Findings of the Association for Computational Linguistics: EMNLP 2021
K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (2021.findings-emnlp)
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| Challenge: | Existing pre-trained language models are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios. |
| Approach: | They propose a knowledge-injected pre-trained language model that can be transferred to both natural language understanding and generation tasks. |
| Outcome: | The proposed model significantly outperforms baselines across the board in e-commerce scenarios. |
Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts (2021.findings-emnlp)
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| Challenge: | Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. |
| Approach: | They develop a neural topic model which extracts topics from word co-occurrence graphs . Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. |
| Outcome: | Empirical results show that the proposed model can generate more coherent topics than baseline topic models. |
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering (2021.findings-emnlp)
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| Challenge: | Experimental results show that our model achieves state-of-the-art results on three SQA benchmarks. |
| Approach: | They propose a self-supervised training stage and a contrastive representation learning stage for spoken question answering with auxiliary tasks and augmentation strategies. |
| Outcome: | The proposed model achieves state-of-the-art results on three SQA benchmarks. |
Language Clustering for Multilingual Named Entity Recognition (2021.findings-emnlp)
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| Challenge: | Recent work in multilingual natural language processing has shown progress on tasks such as natural language inference and joint multilingual translation. |
| Approach: | They propose a technique that groups similar languages together by embeddings from a pre-trained masked language model and automatically discovering language clusters in this embeddable space. |
| Outcome: | The proposed technique outperforms baselines on 15 languages in the WikiAnn dataset showing meaningful multilingual transfer for low-resource languages (Swahili and Yoruba). |
Neural News Recommendation with Collaborative News Encoding and Structural User Encoding (2021.findings-emnlp)
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| Challenge: | Existing news recommendation models encode news title and content separately without leveraging the structural correlation of user browsing histories to reflect user interests explicitly. |
| Approach: | They propose a news recommendation framework consisting of collaborative news encoding and structural user encode to enhance news and user representation learning. |
| Outcome: | The proposed framework improves the performance of news recommendation on the MIND dataset. |
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data (2021.findings-emnlp)
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| Challenge: | despite considerable progress, most machine reading comprehension tasks lack sufficient training data to fully exploit powerful deep neural network models. |
| Approach: | They propose to use QA data to generate more training data for machine reading comprehension tasks by crowdsourcing . they first collect a large-scale multiple-choice QA dataset for Chinese, ExamQA, and then use incomplete, yet relevant snippets returned by a web search engine as the context for each QA instance. |
| Outcome: | The proposed model improves a Chinese MRC task with +5.1% accuracy and +3.8% exact match. |
A Web Scale Entity Extraction System (2021.findings-emnlp)
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| Challenge: | Existing systems for large-scale entity extraction are limited by the scale and variety of data available on internet platforms. |
| Approach: | They propose to build an entity extraction system for multiple document types at large scale using multi-modal Transformers. |
| Outcome: | The proposed system extracts multiple types of entities from multiple document types at large scale using multi-modal Transformers. |
Joint Multimedia Event Extraction from Video and Article (2021.findings-emnlp)
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Brian Chen, Xudong Lin, Christopher Thomas, Manling Li, Shoya Yoshida, Lovish Chum, Heng Ji, Shih-Fu Chang
| Challenge: | Existing methods to extract multimedia events from video and text are limited to video and images. |
| Approach: | They propose a task to jointly extract events from video and text documents . they propose 'self-supervised' cross-modal event coreference model and cross-mod transformer architecture . |
| Outcome: | The proposed method achieves 6.0% and 5.8% absolute F-score gain on video-article pairs . the proposed method can resolve coreference and extract multimodal event frames more accurately than existing methods. |
Fine-grained Semantic Alignment Network for Weakly Supervised Temporal Language Grounding (2021.findings-emnlp)
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| Challenge: | Existing weakly supervised methods for temporal language grounding lose the complexity of the video and the semantics of the sentence. |
| Approach: | They propose a candidate-free framework for weakly supervised Temporal Language Grounding . they use a token-by-clip cross-modal semantic alignment module to learn alignment . |
| Outcome: | The proposed framework achieves state-of-the-art on two widely-used benchmarks: ActivityNet-Captions, and DiDeMo. |
Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation (2021.findings-emnlp)
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| Challenge: | Existing methods to evaluate factual consistency in text summarization neglect the intrinsic cause of factual inconsistency or rely on auxiliary tasks. |
| Approach: | They propose a method to evaluate the factual consistency in text summarization via counterfactual estimation, which formulates the causal relationship between source document, generated summary, and the language prior. |
| Outcome: | The proposed metric improves correlation with human judgments and convenience of usage on three public abstractive text summarization datasets. |
Cross-Modal Retrieval Augmentation for Multi-Modal Classification (2021.findings-emnlp)
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| Challenge: | Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing. |
| Approach: | They propose a retrieval-augmented multi-modal transformer architecture for embedding images and captions in the same space. |
| Outcome: | The proposed approach improves visual question answering over strong baselines and hot-swapping indices. |
HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition (2021.findings-emnlp)
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| Challenge: | Existing studies for named entity recognition focus on flat NER, i.e., without nested entities, by sequence labeling methods. |
| Approach: | They propose a Hierarchical Transformer network which decomposes the input sentence into multi-grained spans and enhances the representation learning in a hierarchical manner. |
| Outcome: | The proposed method achieves much better performance than the state-of-the-art approaches on GENIA, ACE-2004, ace-2005 and NNE datasets. |
Improving Embedding-based Large-scale Retrieval via Label Enhancement (2021.findings-emnlp)
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| Challenge: | Existing methods for large-scale retrieval are trained with 0-1 hard labels that indicate whether a query is relevant to a document, ignoring rich information of the relevance degree. |
| Approach: | They propose to introduce label enhancement for the first time to characterize query-document relevance degree by embedding label distribution into contextual embeddables. |
| Outcome: | The proposed method significantly outperforms existing retrieval models and its counterparts equipped with two alternative methods on English and Chinese large-scale retrieval tasks. |
Improving Privacy Guarantee and Efficiency of Latent Dirichlet Allocation Model Training Under Differential Privacy (2021.findings-emnlp)
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| Challenge: | Latent Dirichlet allocation (LDA) is a widely used topic model to discover the latent semantic of text data. |
| Approach: | They propose to combine a subsampling method with CGS to improve efficiency while amplifying privacy by using a novel metric, the efficiency–privacy function. |
| Outcome: | The proposed algorithm improves efficiency while amplifying privacy while subsampling in CGS increases efficiency while preserving privacy. |
Generating Mammography Reports from Multi-view Mammograms with BERT (2021.findings-emnlp)
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Alexander Yalunin, Elena Sokolova, Ilya Burenko, Alexander Ponomarchuk, Olga Puchkova, Dmitriy Umerenkov
| Challenge: | Increasing numbers of new cases and deaths are observed in both developed and less developed countries due to the increasing population age. |
| Approach: | They propose a method to generate mammography reports given four images . they propose an encoder-decoder model that includes an encoded and a decoder . |
| Outcome: | The proposed method can localize salient regions on the input mammograms and generate a visually interpretable report. |
Euphemistic Phrase Detection by Masked Language Model (2021.findings-emnlp)
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| Challenge: | euphemisms are ordinary-sounding words with a secret meaning that are used to conceal information . a primary motive of their use on social media is to evade content moderation efforts . |
| Approach: | They propose to use social media to detect euphemisms without human effort . they first perform phrase mining on a raw text corpus to extract quality phrases . then they use word embedding similarities to select a set of euphoristic phrase candidates . |
| Outcome: | The proposed algorithm shows 20-50% higher detection accuracies than baselines. |
Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable (2021.findings-emnlp)
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| Challenge: | Multi-hop QA requires the machine to answer complex questions through finding multiple clues and reasoning, and provide explanatory evidence to demonstrate the reasoning process. |
| Approach: | They propose a three-stage framework based on complex question decomposition that decomposes the complex question, then reads the sub-questions and then performs numerical comparison to get the final answer. |
| Outcome: | The proposed framework achieves state-of-the-art in the 2WikiMultiHopQA dataset, with a winning joint F1 score of 53.58 on the leaderboard. |
Segmenting Natural Language Sentences via Lexical Unit Analysis (2021.findings-emnlp)
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| Challenge: | Recent work on sequence segmentation models suffer from invalid predictions and a lack of consistency. |
| Approach: | They propose a unified span-based model that embeds every span and computes a score for each segmentation candidate. |
| Outcome: | The proposed model achieves state-of-the-art on 6 of the 3 tasks tested. |
Dense Hierarchical Retrieval for Open-domain Question Answering (2021.findings-emnlp)
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| Challenge: | Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA) current dense retrievers require splitting documents into short passages that usually contain local, partial and sometimes biased context, and may yield inaccurate and misleading hidden representations, thus deteriorating the final retrieval result. |
| Approach: | They propose a hierarchical framework which can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic specific to each passage. |
| Outcome: | The proposed framework significantly outperforms the original dense passage retriever and helps an end-to-end QA system outperfect the strong baselines on multiple open-domain QA benchmarks. |
Visually Grounded Concept Composition (2021.findings-emnlp)
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| Challenge: | Existing approaches to visual grounding do not explicitly model compositional structures of text expressions. |
| Approach: | They propose a concept-relation Graph and a composition neural network to combine CRGs . they propose to align CRG-based concepts with images to learn visually grounded concepts . |
| Outcome: | The proposed model can model grounded concepts forming at sentence level and word level. |
Compositional Networks Enable Systematic Generalization for Grounded Language Understanding (2021.findings-emnlp)
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| Challenge: | a recent study shows that deep networks can mimic some human language abilities when presented with novel sentences . a general-purpose mechanism that enables agents to generalize their language understanding to compositional domains is critical to building safe and fair robots, says a new study. |
| Approach: | They build a general-purpose mechanism that enables agents to generalize their language understanding to compositional domains. |
| Outcome: | a new network generalizes its language understanding to compositional domains while generalizing its knowledge when prior work does not. |
An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages (2021.findings-emnlp)
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| Challenge: | Existing methods to build parallel sentence simplification corpora are limited . SS is used to rephrase sentences into simpler forms for those with cognitive disabilities . |
| Approach: | They propose to build SS corpora from large-scale bilingual translation corpors using a parallel approach. |
| Outcome: | The proposed method outperforms the existing methods on WikiLarge and achieves state-of-the-art results. |
WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach (2021.findings-emnlp)
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| Challenge: | Pre-trained language models perform well on learning sentence semantics when fine-tuned with supervised data. |
| Approach: | They conduct a thorough examination of pretrained model based unsupervised sentence embeddings. |
| Outcome: | The proposed approach improves on whitening-based vector normalization with less than 10 lines of code. |
TWEETSUMM - A Dialog Summarization Dataset for Customer Service (2021.findings-emnlp)
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Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, Ranit Aharonov
| Challenge: | a dataset focused on customer care dialog summarization is the first to focus on real-world customer care conversations . it contains extractive and abstractive summaries, and extractive summarizing methods are also introduced . |
| Approach: | They present a customer care dialog summarization dataset with 6500 human annotated summaries . they introduce an unsupervised method for extracting dialog summary data . |
