetze

On the Importance of Effectively Adapting Pretrained Language Models for Active Learning (2022.acl-short)

Challenge:	Pretrained language models (PLMs) have achieved superhuman performance on many benchmarks, creating a need for harder tasks.
Approach:	They propose a benchmark that measures natural language understanding (NLU) abilities of pretrained language models.
Outcome:	The proposed benchmark measures the ability of pretrained language models to perform on many tasks.

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Katerina Margatina, Loic Barrault, Nikolaos Aletras

A Recipe for Arbitrary Text Style Transfer with Large Language Models (2022.acl-short)

Challenge:	Recent active learning approaches in NLP use off-the-shelf pretrained language models (LMs) . a poor training strategy can be catastrophic for AL, authors argue .
Approach:	They propose to first adapt the pretrained LM to the target task and then use it for AL.
Outcome:	The proposed approach provides substantial data efficiency improvements compared to the standard fine-tuning approach.

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Emily Reif, Daphne Ippolito, Ann Yuan, Andy Coenen, Chris Callison-Burch, Jason Wei

Low-Resource NLP

Challenge:	augmented zero-shot learning is a prompting method that allows large language models to perform zero-shoot text style transfer to arbitrary styles, without any model fine-tuning or exemplars in the target style.
Approach:	They propose a prompting method that frames style transfer as a sentence rewriting task and requires only a natural language instruction.
Outcome:	The proposed method is based on a large language model and is shown to perform on standard style transfer tasks and arbitrary transformations.

DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction (2022.acl-short)

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Abhyuday Bhartiya, Kartikeya Badola, null Mausam

(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models’ Performance (2022.acl-short)

Challenge:	Existing benchmarking datasets for multilingual relation extraction have been lacking .
Approach:	They propose to use a new benchmark dataset to study multilingual relation extraction task by distant supervision.
Outcome:	The proposed task is performed on a multilingual relation extraction dataset using an mBERT encoder.

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Omer Goldman, David Guriel, Reut Tsarfaty

Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks (2022.acl-short)

Challenge:	Inflection tasks have gained a lot of traction in recent years, mostly via SIGMORPHON's shared-tasks.
Approach:	They propose to use split-by-lemma to challenge the generalization capacity of morphological inflection models by employing harder train-test splits.
Outcome:	The proposed method is based on a split-by-lemma method that challenges the generalization capacity of the models.

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Xing Wu, Chaochen Gao, Meng Lin, Liangjun Zang, Songlin Hu