| Outcome: | The proposed method is based on real-world customer support dialogs and includes extractive and abstractive summaries. |
Discourse-Based Sentence Splitting (2021.findings-emnlp)
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| Challenge: | Sentence splitting is a key component of sentence simplification and has been shown to help human comprehension. |
| Approach: | They propose to use a discourse connective to generate a sentence that is shorter than the input text. |
| Outcome: | The proposed models outperform end-to-end models in learning the various ways of expressing a discourse relation but generate text that is less grammatical. |
Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering (2021.findings-emnlp)
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| Challenge: | Existing approaches to multitask dense retrieval are not effective due to corpus inconsistency. |
| Approach: | They propose to train individual dense passage retrievers for different open-domain question-answering tasks and aggregate their predictions during test time. |
| Outcome: | The proposed method achieves state-of-the-art performance on 5 benchmark QA datasets, with up to 10% improvement in top-100 accuracy compared to a joint-training multi-task DPR on SQuAD. |
Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States (2021.findings-emnlp)
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| Challenge: | Existing studies on political responsiveness focus on long-term policies collected over decades . recent COVID-19 pandemic has given rise to a new political phenomenon, where political leaders make frequent short-term decisions on the same controlled topic. |
| Approach: | They propose to use Twitter data to classify the sentiments toward governors of each state and conduct controlled studies and comparisons. |
| Outcome: | The proposed model focuses on the COVID-19 pandemic, where political leaders make frequent short-term decisions on the same controlled topic. |
Self-Attention Graph Residual Convolutional Networks for Event Detection with dependency relations (2021.findings-emnlp)
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| Challenge: | Existing methods to classify events using syntactic dependency relations have not been developed. |
| Approach: | They propose a model which combines syntactic dependency relations with attention-based dynamic tensors to mine node-to-node latent dependency relations via self-attention mechanism. |
| Outcome: | The proposed model improves on the ACE2005 dataset and compares with baseline models. |
Mixup Decoding for Diverse Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing methods for generating multiple translations for source and target languages neglect the one-to-many mapping between the source and the target languages. |
| Approach: | They propose a method to generate different translations for the input sentence by linearly interpolating it with different sentence pairs sampled from the training corpus during decoding. |
| Outcome: | Experiments on WMT’16 en-ro, WMT'14 en de, and WMT ‘17 zh-en show that the proposed method outperforms all previous diverse machine translation methods. |
An Alignment-Agnostic Model for Chinese Text Error Correction (2021.findings-emnlp)
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| Challenge: | Existing models for Chinese text error correction can correct mistaken, missing and redundant characters, but they cannot handle missing or redundant characters. |
| Approach: | They propose an alignment-agnostic framework to correct Chinese text errors . framework detects missing and redundant characters and can be used as a cold start model . |
| Outcome: | The proposed framework can handle both text aligned and non-aligned situations and can serve as a cold start model when no annotation data are provided. |
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer (2021.findings-emnlp)
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| Challenge: | Visual dialog is a task of answering questions grounded in an image using dialog history as context. |
| Approach: | They propose a Sparse Graph Learning method to formulate visual dialog as a graph structure learning task. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the VisDial v1.0 dataset. |
Exploring Sentence Community for Document-Level Event Extraction (2021.findings-emnlp)
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| Challenge: | Existing approaches to document-level event extraction neglect the complex logic structures in long texts. |
| Approach: | They propose a framework that exploits the relationship between sentences to extract multiple events by sentence community detection using graph attention networks. |
| Outcome: | The proposed framework achieves competitive results over state-of-the-art methods on the large-scale document-level event extraction dataset. |
A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems (2021.findings-emnlp)
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| Challenge: | Existing studies on open-domain dialogue systems that allow free topics are challenging . however, non-English dialogue systems suffer from reproducing the performance of English dialogue systems . |
| Approach: | They propose to use English knowledge to improve the performance of open-domain dialogue systems . they construct a Korean-English T5 language model and develop a knowledge-grounded Korean dialogue model . |
| Outcome: | The proposed model improves even when only English knowledge is given . the model is built with a pre-trained language model and a knowledge-grounded Korean dialogue model . |
WHOSe Heritage: Classification of UNESCO World Heritage Statements of "Outstanding Universal Value” with Soft Labels (2021.findings-emnlp)
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| Challenge: | Evaluating and justifying Outstanding Universal Value (OUV) is essential for each site inscribed in the WHL . manual annotation of heritage values and attributes from multi-source textual data is knowledge-demanding and time-consuming. |
| Approach: | They propose to use NLP to build a classifier on a dataset containing Statements of OUV. |
| Outcome: | The proposed model can reach 94.3% accuracy on a dataset containing Statements of OUV . the study is promising to be further developed and applied in heritage research and practice. |
P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion (2021.findings-emnlp)
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| Challenge: | Existing methods to encode and match entity pairs have only a few observed reference entity pairs. |
| Approach: | They propose a model that infers and leverages paths that can expressively encode the relation of two entities. |
| Outcome: | The proposed model outperforms the state-of-the-art models by 11.2– 14.2% in terms of Hits@1. |
Cartography Active Learning (2021.findings-emnlp)
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| Challenge: | Existing methods to label data are limited in their notion of informativeness, due to post-training model uncertainty and batch diversity. |
| Approach: | They propose a new Active Learning algorithm that exploits the behavior of the model on individual instances during training as a proxy to find the most informative instances for labeling. |
| Outcome: | The proposed method is competitive to other common AL methods, showing that training dynamics derived from small seed data can be successfully used for AL. |
Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization (2021.findings-emnlp)
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| Challenge: | Existing approaches to improve generalization of neural models use a small component of the gradient for maximizing dot-product between batches. |
| Approach: | They propose to use a finite differences first-order algorithm to calculate a gradient from dot-product of gradients and regularize it. |
| Outcome: | The proposed method outperforms previous approaches of Reptile and MAML when used as a regularization technique. |
GooAQ: Open Question Answering with Diverse Answer Types (2021.findings-emnlp)
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| Challenge: | Existing research on niche answer types, mainly short responses and, in a few cases, long responses, has failed to adequately address the answer diversity of questions. |
| Approach: | They propose to use Google's autocomplete feature to collect questions from a large-scale dataset with a variety of answer types to facilitate further research on improving QA with diverse response types. |
| Outcome: | The proposed model produces naturalistic questions that are short and expressed using simple language. |
Attention Weights in Transformer NMT Fail Aligning Words Between Sequences but Largely Explain Model Predictions (2021.findings-emnlp)
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| Challenge: | Using attention weights, we show that NMT models make alignment errors by relying on uninformative tokens from the source sequence. |
| Approach: | They propose to use attention weights to regulate alignment errors in NMT models . they propose methods that largely reduce the word alignment error rate compared to standard induced alignments from attention weighted tokens. |
| Outcome: | The proposed methods reduce the word alignment error rate compared to standard induced alignments from attention weights. |
BFClass: A Backdoor-free Text Classification Framework (2021.findings-emnlp)
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| Challenge: | Various trigger design strategies have been explored to attack text classifiers, however, defending such attacks remains an open problem. |
| Approach: | They propose a backdoor-free training framework that poisons a subset of training data by injecting trigger patterns and setting their labels as the target labels. |
| Outcome: | The proposed framework can detect all the triggers, remove 95% of poisoned training samples with very limited false alarms, and achieve almost the same performance as the models trained on benign training data. |
Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing methods for extracting complete (binary) parses from pre-trained language models are expensive and time-consuming. |
| Approach: | They propose a chart-based method and an effective top-K ensemble technique to extractbinary parses from PLMs. |
| Outcome: | The proposed method can induce non-trivial parses for sentences from nine languages in an integrated and language-agnostic manner, and is robust to cross-lingual transfer. |
Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph Embeddings (2021.findings-emnlp)
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| Challenge: | Recent knowledge graph embedding models based on hyperbolic geometry are complicated than Euclidean operations. |
| Approach: | They propose to use hyperbolic geometry to generate high-fidelity and parsimonious representations of hierarchical patterns in knowledge graphs. |
| Outcome: | The proposed models achieve state-of-the-art performance on two widely-used datasets and cost less than RotH. |
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade (2021.findings-emnlp)
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| Challenge: | Experimental results show that CascadeBERT can achieve an overall 15% improvement under 4x speed-up compared with existing dynamic early exiting methods on six classification tasks. |
| Approach: | They propose a framework which emits predictions in internal layers without passing through the entire model. |
| Outcome: | The proposed framework can achieve 15% improvement under 4x speed-up compared with existing methods on six classification tasks yielding more calibrated and accurate predictions. |
Semi-supervised Relation Extraction via Incremental Meta Self-Training (2021.findings-emnlp)
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| Challenge: | Existing methods suffer from the gradual drift problem, where noisy pseudo labels are incorporated during training. |
| Approach: | They propose a method that uses pseudo labels to assess quality on unlabeled samples . they use a relation label generation network to learn from successful and failed attempts . |
| Outcome: | Experimental results show the proposed method can improve on two public datasets. |
Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning (2021.findings-emnlp)
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| Challenge: | Existing KG evaluation metrics are only aware of the exact correctness of predictions on phrase-level and ignore semantic similarities between similar predictions and targets, which inhibits the model from learning deep linguistic patterns. |
| Approach: | They propose a fine-grained evaluation metric to improve the previous KG framework . the evaluation metrics are only aware of the exact correctness of predictions on phrase-level . |
| Outcome: | The proposed method outperforms the existing frameworks among all evaluation scores. |
Improving Knowledge Graph Embedding Using Affine Transformations of Entities Corresponding to Each Relation (2021.findings-emnlp)
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| Challenge: | Existing knowledge graph embedding methods use k-dimensional vectors to represent each entity in a knowledge graph. |
| Approach: | They propose to use affine transformations to embed knowledge graphs using previous methods . they propose to add k additional variables to the existing methods to perform embedding . |
| Outcome: | The proposed method outperforms RotatE, Distmult and ComplEx on various data sets. |
Using Question Answering Rewards to Improve Abstractive Summarization (2021.findings-emnlp)
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| Challenge: | Neural abstractive summarization models have seen improvements in recent years, but they still suffer from multiple drawbacks. |
| Approach: | They propose a general framework to train abstractive summarization models to alleviate these issues by question-answering based rewards. |
| Outcome: | The proposed framework is preferred over general abstractive summarization models. |
Effect Generation Based on Causal Reasoning (2021.findings-emnlp)
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| Challenge: | Existing methods for reasoning causalities on word level are limited . a word-level causal reasoning method may only predict the unintelligible effect of "quarrel" |
| Approach: | They propose a novel event-level causal reasoning method that structuralizes event-effect event pairs into an event causality network and shows its use in the task of effect generation. |
| Outcome: | The proposed method generates more reasonable effect sentences than well-designed competitors. |
Distilling Word Meaning in Context from Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing methods to transform contextualised representations weaken excessive effects of contextual information. |
| Approach: | They propose a self-supervised learning method that distils word meaning in context from a pre-trained masked language model. |
| Outcome: | The proposed method outperforms the state-of-the-art method for lexical semantics and STS estimation. |
Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation (2021.findings-emnlp)
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| Challenge: | Existing methods for complex question answering are limited in the search space of all possible relation paths. |
| Approach: | They propose a method that directly generates an executable SPARQL query without simplification. |
| Outcome: | The proposed method significantly outperforms the previous methods and has higher interpretability and computational efficiency than the previous ones. |
Bidirectional Hierarchical Attention Networks based on Document-level Context for Emotion Cause Extraction (2021.findings-emnlp)
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| Challenge: | Emotion cause extraction (ECE) aims to extract the causes behind certain emotion in text. |
| Approach: | They propose a bidirectional hierarchical attention network corresponding to the specified candidate cause clause to capture document-level context in a structured and dynamic manner. |
| Outcome: | The proposed method achieves competitive performances on two public datasets in Chinese and English. |
Distantly Supervised Relation Extraction in Federated Settings (2021.findings-emnlp)
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| Challenge: | Existing methods to label training datasets using distant supervision are expensive and cannot cover all walks of life. |
| Approach: | They propose a federated denoising framework to suppress label noise in federation . they propose to use a multiple instance learning based denoisation method to select reliable sentences . |
| Outcome: | The proposed method can select reliable sentences via cross-platform collaboration. |
Casting the Same Sentiment Classification Problem (2021.findings-emnlp)
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| Challenge: | Identifying the stance of an argument towards a topic is a fundamental problem in computational argumentation. |
| Approach: | They propose a task where text users are asked to determine if they have the same sentiment . they aim to enable a more topic-agnostic sentiment classification by using Yelp data . |
| Outcome: | The proposed task achieves an accuracy above 83% for category subsets across topics and 89% on average. |
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing (2021.findings-emnlp)
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| Challenge: | Neural network models suffer from performance losses when faced with compositionally out-of-distribution data. |
| Approach: | They propose to use neural semantic parsers to detect compositionally out-of-distribution (OOD) data. |
| Outcome: | The proposed methods perform well on the standard SCAN and CFQ datasets. |
Saliency-based Multi-View Mixed Language Training for Zero-shot Cross-lingual Classification (2021.findings-emnlp)
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| Challenge: | Recent multilingual pre-trained models have been demonstrated effective in many cross-lingual tasks. |
| Approach: | They propose a framework that leverages code-switched data with multi-view learning to fine-tune XLM-R. |
| Outcome: | The proposed model achieves state-of-the-art on zero-shot cross-lingual sentiment classification and dialogue state tracking tasks. |
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society (2021.findings-emnlp)
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Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, Preslav Nakov
| Challenge: | a dataset of 16K manually annotated tweets is used to analyze disinformation . the democratic nature of social media has raised questions about the quality and the factuality of the information that is shared on these platforms. |
| Approach: | They use a dataset of manually annotated tweets to analyze COVID-19 disinformation . they show that tweets contain fake cures, rumors, conspiracy theories and xenophobia . |
| Outcome: | The proposed dataset shows that it is useful in monolingual vs. multilingual settings. |
FANATIC: FAst Noise-Aware TopIc Clustering (2021.findings-emnlp)
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| Challenge: | a large amount of data can be computationally prohibitive for extracting topic noise . many clustering algorithms assign documents to one of the available clusters . a novel algorithm that efficiently distinguishes documents from genuine topics is developed . |
| Approach: | They propose an algorithm that efficiently distinguishes documents from genuine topics . they use a reddit dataset to showcase the algorithm as it contains short, noisy data . |
| Outcome: | The proposed algorithm outperforms hdbscan and hANATIC on a Twitter dataset. |
Stream-level Latency Evaluation for Simultaneous Machine Translation (2021.findings-emnlp)
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| Challenge: | Simultaneous machine translation systems need to find a trade-off between translation quality and response time. |
| Approach: | They propose to adapt existing translation latency measures to streaming scenarios by re-segmenting the output translation to take into account sequential nature of streaming scenarios. |
| Outcome: | The proposed measures are evaluated on a streaming task on simulated speech translation systems. |
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning (2021.findings-emnlp)
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| Challenge: | Existing methods to learn sentence embeddings require labeled data, but it is expensive. |
| Approach: | They propose an unsupervised method which learns sentence embeddings using unlabeled data . they propose a transformer-based sequence denoising auto-encoder which can be used for training . |
| Outcome: | The proposed method outperforms existing methods on four datasets from heterogeneous domains. |
How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology? (2021.findings-emnlp)
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| Challenge: | Data-driven subword segmentation is the default strategy for open-vocabulary machine translation but may not be sufficiently generic for learning non-concatenative morphology. |
| Approach: | They propose to test data-driven subword segmentation on non-concatenative morphological phenomena in a controlled, semi-synthetic setting. |
| Outcome: | The proposed model can translate non-concatenative morphological phenomena in a controlled, semi-synthetic setting. |
Rethinking Why Intermediate-Task Fine-Tuning Works (2021.findings-emnlp)
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| Challenge: | Pretrained language models can be fine-tuned on intermediate labeled-data tasks before fine- tuning the models on the target task of interest. |
| Approach: | They conduct extensive experiments to study the impact of different factors on STILT . they find that the improvement from an intermediate task could be orthogonal to it containing reasoning or other complex skills. |
| Outcome: | The proposed method improves the performance of pretrained language models on various target tasks. |
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning (2021.findings-emnlp)
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| Challenge: | Existing models that pursue rapid generalization to new tasks are mostly trained in a single shot on fixed datasets, unable to dynamically expand their knowledge. |
| Approach: | They propose a new learning setup that assumes a model learns from a sequence of diverse NLP tasks arriving sequentially, accumulating knowledge for improved generalization to new tasks. |
| Outcome: | The proposed learning setup improves generalization ability while retaining performance on the tasks learned earlier. |
Efficient Test Time Adapter Ensembling for Low-resource Language Varieties (2021.findings-emnlp)
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| Challenge: | Specialized language and task adapters have been proposed to facilitate cross-lingual transfer of multilingual pretrained models. |
| Approach: | They propose a method that optimizes the ensemble weights of pretrained adapters for each test sentence by minimizing the entropy of its predictions. |
| Outcome: | The proposed method improves robustness to uncovered languages without training new adapters. |
An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces (2021.findings-emnlp)
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Kelly Marchisio, Youngser Park, Ali Saad-Eldin, Anton Alyakin, Kevin Duh, Carey Priebe, Philipp Koehn
| Challenge: | Existing work in bilingual lexicon induction views word embeddings as vectors in Euclidean space. |
| Approach: | They propose to use word embeddings as nodes in a weighted graph to examine a node’s graph neighborhood without assuming a linear transform. |
| Outcome: | The proposed approaches are compared under different data conditions and show that they complement each other when combined. |
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding (2021.findings-emnlp)
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| Challenge: | Knowledge Distillation (KD) is a model compression algorithm that helps transfer knowledge in a large neural network into a smaller one. |
| Approach: | They propose a framework to assess adversarial robustness of multiple KD algorithms. |
| Outcome: | The proposed algorithm achieves state-of-the-art on the GLUE benchmark and out-of domain generalization and adversarial robustness compared to competitive methods. |
Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction (2021.findings-emnlp)
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| Challenge: | Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. |
| Approach: | They propose a tightly coupled two-stage approach to extract latent user sentiments and item properties from reviews and an Attention-Property-aware Rating Estimator (APRE). |
| Outcome: | Extensive experiments on seven real-world Amazon review datasets show that the proposed approach extracts the latent user sentiments, item properties, and the complicated interactions between the two components. |
Learning Hard Retrieval Decoder Attention for Transformers (2021.findings-emnlp)
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| Challenge: | In this paper, we show that learning a hard retrieval attention that attends to a single token in a sentence is 1.43 times faster than the standard scaled dot-product attention. |
| Approach: | They propose a method to learn hard retrieval attention where an attention head attends to a single token in a sentence rather than all tokens. |
| Outcome: | The proposed method is 1.43 times faster in decoding while preserving translation quality on a wide range of MT tasks. |
Recall and Learn: A Memory-augmented Solver for Math Word Problems (2021.findings-emnlp)
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| Challenge: | Existing methods for solving math word problems are based on template-based generation which results in limited generalization capability. |
| Approach: | They propose a human-like analogical learning method for the math word problem . it uses modules of memory, representation, analogy, and reasoning to make a new exercise . |
| Outcome: | The proposed method outperforms state-of-the-art models on two well-known datasets. |
An Uncertainty-Aware Encoder for Aspect Detection (2021.findings-emnlp)
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| Challenge: | Existing methods for aspect detection use seed words as priors or features of topic models. |
| Approach: | They propose a weakly-supervised method to exploit seed words for aspect detection . goal is approximating similarity between segments and aspects and ground-truth similarity generated from seed words. |
| Outcome: | The proposed method outperforms previous work on several benchmarks in various domains. |
Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations (2021.findings-emnlp)
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| Challenge: | Existing approaches to empathetic response generation ignore the emotion cause . existing dialogue systems lack emotion understanding and empathy . |
| Approach: | They propose a framework that integrates emotion cause information into empathetic response generation by predicting context emotion labels and sequence of emotion cause-oriented labels. |
| Outcome: | The proposed framework improves empathetic response generation by incorporating emotion cause information into the model. |
Probing Across Time: What Does RoBERTa Know and When? (2021.findings-emnlp)
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| Challenge: | Current approaches to natural language processing rely on fixed artifacts such as language models . current studies have focused on how these models acquire and demonstrate knowledge . |
| Approach: | They apply probing techniques to examine how language models acquire knowledge . they aim to inform future work on more efficient pretraining and understanding dependencies . |
| Outcome: | The proposed model learns linguistic abstractions, factual and commonsense knowledge, and reasoning abilities fast, stably, and robustly across domains. |
Knowledge-Guided Paraphrase Identification (2021.findings-emnlp)
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| Challenge: | Existing methods for paraphrase identification (PI) are limited due to lack of professional knowledge. |
| Approach: | They propose to leverage Wikipedia knowledge to accurately identify paraphrases by mining outline knowledge of given sentences from Wikipedia. |
| Outcome: | The proposed framework outperforms state-of-the-art models on two public datasets: PARADE and clinicalSTS2019. |
R2-D2: A Modular Baseline for Open-Domain Question Answering (2021.findings-emnlp)
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| Challenge: | Using extractive and generative reader, we demonstrate its strength across three open-domain QA datasets: NaturalQuestions, TriviaQA and EfficientQA. |
| Approach: | They propose a four-stage open-domain QA pipeline with a retriever, passage reranker, extractive reader, generative reader and a mechanism that aggregates the final prediction from all system’s components. |
| Outcome: | The proposed pipeline outperforms state-of-the-art on three open-domain QA datasets and is twice as effective as the posterior averaging ensemble of the same models with different parameters. |
What Does Your Smile Mean? Jointly Detecting Multi-Modal Sarcasm and Sentiment Using Quantum Probability (2021.findings-emnlp)
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| Challenge: | Existing methods to model multi-modal sarcasm and sentiment are based on quantum probability . sarcasm and feelings embody intrinsic uncertainty of human cognition . |
| Approach: | They propose a quantum probability-driven multi-task learning framework for sarcasm and sentiment recognition using quantum superpositions and quantum interference. |
| Outcome: | The proposed model achieves state-of-the-art in multi-modal sarcasm and sentiment recognition. |
Discovering Representation Sprachbund For Multilingual Pre-Training (2021.findings-emnlp)
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| Challenge: | Existing models perform poorly on many languages and cross-lingual tasks due to typological differences and contradictions between some languages. |
| Approach: | They propose to pre-train multilingual pre-trained models to handle cross-lingual tasks in one model. |
| Outcome: | The proposed model improves performance on cross-lingual tasks compared to baselines on multiple languages . |
Plan-then-Generate: Controlled Data-to-Text Generation via Planning (2021.findings-emnlp)
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| Challenge: | Existing studies focus on producing results that are close to the references, i.e. what to generate and in what order (the output structure) cannot be explicitly controlled by the users. |
| Approach: | They propose a Plan-then-Generate framework to improve the controllability of neural data-to-text models. |
| Outcome: | The proposed model can control both the intra-sentence and inter-sentent structure of the generated output. |
Few-Shot Table-to-Text Generation with Prototype Memory (2021.findings-emnlp)
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| Challenge: | Neural table-to-text generation models are data-hungry and require large amounts of training data to learn the mapping between tables and texts. |
| Approach: | They propose a framework for table-to-text generation under the few-shot scenario that uses retrieved prototypes and a prototype selector to bridge the structural gap between tables and texts. |
| Outcome: | The proposed framework significantly improves the model performance on three benchmark datasets with state-of-the-art models. |
Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation (2021.findings-emnlp)
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| Challenge: | Word sense disambiguation (WSD) methods have not explored word-formations in parataxis languages like Chinese. |
| Approach: | They propose to leverage word-formation knowledge to enhance Chinese WSD by incorporating word-forms into sense disambiguation models. |
| Outcome: | The proposed model improves on baselines in Chinese word sense disambiguation (WSD) with word-formation knowledge, the results show. |
Exploiting Curriculum Learning in Unsupervised Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Experimental results show that the proposed method achieves consistent improvements with faster convergence speed. |
| Approach: | They propose a curriculum learning method to gradually utilize pseudo bi-texts based on their quality from multiple granularities. |
| Outcome: | The proposed method achieves consistent improvements with faster convergence speed on WMT 14 En-Fr, WMT14 En-De, and LDC En-Zh translation tasks. |
Robust Fragment-Based Framework for Cross-lingual Sentence Retrieval (2021.findings-emnlp)
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Nattapol Trijakwanich, Peerat Limkonchotiwat, Raheem Sarwar, Wannaphong Phatthiyaphaibun, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Cross-lingual Sentence Retrieval (CLSR) aims at retrieving parallel sentence pairs that are translations of each other from a multilingual set of comparable documents. |
| Approach: | They propose a framework for cross-lingual sentence retrieval that uses a collection of fragments to improve sentence retrievals. |
| Outcome: | The proposed framework improves the retrieval robustness of the base sentences encoded by m-USE, LASER, and LaBSE. |
Towards Improving Adversarial Training of NLP Models (2021.findings-emnlp)
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| Challenge: | Recent methods for generating NLP adversarial examples involve combinatorial search and expensive sentence encoders for constraining the generated instances. |
| Approach: | They propose to use vanilla adversarial training to train NLP models using a word substitution attack optimized for vanilla adversary training. |
| Outcome: | The proposed approach improves model performance and standard accuracy and can defend against other types of word substitution attacks. |
To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence (2021.findings-emnlp)
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| Challenge: | a new study examines the media coverage of police violence in the United States by examining the framing of 82k news articles spanning 7k police killings. |
| Approach: | They propose an NLP framework to measure entity-centric framing to understand media coverage on police violence in the United States in a new police violence frames corpus of 82k news articles spanning 7k police killings. |
| Outcome: | The proposed framework reveals significant differences in the way liberal and conservative news sources frame both the issue of police violence and the entities involved. |
Calibrate your listeners! Robust communication-based training for pragmatic speakers (2021.findings-emnlp)
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| Challenge: | Prior work has investigated training NLP systems with communication-based objectives . prior work has focused on supervised learning, but is expensive to collect . |
| Approach: | They propose a method that uses a population of neural listeners to regularize speaker training. |
| Outcome: | The proposed method improves on ensemble- and dropout-based listening populations on reference games and generalizes to new games and listeners. |
When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions (2021.findings-emnlp)
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| Challenge: | Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question contextualized by a long scenario description. |
| Approach: | They propose a model where the retriever is implicitly supervised only using QA labels via a novel word weighting mechanism. |
| Outcome: | The proposed model outperforms strong baselines on multiple-choice questions in three datasets. |
Structured abbreviation expansion in context (2021.findings-emnlp)
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| Challenge: | Ad hoc abbreviations are commonly found in informal communication channels that favor shorter messages. |
| Approach: | They propose to reverse ad hoc abbreviations in context to recover normalized, expanded versions of abbrevated messages. |
| Outcome: | The proposed method can recover normalized, expanded abbreviations from text . it is similar to spelling correction, but requires more extensive work . |
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding (2021.findings-emnlp)
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| Challenge: | Task-adaptive pre-training (TAPT) and Self-training can be complementary with simple TFS protocol. |
| Approach: | They propose to use task-adaptive pre-training and self-training to combine TAPT and ST with a simple TFS protocol to achieve strong combined gains across six datasets. |
| Outcome: | The proposed method can achieve strong combined gains across six datasets covering sentiment classification, paraphrase identification, natural language inference, named entity recognition and dialogue slot classification. |
CNNBiF: CNN-based Bigram Features for Named Entity Recognition (2021.findings-emnlp)
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| Challenge: | Named entity recognition tasks require a self-attention mechanism with unconstrained length that fails to capture local dependencies. |
| Approach: | They propose a joint training objective which better captures the semantics of words corresponding to the same entity by augmenting the objective with a group-consistency loss component. |
| Outcome: | The proposed model achieves a test F1 of 93.98 with a single transformer model. |
Compositional Generalization via Semantic Tagging (2021.findings-emnlp)
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| Challenge: | Existing neural sequence-to-sequence models fail at compositional generalization, i.e., they cannot generalize to unseen compositions of seen components. |
| Approach: | They propose a decoding framework that preserves expressivity and generality of sequence-to-sequence models while featuring lexicon-style alignments and disentangled information processing. |
| Outcome: | The proposed framework improves compositional generalization across model architectures, domains, and semantic formalisms on three semantic parsing datasets. |
Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering (2021.findings-emnlp)
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| Challenge: | Document-level paraphrase generation is an important task in natural language processing. |
| Approach: | They propose a coherence relationship-guided paraphrase generation model that leverages graph GRU to encode the coherency relationship graph and get the cohesion-aware representation for each sentence. |
| Outcome: | The proposed model outperforms baseline models on BERTScore and diversity scores. |
Exploring Decomposition for Table-based Fact Verification (2021.findings-emnlp)
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| Challenge: | Existing research focuses on fact verification based on unstructured text, but structured data is becoming more prevalent. |
| Approach: | They propose to decompose complex statements into simpler subproblems to improve table-based verification by a weakly supervised parser. |
| Outcome: | The proposed method achieves state-of-the-art accuracy on the TabFact benchmark. |
Diversity and Consistency: Exploring Visual Question-Answer Pair Generation (2021.findings-emnlp)
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| Challenge: | Existing tasks to generate question-answer pairs from visual images are under-explored. |
| Approach: | They propose a task that targets question-answer pair generation from visual images. |
| Outcome: | The proposed model can generate diverse or consistent QAPs on two benchmarks. |
Entity-level Cross-modal Learning Improves Multi-modal Machine Translation (2021.findings-emnlp)
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| Challenge: | Multi-modal machine translation aims at improving translation performance by incorporating visual information. |
| Approach: | They propose an explicit entity-level cross-modal learning approach that aims to augment the entity representation by combining a translation task and a reconstruction task. |
| Outcome: | The proposed approach achieves comparable or even better performance than state-of-the-art models. |
Learning to Ground Visual Objects for Visual Dialog (2021.findings-emnlp)
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| Challenge: | Existing methods to ground visual objects are inadequate for visual dialog . a posterior distribution is inferred from context and questions, while posterior distributions are used to facilitate visual objects grounding. |
| Approach: | They propose a method to learn to ground visual objects for visual dialog using prior and posterior distributions over visual objects to facilitate visual objects grounding. |
| Outcome: | The proposed approach improves the existing models in generative and discriminative settings by a significant margin. |
KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals (2021.findings-emnlp)
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| Challenge: | Existing frameworks for multi-subgoal dialogs require a system to build a social bond with users to gain trust and develop affinity. |
| Approach: | They propose a framework for common knowledge-based multi-subgoal dialogs that divides up conversations with multiple subgoals and propose mechanisms to filter noisy knowledge and to include cleaned knowledge in the dialog response generation process. |
| Outcome: | The proposed framework obtains state-of-the-art results on a DuRecDial dataset in both automatic and human evaluation. |
Less Is More: Domain Adaptation with Lottery Ticket for Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Existing domain adaptation paradigms for reading comprehension require large amounts of annotation data to achieve the desired task performance. |
| Approach: | They propose a few-shot domain adaptation paradigm for reading comprehension . they introduce self-attention attribution to weigh parameters and refine the lottery subnetwork . |
| Outcome: | The proposed model outperforms the full model fine-tuning adaptation on four out of five domains with a small amount of data available for adaptation. |
Effectiveness of Pre-training for Few-shot Intent Classification (2021.findings-emnlp)
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| Challenge: | Existing paradigms further pre-train language models such as BERT on vast amount of unlabeled corpus, but we find it highly effective and efficient to simply fine-tune BERT with roughly 1,000 labeled utterances from public datasets. |
| Approach: | They propose to fine-tune BERT with a small set of labeled utterances from public datasets to achieve a pre-trained model based on a set of 1,000 labeles. |
| Outcome: | The proposed model can outperform existing models on domains with very different semantics on novel domains. |
Improving Abstractive Dialogue Summarization with Hierarchical Pretraining and Topic Segment (2021.findings-emnlp)
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| Challenge: | Existing methods for meeting summary have limited the ability to deal with long-term dependency. |
| Approach: | They propose a hierarchical transformer encoder-decoder network with multi-task pre-training to capture key sentences at word level and generate them at word-level. |
| Outcome: | The proposed model is superior to the previous methods in meeting summary datasets AMI and ICSI. |
Learning to Answer Psychological Questionnaire for Personality Detection (2021.findings-emnlp)
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| Challenge: | Existing text-based personality detection research relies on data-driven approaches to implicitly capture personality cues in online posts lacking the guidance of psychological knowledge. |
| Approach: | They propose a model to capture key information in texts and a questionnaire to help the user to make a personality assessment. |
| Outcome: | The proposed model captures key information in texts and a questionnaire and can be used to improve personality prediction. |
Exploiting Reasoning Chains for Multi-hop Science Question Answering (2021.findings-emnlp)
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| Challenge: | Existing frameworks for multi-hop Science question answering do not require corpus-specific annotations. |
| Approach: | They propose a chain-guided retriever-reader framework that performs explainable reasoning without corpus annotations. |
| Outcome: | The proposed framework performs explainable reasoning without corpus-specific annotations . it is shown to be effective on OpenBookQA and ARC-Challenge . |
Winnowing Knowledge for Multi-choice Question Answering (2021.findings-emnlp)
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| Challenge: | Existing reasoning models suffer from noises in retrieved knowledge . encoding methods that use commonsense knowledge are less effective . |
| Approach: | They propose a method which conducts interception and soft filtering to reduce noise . they use commonsense knowledge from Wikipedia and ConceptNet to encode questions and options . |
| Outcome: | The proposed method improves on commonsense question answering tasks compared to baselines . it is able to conduct interception and soft filtering to shield the encoder from noise . |
Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts (2021.findings-emnlp)
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| Challenge: | Existing studies on the detection and aggregation of media bias lack a gold standard data set and high context dependencies. |
| Approach: | They propose to use a data set to identify media bias by word and sentence level . they propose to train a model to detect bias-inducing sentences in news articles automatically . |
| Outcome: | The proposed model outperforms existing methods on a large corpus of labels on the word and sentence level. |
Learning and Evaluating a Differentially Private Pre-trained Language Model (2021.findings-emnlp)
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Shlomo Hoory, Amir Feder, Avichai Tendler, Sofia Erell, Alon Peled-Cohen, Itay Laish, Hootan Nakhost, Uri Stemmer, Ayelet Benjamini, Avinatan Hassidim, Yossi Matias
| Challenge: | Contextual language models have improved performance but can lead to information leakage . |
| Approach: | They propose a differentially-private word-piece algorithm that allows training a tailored domain-specific vocabulary while maintaining privacy. |
| Outcome: | The proposed model can guarantee privacy while maintaining good model performance. |
Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions (2021.findings-emnlp)
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| Challenge: | Popular dialog datasets such as MultiWOZ are created by providing crowd workers with instructions that describe the task to be accomplished. |
| Approach: | They propose a data creation strategy that uses a pre-trained language model to simulate the interaction between crowd workers by creating a user bot and an agent bot. |
| Outcome: | The proposed data creation strategy improves on two publicly available datasets using a pre-trained language model and a smaller percentage of actual crowd-generated conversations and their corresponding instructions. |
Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge (2021.findings-emnlp)
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| Challenge: | Conversational Emotion Recognition (CER) is a task to predict the emotion of an utterance in the context of a conversation. |
| Approach: | They propose a pSychological-Knowledge-Aware Interaction Graph to model the emotional state of an utterance in the context of a conversation. |
| Outcome: | The proposed method achieves state-of-the-art and competitive performance on four popular CER datasets. |
An unsupervised framework for tracing textual sources of moral change (2021.findings-emnlp)
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| Challenge: | Existing studies on moral sentiment classification and temporal inference of moral sentiment have not quantified the origins of these changes. |
| Approach: | They propose an unsupervised framework for tracing textual sources of moral change toward entities through time. |
| Outcome: | The proposed framework captures fine-grained human moral judgments and identifies coherent source topics of moral change triggered by historical events. |
Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization (2021.findings-emnlp)
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| Challenge: | Existing methods to abstractly summarize dialogues are limited to two or more interlocutors. |
| Approach: | They propose to use existing document summarization models to capture the various topic information of a conversation and outline salient facts for the captured topics. |
| Outcome: | The proposed method significantly outperforms baselines and achieves new state-of-the-art performance on benchmark datasets. |
TWT: Table with Written Text for Controlled Data-to-Text Generation (2021.findings-emnlp)
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| Challenge: | Existing methods output hallucinated text that is not faithful on TWT. |
| Approach: | They propose to generate text conditioned on the structured data and a prefix by leveraging pre-trained neural models. |
| Outcome: | The proposed approach outperforms state-of-the-art methods under automatic and human evaluation metrics. |
ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective (2021.findings-emnlp)
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| Challenge: | Existing solutions to reduce the cost of pretraining Transformer-based models are expensive especially for large-scale models. |
| Approach: | They propose to reduce the cost of pre-training Transformer-based models by compressing the sequence of hidden states inside Transformer architecture. |
| Outcome: | The proposed model achieves state-of-the-art on several Arabic downstream tasks despite using less computational resources compared to other BERT-based models. |
Which is Making the Contribution: Modulating Unimodal and Cross-modal Dynamics for Multimodal Sentiment Analysis (2021.findings-emnlp)
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| Challenge: | Recent studies focus on learning cross-modal dynamics, but neglect to explore optimal solution for unimodal networks. |
| Approach: | They propose a new MSA framework to identify contribution of modalities and reduce impact of noisy information. |
| Outcome: | The proposed model outperforms state-of-the-art methods on publicly available datasets. |
CVAE-based Re-anchoring for Implicit Discourse Relation Classification (2021.findings-emnlp)
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| Challenge: | Existing studies show that training implicit discourse relation classifiers suffers from data sparsity. |
| Approach: | They propose a re-anchoring strategy to reduce the risk of erroneous sampling . they use Conditional VAE to estimate the risk and migrate the anchor to reduce it . |
| Outcome: | The proposed method improves the baseline classifier performance on PDTB v2.0 . |
Combining Curriculum Learning and Knowledge Distillation for Dialogue Generation (2021.findings-emnlp)
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| Challenge: | Existing studies have shown that curriculum learning facilitates dialogue generation tasks while knowledge distillation can yield significant performance boosts for student models. |
| Approach: | They propose a combination of curriculum learning and knowledge distillation for dialogue generation models . they cluster training cases according to their complexity and employ an adversarial training strategy . |
| Outcome: | The proposed model improves compared with baselines. |
Improving End-to-End Task-Oriented Dialog System with A Simple Auxiliary Task (2021.findings-emnlp)
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| Challenge: | Using large pre-trained language models for end-to-end TOD modeling has made significant progress on benchmarks . a paradigm of leveraging large pretrained models has shown promising results . |
| Approach: | They combine paradigm of leveraging large pre-trained language models with multi-task learning framework . their model achieves new state-of-the-art results with combined scores of 108.3 and 107.5 . |
| Outcome: | The proposed model achieves state-of-the-art results on multiWOZ 2.0 and MultiWOZ 2.1 . it also improves generalization capability through domain adaptation experiments in the few-shot setting. |
EDTC: A Corpus for Discourse-Level Topic Chain Parsing (2021.findings-emnlp)
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| Challenge: | Discourse analysis is a fundamental part of natural language processing. |
| Approach: | They propose a discourse-level topic chain parsing system which can be automated . they propose lexical cohesion modeling instead of lexically measuring topic structure . |
| Outcome: | The proposed system is robust and reliable, and can provide high reliability and low confidence scores. |
Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help? (2021.findings-emnlp)
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| Challenge: | Multilingual Neural Machine Translation (MNMT) trains a single model that supports translation between multiple languages . transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer. |
| Approach: | They propose a hierarchical knowledge distillation approach to train multilingual models . they use typological features and phylogeny to overcome negative transfer issue . |
| Outcome: | The proposed approach avoids negative transfer effect by capitalising on language groups generated according to typological features and phylogeny of languages. |
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading Comprehension (2021.findings-emnlp)
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| Challenge: | unified Aspect-based Sentiment Analysis (ABSA) aims to couple aspect terms with their corresponding opinion terms, which might make it easier to predict sentiment polarities. |
| Approach: | They propose a new paradigm to pair aspect terms with their corresponding opinion terms . they propose to use a machine learning paradigm to solve the unified ABSA task . |
| Outcome: | The proposed framework can solve the ABSA task without any additional data annotation or transformation. |
Generalization in Text-based Games via Hierarchical Reinforcement Learning (2021.findings-emnlp)
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| Challenge: | Reinforcement Learning (RL) based agents are promising for text-based games, but their generalization remains a challenge. |
| Approach: | They propose a hierarchical framework for reinforcement learning based on knowledge graphs . they propose to decompose the game into subtasks and execute a sub-policy in the low level to conduct goal-conditioned reinforcement learning. |
| Outcome: | The proposed framework enjoys favorable generalizability on a set of difficulty levels and is able to handle complex training tasks. |
A Finer-grain Universal Dialogue Semantic Structures based Model For Abstractive Dialogue Summarization (2021.findings-emnlp)
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| Challenge: | Abstractive summarization models have achieved impressive results on document summarizing tasks, but their performance on dialogue modeling is poor due to the crude and straight methods for dialogue encoding. |
| Approach: | They propose a model that leverages Finer-grain universal Dialogue semantic Structures to model dialogue and generate better summaries. |
| Outcome: | The proposed model outperforms various dialogue summarization approaches and achieves state-of-the-art (SOTA) ROUGE results on a SAMsum dataset. |
Constructing contrastive samples via summarization for text classification with limited annotations (2021.findings-emnlp)
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| Challenge: | Various contrastive learning methods have been developed and lead to state-of-the-art performance in many computer vision tasks. |
| Approach: | They propose a method to construct efficient contrastive samples using text summarization to gain better representations of text classification tasks with limited annotations. |
| Outcome: | The proposed framework gains better representations on text classification tasks with limited annotations and is compared with existing methods on real-world text classification datasets. |
End-to-end Neural Information Status Classification (2021.findings-emnlp)
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| Challenge: | Existing studies on information status classification and bridging anaphora recognition assume that gold mention or syntactic tree information is given. |
| Approach: | They propose an end-to-end neural approach for information status classification using a mention extraction component and an information status assignment component. |
| Outcome: | The proposed system achieves state-of-the-art on fine-grained IS classification based on gold mentions and better than baselines on ISNotes and SciCorp. |
EventKE: Event-Enhanced Knowledge Graph Embedding (2021.findings-emnlp)
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| Challenge: | Experimental results show that events can greatly improve the quality of KG embeddings on multiple downstream tasks. |
| Approach: | They propose an event-enhanced KG embedding model that incorporates events into KGs . they first incorporate event nodes by building a heterogeneous network with event argument links . |
| Outcome: | The proposed model incorporates event nodes into the original knowledge graphs . it can be used to fuse event information into the KG embeddings on multiple tasks . |
Modeling Concentrated Cross-Attention for Neural Machine Translation with Gaussian Mixture Model (2021.findings-emnlp)
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| Challenge: | Dot-product attention only considers the pair-wise correlation between words, resulting in dispersion when dealing with long sentences and neglecting source neighboring relationships. |
| Approach: | They propose to model concentrated attention in cross-attention using a Gaussian Mixture Model to model cross- attention in a language model. |
| Outcome: | Experiments on three datasets show that the proposed method outperforms the baseline and has significant improvement on alignment quality, N-gram accuracy, and long sentence translation. |
Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection (2021.findings-emnlp)
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| Challenge: | Existing rumor detection models focus on textual data to extract distinctive features, but they fail to capture the inconsistency information among the content and background knowledge. |
| Approach: | They propose to capture inconsistency semantics and content-knowledge level in a unified framework to detect rumors with multimedia content. |
| Outcome: | Extensive experiments on two public real-world datasets show that the proposed network outperforms the state-of-the-art models. |
EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation (2021.findings-emnlp)
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| Challenge: | Pre-trained language models have shown remarkable results on various NLP tasks. |
| Approach: | They propose to improve the feed-forward network (FFN) in BERT with a higher computational cost than improving the multi-head attention (MHA). |
| Outcome: | The proposed model is 6.9 smaller and 4.4 faster than BERTBASE and has competitive performances on GLUE and SQuAD Benchmarks. |
Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving (2021.findings-emnlp)
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| Challenge: | Existing news recommendation methods rely on user behavior data to model user interests and user interests. |
| Approach: | They propose a unified news recommendation framework that uses user data locally stored in user clients to train models and serve users in a privacy-preserving way. |
| Outcome: | The proposed framework outperforms baseline methods and effectively protects user privacy. |
Mapping Language to Programs using Multiple Reward Components with Inverse Reinforcement Learning (2021.findings-emnlp)
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| Challenge: | Existing approaches focus on likelihood-based training or using reinforcement learning to fine-tune models based on a single reward. |
| Approach: | They propose an approach to fine-tune programs from natural language instruction . they propose a reward function that linearly combines them and a policy for program generation . |
| Outcome: | The proposed approach achieves better performance than competing methods using Reinforcement Learning. |
Topic-Guided Abstractive Multi-Document Summarization (2021.findings-emnlp)
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| Challenge: | Existing studies on multi-document summarization (MDS) focus on extractive and abstractive approaches to create a fluent and concise summary for a collection of thematically related documents. |
| Approach: | They propose a novel abstractive MDS model that represents multiple documents as a heterogeneous graph and then applies a graph-to-sequence framework to generate summaries. |
| Outcome: | The proposed model outperforms state-of-the-art models on Rouge scores and human evaluation, while learning high-quality topics. |
An Edge-Enhanced Hierarchical Graph-to-Tree Network for Math Word Problem Solving (2021.findings-emnlp)
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| Challenge: | Existing work on graph neural networks to capture word relationships neglects the rest of the problem. |
| Approach: | They propose an edge-enhanced hierarchical graph encoder to incorporate edge label information. |
| Outcome: | The proposed model can improve performance on the MAWPS and Math23K datasets compared with state-of-the-art methods. |
SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation (2021.findings-emnlp)
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| Challenge: | Generating texts in scientific papers requires not only capturing the content contained within the given input but also frequently acquiring the external information called context. |
| Approach: | They propose a task of context-aware text generation in the scientific domain to exploit the contributions of context in generated texts. |
| Outcome: | The proposed dataset comprehensively benchmarks the efficacy of the proposed dataset in generating description and paragraph. |
Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting (2021.findings-emnlp)
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| Challenge: | a method for user targeting is developed to identify online users to whom an ad should be targeted. |
| Approach: | They propose a method for automatic augmentation of positive and negative clickthrough data for user targeting models. |
| Outcome: | The proposed method can increase positive and negative instances of positive training instances on two datasets. |
Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph (2021.findings-emnlp)
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| Challenge: | Existing approaches to cross-lingual text classification require task-specific training data in high-resource sources . labeling cost, task characteristics, and privacy concerns can hinder the use of cross-linguistic training . |
| Approach: | They propose a dictionary-based heterogeneous graph (DHGNet) that uses bilingual dictionaries for task-independent word embeddings. |
| Outcome: | The proposed method outperforms pretrained models even though it does not access to large corpora. |
Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning (2021.findings-emnlp)
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| Challenge: | Named entity recognition (NER) is a method of detecting entity spans and classifying them into predefined categories. |
| Approach: | They propose a method to iteratively perform noisy label refinery by using self-collaborative denoising learning. |
| Outcome: | The proposed learning paradigm exploits reliable labels and communicates with unreliable annotations by collaborative denoising. |
Entity-Based Semantic Adequacy for Data-to-Text Generation (2021.findings-emnlp)
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| Challenge: | Existing pre-trained language models have improved the fluency of text generation systems, but semantic adequacy remains an unsolved issue. |
| Approach: | They propose an automatic evaluation metric to assess to what extent models that verbalise RDF graphs produce text that contains mentions of entities occurring in the input. |
| Outcome: | The proposed metric can be used to assess to what extent generation models verbalise RDF graphs produce text that contains mentions of the entities occurring in the input. |
MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization (2021.findings-emnlp)
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| Challenge: | Current news summarization systems often contain 'extrinsic hallucinations', i.e. facts that are not present in the source document, which are often derived via world knowledge. |
| Approach: | They propose to use multiple supplementary resource documents to assist the task by pairing a single document with a human authored summary as the summary. |
| Outcome: | The proposed model reduces 55% of hallucinations when compared to single-document summarization models trained on the main article only. |
A Conditional Generative Matching Model for Multi-lingual Reply Suggestion (2021.findings-emnlp)
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| Challenge: | Existing models for multilingual RS are limited by capacity and data distribution skew . we propose Conditional Generative Matching models (CGM) to overcome these challenges . |
| Approach: | They propose Conditional Generative Matching models to address multilingual RS challenges . they use expressive message conditional priors, mixture densities and latent alignment . results exceed ROUGE scores by 10% on average, and 16% for low resource languages . |
| Outcome: | The proposed model exceeds baselines in relevance by 10% on average and 16% for low resource languages. |
Rethinking Sentiment Style Transfer (2021.findings-emnlp)
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| Challenge: | Existing evaluation methods for text style transfer are unsatisfactory. |
| Approach: | They propose to use a graph-based method to extract attribute content from sentences . they propose an efficient regularization to leverage attribute-dependent content as guiding signals. |
| Outcome: | The proposed method is based on a YELP and IMDB dataset and it is able to detect errors in the human evaluation. |
HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge (2021.findings-emnlp)
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| Challenge: | despite its abundance, the computational explorations of hyperboles remain under-explored. |
| Approach: | They propose a sentence-level hyperbole generation method that leverages commonsense and counterfactual inference to generate hyperbolic candidates based on the results. |
| Outcome: | The proposed method generates hyperboles with high success rate, intensity, funniness, and creativity. |
Profiling News Discourse Structure Using Explicit Subtopic Structures Guided Critics (2021.findings-emnlp)
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| Challenge: | Experimental results show that the hierarchical model learns to segment a document into subtopics and improves performance on the news discourse profiling task. |
| Approach: | They propose a hierarchical neural network that models multi-level interaction between sentences, subtopics, and the document. |
| Outcome: | The proposed model outperforms the existing model on the news discourse profiling task. |
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection (2021.findings-emnlp)
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| Challenge: | Existing methods to detect out-of-domain (OOD) inputs are limited and lack data. |
| Approach: | They propose a new architecture that extends Prototypical Networks to process in-domain and OOD sentences via Mutual Information Maximization objective. |
| Outcome: | The proposed method significantly improves performance up to 20% for OOD detection in low resource settings of text classification. |
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework (2021.findings-emnlp)
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| Challenge: | Named entity recognition (NER) is a language understanding task that requires large amounts of in-domain labeled data to perform well. |
| Approach: | They propose a framework which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data. |
| Outcome: | The proposed method brings 10%, 23% and 26% improvements over baselines in few-shot learning, domain transfer and zero-shot settings respectively. |
BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks (2021.findings-emnlp)
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| Challenge: | Biomedical entity linking is a task of linking entities in biomedical documents to referent entities in a knowledge base. |
| Approach: | They propose an efficient convolutional neural network with residual connections for biomedical entity linking. |
| Outcome: | The proposed model achieves comparable or even better linking accuracy on five public datasets while having about 60 times fewer parameters. |
Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality (2021.findings-emnlp)
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| Challenge: | Byte-pair encoding (BPE) is a ubiquitous algorithm in the tokenization process of language models but is only based on pre-training data statistics. |
| Approach: | They propose a character-based subword module that learns the subword embedding table in pre-trained language models like BERT. |
| Outcome: | The proposed method significantly improves the performance on the social media linguistic code-switching evaluation (LinCE) benchmark. |
Exploring Multitask Learning for Low-Resource Abstractive Summarization (2021.findings-emnlp)
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| Challenge: | Recent work shows that training text encoders using data from multiple tasks helps to produce an encoder that can be used in numerous downstream tasks with minimal fine-tuning. |
| Approach: | They incorporate four different tasks to improve abstractive summarization performance . they use a pretrained BERT model and train all tasks using a small-scale training corpus . |
| Outcome: | The proposed model outperforms a model trained in a multitask setting with no additional summarization data. |
Conical Classification For Efficient One-Class Topic Determination (2021.findings-emnlp)
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| Challenge: | a lack of research on efficient methods for topic classification of text is limiting . conical classification is a computationally efficient way to identify documents of a particular topic . |
| Approach: | They propose a Conical classification approach that can identify if a document is of a particular topic . they propose 'normal exclusion' approach that is faster to compute and has higher predictive power . |
| Outcome: | The proposed method has higher predictive power and is faster to compute. |
Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation (2021.findings-emnlp)
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| Challenge: | Existing models rely on a traditional cross-entropy loss function during training, which may not be optimal for improving the joint goal accuracy. |
| Approach: | They propose a Turn-based Loss Function that penalises the model if it inaccurately predicts a slot value at the early turns more so than in later turns to improve joint goal accuracy. |
| Outcome: | The proposed techniques improve the state-of-the-art model by approximately 7-8% relative reduction in error and achieve a new state- of-the art joint goal accuracy with 59.50 and 54.90 on MultiWOZ2.1 and MultiWOz2.2, respectively. |
TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling (2021.findings-emnlp)
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| Challenge: | Existing dialog models can generate on-topic utterances but struggle to proactively switch topics. |
| Approach: | They propose a topic-shift aware dialog benchmark based on human topic shift annotations. |
| Outcome: | The proposed benchmark enables chatbots to generate topic-shift responses while still struggling to decide when to change topic. |
Optimal Neural Program Synthesis from Multimodal Specifications (2021.findings-emnlp)
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| Challenge: | Existing methods for multimodal program synthesis combine noisy signals from the user with hard constraints on the program’s behavior. |
| Approach: | They propose an optimal neural synthesis approach where the goal is to find a program that satisfies user-provided constraints while also maximizing the program’s score with respect to a neural model. |
| Outcome: | The proposed approach outperforms prior state-of-the-art methods in terms of accuracy and efficiency and finds model-optimal programs more frequently. |
Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations (2021.findings-emnlp)
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| Challenge: | Experiments show that PICO span detection results achieve much higher results for recall when compared to fully supervised methods. |
| Approach: | They propose to extract and then normalise PICO information from clinical trial articles and use crowdsourced sentence-level annotations to detect spans. |
| Outcome: | The proposed method achieves much higher results for recall when compared to fully supervised methods with PICO sentence detection at least as good as human annotations. |
When in Doubt: Improving Classification Performance with Alternating Normalization (2021.findings-emnlp)
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| Challenge: | a classifier that uses a nonparametric post-processing step for classification suffers when given examples that are close to its decision boundary. |
| Approach: | They propose a nonparametric post-processing step that re-adjusts predicted class probability distributions using high-confidence validation examples. |
| Outcome: | The proposed method improves classifier accuracy on difficult examples. |
APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing (2021.findings-emnlp)
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| Challenge: | Existing models for dependency parsing use labeled training data for several fixed domains, but performance drops when labeles only exist for several out-domains. |
| Approach: | They propose a model for multi-source cross-domain dependency parsing that uses a parameter generation network and adversarial network for learning domain-invariant representations. |
| Outcome: | The proposed model improves cross-domain parsing performance by about 2 points over strong BERT-enhanced baselines over a recently released dataset for multi-domain dependency parse. |
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding (2021.findings-emnlp)
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| Challenge: | Existing studies on character-centric understanding of narratives focus on understanding the characters in the narrative, but these studies are limited to understanding only certain aspects of characters. |
| Approach: | They propose a dataset of literary pieces and their summaries paired with descriptions of characters that appear in them that are used to facilitate character-centric narrative understanding. |
| Outcome: | The proposed dataset includes literary pieces and their summaries paired with descriptions of characters that appear in them. |
Towards Developing a Multilingual and Code-Mixed Visual Question Answering System by Knowledge Distillation (2021.findings-emnlp)
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| Challenge: | Existing methods for training language-vision models only consider monolingual learning, especially English. |
| Approach: | They propose to extend an English language-vision model into a multilingual and code-mixed model by using knowledge distillation techniques. |
| Outcome: | The proposed model outperforms existing models on multilingual and code-mixed VQA datasets on eleven languages. |
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis (2021.findings-emnlp)
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| Challenge: | Existing approaches to Aspect-based sentiment analysis do not exploit the interactive relations among subtasks and do not utilize document-level labeled domain/sentiment knowledge, which restricts their performance. |
| Approach: | They propose an iterative multi-knowledge transfer network for end-to-end ABSA that leverages the inter-task interaction between subtasks. |
| Outcome: | The proposed approach improves on three benchmark datasets. |
Semantic Alignment with Calibrated Similarity for Multilingual Sentence Embedding (2021.findings-emnlp)
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| Challenge: | Existing methods for learning semantic similarity between two English sentences have focused on one sub-task and therefore showed biased performance. |
| Approach: | They propose a method to learn semantic similarity between two English sentences using siamese networks. |
| Outcome: | The proposed method improves on both sub-tasks and predicts similarity scores in 14 languages. |
fBERT: A Neural Transformer for Identifying Offensive Content (2021.findings-emnlp)
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| Challenge: | Existing models such as BERT, XLNET, and XLM-R have outperformed other neural architectures and statistical learning methods in the identification of offensive language and hate speech. |
| Approach: | They present a BERT model retrained on SOLID, the largest English offensive language identification corpus available with over 1.4 million offensive instances. |
| Outcome: | The proposed model outperforms models trained on SOLID, the largest English offensive language identification corpus available with over 1.4 million offensive instances. |
WIKIBIAS: Detecting Multi-Span Subjective Biases in Language (2021.findings-emnlp)
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| Challenge: | a particular type of bias is subjective bias, which introduces improper attitudes or presents a statement with the presupposition of truth. |
| Approach: | They propose to annotate a Wikipedia edits corpus with 4,000 sentence pairs to detect subjective bias. |
| Outcome: | The proposed dataset can be used as a research benchmark and generalize to multiple domains. |
UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings (2021.findings-emnlp)
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| Challenge: | Existing methods to reduce word embedding parameters ignore semantic information . existing methods do not consider semantic information, allowing for performance degradation . |
| Approach: | They propose a method that leverages semantic similarity with weight sharing to reduce dimensionality of word embeddings. |
| Outcome: | The proposed method reduces word embedding parameters by more than 11x on a standard English-German dataset. |
Grounded Graph Decoding improves Compositional Generalization in Question Answering (2021.findings-emnlp)
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| Challenge: | Current compositional generalization models lose syntax context when learning a flat input . a new method to improve compositional globalization is proposed to ground structured predictions with an attention mechanism. |
| Approach: | They propose a method to ground structured predictions by a structure-based attention mechanism. |
| Outcome: | The proposed method performs competitively on the Compositional Freebase Questions dataset. |
Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented Guesser (2021.findings-emnlp)
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| Challenge: | Existing methods to build a visual dialog (VD) Questioner do not provide explicit guidance for questioner to generate visually related and informative questions. |
| Approach: | They propose a Related entity enhanced Questioner that learns entity-based questioning strategy from human dialogs. |
| Outcome: | The proposed approach achieves state-of-the-art performance on image-guessing task and question diversity. |
A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base (2021.findings-emnlp)
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| Challenge: | Existing knowledge bases (KBs) can explicitly facilitate the QA process. |
| Approach: | They propose a numerical reasoning model pretraining NumGNN and NumTransformer, guided by explicit self-supervision signals, to enhance numerical reasoning ability for IR-based KBQA models. |
| Outcome: | Extensive experiments on two KBQA benchmarks confirm the effectiveness of the proposed model. |
RoR: Read-over-Read for Long Document Machine Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Existing models for machine reading comprehension are limited to individual chunks due to encoding length constraint. |
| Approach: | They propose a read-over-read method that expands the reading field from chunk to document by predicting regional answers for each chunk. |
| Outcome: | Extensive experiments on QuAC and TriviaQA show that the proposed model performs well for long document reading. |
Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing (2021.findings-emnlp)
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Akshat Shrivastava, Pierce Chuang, Arun Babu, Shrey Desai, Abhinav Arora, Alexander Zotov, Ahmed Aly
| Challenge: | a novel approach to map utterances to semantic frames is based on non-autoregressive parsers that shift the decoding task from text generation to span prediction. |
| Approach: | They propose a non-autoregressive, task-oriented parser which shifts the decoding task from text generation to span prediction and produces endpoints as opposed to text. |
| Outcome: | The proposed model bridges the quality gap between non-autoregressive and autoregressive parsers, achieving 87 EM on TOPv2 and shows a 70% reduction in latency and 83% reduction in memory at beam size 5 compared to prior non-regressives. |
Language Resource Efficient Learning for Captioning (2021.findings-emnlp)
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| Challenge: | XE loss and SC loss are both considered to be performance degradations for captioning tasks. |
| Approach: | They propose to generalize the single pairwise comparison in SC loss and use multiple generalized pairwise compares to reduce noise in baseline. |
| Outcome: | The proposed method outperforms state-of-the-art models on a video caption dataset using only half of the language resources. |
Translation as Cross-Domain Knowledge: Attention Augmentation for Unsupervised Cross-Domain Segmenting and Labeling Tasks (2021.findings-emnlp)
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| Challenge: | Chinese has no word delimiter or inflection that can indicate segment boundaries or word semantics, increasing the difficulty of segmenting and labeling tasks. |
| Approach: | They propose a paradigm based on attention augmentation to introduce crucial cross-domain knowledge via a translation system into Chinese model. |
| Outcome: | The proposed model significantly advances the state-of-the-art results of Chinese cross-domain segmenting and labeling tasks. |
ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts (2021.findings-emnlp)
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| Challenge: | Contract review is a time-consuming procedure that costs companies millions of dollars each year . linguistic characteristics of contracts, such as negations by exceptions, contribute to the difficulty of this task . |
| Approach: | They propose a document-level natural language inference (NLI) task for contracts . they annotate and release the largest corpus to date consisting of 607 annotated contracts a linguistically rich system is proposed . |
| Outcome: | The proposed system is based on a contract review task that includes 607 annotated contracts. |
Japanese Zero Anaphora Resolution Can Benefit from Parallel Texts Through Neural Transfer Learning (2021.findings-emnlp)
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| Challenge: | Using a pretraining model, we find that the performance of Japanese zero anaphora resolution (ZAR) is improved by using machine translation. |
| Approach: | They propose to inject machine translation as an intermediate task between pretraining and ZAR by injecting machine translation into a pretrained BERT model and injecting it into MT. |
| Outcome: | The proposed framework shows that Japanese zero anaphora resolution (ZAR) can be improved by transfer learning from machine translation (MT). |
Grouped-Attention for Content-Selection and Content-Plan Generation (2021.findings-emnlp)
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| Challenge: | Recent neural data-to-text generation models explicitly learn content-plan given a set of attributes as input. |
| Approach: | They propose a neural content-planner that captures local and global contexts . they use a token-level attention constrained within each input attribute . |
| Outcome: | The proposed model outperforms competitors by 4.92%, 4.70%, and 16.56% on real-world datasets. |
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling (2021.findings-emnlp)
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| Challenge: | Intent classification and slot filling are key building blocks in task-oriented dialogue systems. |
| Approach: | They propose an explicit-joint and supervised-contrastive learning framework for few-shot intent classification and slot filling. |
| Outcome: | The proposed model extracts intent and slot representations via bidirectional interactions and extends prototypical network to achieve explicit-joint learning. |
Retrieve, Discriminate and Rewrite: A Simple and Effective Framework for Obtaining Affective Response in Retrieval-Based Chatbots (2021.findings-emnlp)
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| Challenge: | Existing work on retrieval-based chatbots has low-quality affect response . Existing frameworks for obtaining affective response are based on Retrieve-and-Rerank . |
| Approach: | They propose a retrieval-based framework which provides affective response for retrieval chatbots by using a new discriminate-and-rewrite mechanism. |
| Outcome: | The proposed framework outperforms existing baselines and can guarantee the quality of the response and satisfy the affect label. |
Span Fine-tuning for Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing methods to fine-tune pre-trained language models are time-consuming and lack flexibility. |
| Approach: | They propose a span fine-tuning method which allows for a more efficient and efficient way of incorporating span-level information into pre-training. |
| Outcome: | Experiments on GLUE benchmark show that the proposed method significantly enhances the PrLM and offers more flexibility in an efficient way. |
DIRECT: Direct and Indirect Responses in Conversational Text Corpus (2021.findings-emnlp)
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| Challenge: | Neural conversation models have been able to generate fluent responses through training on a dialogue corpus, but they lack the ability to reveal the implied intentions of users. |
| Approach: | They propose to train neural conversation models on a dialogue corpus that provides pragmatic paraphrases to advance techniques for natural language understanding in dialogue systems. |
| Outcome: | The proposed corpus provides 71,498 pairs of indirect–direct utterance pairs accompanied by a multi-turn dialogue history extracted from the MultiWoZ dataset. |
Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning (2021.findings-emnlp)
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| Challenge: | Existing approaches fail to generalize well to concepts that are not observed during training. |
| Approach: | They propose a framework that revolves around probing several similar image caption training instances and performing analogical reasoning over relevant entities in retrieved prototypes. |
| Outcome: | The proposed framework improves on the widely used image captioning benchmarks and on composition-related evaluation metrics. |
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation (2021.findings-emnlp)
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| Challenge: | Recent advances in generative language models have enabled machines to generate realistic texts. |
| Approach: | They propose a benchmark environment to test the 'Turing Test' problem for neural text generation methods. |
| Outcome: | The proposed benchmark environment is based on 200K human- or machine-generated samples across 20 labels Human, GPT-1, GTP-2_small, GTT-2_medium, GPG-2_large, GGT-2_PyTorch, GGP-3, GROVER_base, griover_large and GRover_mega. |
Say ‘YES’ to Positivity: Detecting Toxic Language in Workplace Communications (2021.findings-emnlp)
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| Challenge: | Toxic workplace communication is subtle, hidden or shows human biases . lack of corpus, sparsity of toxicity in enterprise emails hinder study . |
| Approach: | They propose a taxonomy to study toxic language at the workplace and a dataset to study it. |
| Outcome: | The proposed taxonomy provides a general and computationally viable taxonomies for studying toxic language at the workplace and analyzes why offensive language and hate-speech datasets are not suitable to detect workplace toxicity. |
Natural SQL: Making SQL Easier to Infer from Natural Language Specifications (2021.findings-emnlp)
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| Challenge: | Existing models that do not support executable SQL generation can generate executable queries. |
| Approach: | They propose an SQL intermediate representation called Natural SQL (NatSQL) they propose to preserve the core functionalities of SQL while simplifying the queries . |
| Outcome: | The proposed model outperforms existing models on a text-to-SQL benchmark . it significantly improves the performance of previous models on the same dataset . |
Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization (2021.findings-emnlp)
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| Challenge: | a new study explores data manipulation techniques for improving abstractive summarization models without the need for any additional data. |
| Approach: | They propose a method of data synthesis with paraphrasing, data augmentation with sample mixing and curriculum learning with new difficulty metrics based on specificity and abstractiveness. |
| Outcome: | The proposed techniques improve abstractive summarization models without additional data . the proposed techniques can be applied in isolation and when combined . |
Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Existing models for multi-party dialogue machine reading comprehension focus on how to incorporate speaker information into the model, which is usually rare in real scenarios. |
| Approach: | They propose to model speaker and key-utterances using self-supervised prediction tasks and capture salient clues in a long dialogue. |
| Outcome: | The proposed method outperforms baseline models and state-of-the-art models on two benchmark datasets. |
Few-Shot Novel Concept Learning for Semantic Parsing (2021.findings-emnlp)
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| Challenge: | Existing deep learning algorithms typically require thousands of examples to learn novel concepts. |
| Approach: | They propose an algorithm for learning novel concepts by representing them as programs over existing concepts. |
| Outcome: | The proposed approach outperforms end-to-end neural semantic parsers in a few-shot novel concept learning setting. |
Compositional Data and Task Augmentation for Instruction Following (2021.findings-emnlp)
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| Challenge: | Existing models that understand spatial concepts and compositional language are inadequate for executing natural language instructions in a physically grounded domain. |
| Approach: | They propose to use knowledge-free auxiliary signals to help the model understand compositional instructions and provide supervision for the instruction's components. |
| Outcome: | The proposed model correctly identifies the source block while the existing model fails on this example. |
Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization (2021.findings-emnlp)
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| Challenge: | Despite the progress of factual evaluation methods, they are limited in their opacity and lack the ability to assess the factuality of the summaries. |
| Approach: | They propose to use a meta-evaluation methodology to diagnose the fine-grained strengths and weaknesses of 6 existing top-performing metrics over 24 diagnostic test datasets. |
| Outcome: | The proposed method diagnoses the strengths and weaknesses of 6 existing top-performing metrics over 24 diagnostic test datasets and searches for directions for further improvement by data augmentation. |
On the Effects of Transformer Size on In- and Out-of-Domain Calibration (2021.findings-emnlp)
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| Challenge: | Large pre-trained transformer language models are notoriously expensive to train . prior work has developed smaller, more compact models to reduce training costs . |
| Approach: | They propose to develop smaller, more compact transformer language models which can be calibrated in-domain . they show that smaller models can achieve competitive calibration compared to larger models . |
| Outcome: | The proposed models achieve competitive calibration and better calibration than larger models on a wide range of tasks. |
Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings (2021.findings-emnlp)
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| Challenge: | polarization of the news media has been blamed for fanning disagreement, controversy and even violence. |
| Approach: | They propose a method to automatically detect polarized topics from partisan news sources by corpus-contextualized topic embedding a news corpus on a topic and using cosine distance to capture topical polarization. |
| Outcome: | The proposed method captures topical polarization and shows it can retrieve the most polarized topics. |
GenerativeRE: Incorporating a Novel Copy Mechanism and Pretrained Model for Joint Entity and Relation Extraction (2021.findings-emnlp)
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| Challenge: | Existing models for extracting relation triplets suffer from incompletion and disorder problems when they extract multi-token entities from input sentences. |
| Approach: | They propose a special entity labelling method that fine-tunes the pre-trained model and learns the special entity labels simultaneously. |
| Outcome: | The proposed model achieves 4.6% and 0.9% improvement over current methods in the NYT24 and NYT29 benchmark datasets. |
Re-entry Prediction for Online Conversations via Self-Supervised Learning (2021.findings-emnlp)
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| Challenge: | Existing work on re-entry prediction ignores conversation thread patterns and repeated engagement of target users. |
| Approach: | They propose to use conversation thread patterns to predict whether a user will come back to a conversation they once participated in to train a model on labels that are automatically derived from the data. |
| Outcome: | The proposed task outperforms the state-of-the-art models on two social media datasets with fewer parameters and faster convergence. |
proScript: Partially Ordered Scripts Generation (2021.findings-emnlp)
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| Challenge: | Scripts represent structured commonsense knowledge about prototypical events in everyday situations/scenarios such as bake a cake. |
| Approach: | They collect 6.4k crowdsourced partially ordered scripts and develop models that combine language generation and graph structure prediction to generate scripts. |
| Outcome: | The proposed models perform well on two tasks: edge prediction and script generation. |
Speaker Turn Modeling for Dialogue Act Classification (2021.findings-emnlp)
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| Challenge: | Existing approaches to DA classification model utterances without incorporating the turn changes among speakers throughout the dialogue, thus treating it no different than non-interactive written text. |
| Approach: | They propose to integrate the turn changes in conversations among speakers when modeling DAs by learning conversation-invariant speaker turn embeddings to represent speaker turns in a conversation. |
| Outcome: | The proposed model captures semantics from the dialogue content while accounting for different speaker turns in a conversation. |
Unsupervised Domain Adaptation Method with Semantic-Structural Alignment for Dependency Parsing (2021.findings-emnlp)
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| Challenge: | Existing methods for dependency parsing are often of the pseudo-annotation type, but they fail to consider the change of model structure for domain adaptation. |
| Approach: | They propose a method that accomplishes unsupervised cross-domain dependency parsing without using labeled data. |
| Outcome: | The proposed method achieves consistent performance improvement on CODT1 and CTB9 domains. |
Devil’s Advocate: Novel Boosting Ensemble Method from Psychological Findings for Text Classification (2021.findings-emnlp)
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| Challenge: | Existing ensemble methods that combine submodels to create a composite model can improve model performance by diminishing model bias and variance. |
| Approach: | They propose a method which uses a deliberately dissenting model to force other submodels within the ensemble to better collaborate. |
| Outcome: | The proposed method shows comparable or improved performance on 5 text classification tasks when compared to conventional methods. |
SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks (2021.findings-emnlp)
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| Challenge: | Existing methods to generate pre-trained language models with attributes are expensive and overfitted on small training sets. |
| Approach: | They propose a novel approach to control the generation of Transformer-based pre-trained language models using a new control attributes loss framework. |
| Outcome: | The proposed method is shown to perform well with very limited training samples. |
Is BERT a Cross-Disciplinary Knowledge Learner? A Surprising Finding of Pre-trained Models’ Transferability (2021.findings-emnlp)
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| Challenge: | Using pre-trained language models, we can apply them to specialized domains such as scientific articles or clinical data. |
| Approach: | They propose to pre-train BERT models on large text corpora and use them to generalize to token sequence classification applications. |
| Outcome: | The models pre-trained on text classification tasks perform better than the models using task-specific knowledge and share non-trivial similarities. |
Geo-BERT Pre-training Model for Query Rewriting in POI Search (2021.findings-emnlp)
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| Challenge: | Existing methods to solve the word mismatch between queries and documents are often inadequate to integrate geographic information into the pre-training model. |
| Approach: | They propose to train a pre-training model to integrate semantics and geographic information in the pre-trained representations of POIs. |
| Outcome: | The proposed model achieves excellent accuracy on a wide range of real-world datasets of map services. |
Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search (2021.findings-emnlp)
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| Challenge: | Existing relevance models rely on query-keyword pairs but keywords are usually short texts with scarce semantic information, which may not accurately reflect the underlying advertising purposes. |
| Approach: | They propose a bidding-graph augmented triple-based relevance model with three towers to deeply fuse the bidding graphs and semantic textual data. |
| Outcome: | The proposed model outperforms existing models on a large industry dataset and consistently outperformed existing models. |
GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation (2021.findings-emnlp)
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| Challenge: | Recent studies report that prompt-based direct classification eliminates the need for fine-tuning but lacks data and inference scalability. |
| Approach: | They propose a data augmentation technique that leverages large-scale language models to generate real text samples from a mixture of real samples. |
| Outcome: | The proposed method outperforms existing methods on diverse classification tasks. |
Context-aware Entity Typing in Knowledge Graphs (2021.findings-emnlp)
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| Challenge: | Existing methods for knowledge graph entity typing are embedding-based and graph convolutional networks (GCNs) . Existing approaches for knowledge Graph Entity Typing (KGET) are incomplete and require multiple inference mechanisms. |
| Approach: | They propose a method that uses entities’ contextual information to infer missing types in knowledge graphs by using two inference mechanisms: N2T and Agg2T. |
| Outcome: | The proposed method can infer entities' missing types by completing two real-world KGs. |
Attribute Alignment: Controlling Text Generation from Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Large language models can generate text with sentiment polarity or specific topics without changing the original model parameters. |
| Approach: | They propose a method for controlling text generation by aligning disentangled attribute representations. |
| Outcome: | The proposed method shows large performance gains while maintaining diversity and fluency. |
Generate & Rank: A Multi-task Framework for Math Word Problems (2021.findings-emnlp)
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| Challenge: | Existing studies formalize MWP as a generation task but mathematical expressions are prone to minor mistakes. |
| Approach: | They propose a ranking task for math word problem (MWP) that learns from its own mistakes and distinguishes between correct and incorrect expressions. |
| Outcome: | The proposed model outperforms baselines on the classical Math23k dataset and is 7% higher than the state-of-the-art. |
MIRTT: Learning Multimodal Interaction Representations from Trilinear Transformers for Visual Question Answering (2021.findings-emnlp)
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| Challenge: | Existing bilinear methods focus on inter-modality information between images and questions . existing models focus on the interaction between images, questions, and images . |
| Approach: | They propose a trilinear interaction framework that incorporates attention mechanisms for capturing inter-modality and intra-modal relationships. |
| Outcome: | The proposed model outperforms bilinear models on the Visual7W Telling task and VQA-1.0 Multiple Choice task and outperformed baselines on the VQA, TDIUC and GQA datasets. |
UniteD-SRL: A Unified Dataset for Span- and Dependency-Based Multilingual and Cross-Lingual Semantic Role Labeling (2021.findings-emnlp)
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| Challenge: | Multilingual and cross-lingual Semantic Role Labeling (SRL) has attracted increasing attention as multilingual text representation techniques have become more effective and widely available. |
| Approach: | They propose a benchmark for multilingual and cross-lingual, span- and dependency-based SRL that provides expert-curated parallel annotations using a common predicate-argument structure inventory. |
| Outcome: | The proposed benchmark provides expert-curated parallel annotations using a common predicate-argument structure inventory, allowing direct comparisons across languages and encouraging studies on cross-lingual transfer in SRL. |
Enhancing Dual-Encoders with Question and Answer Cross-Embeddings for Answer Retrieval (2021.findings-emnlp)
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| Challenge: | Existing approaches to solve question answering (QA) problems are limited by the need for text generation and answer retrieval. |
| Approach: | They propose to introduce QA interaction features in scoring function but at the cost of low efficiency in inference stage. |
| Outcome: | The proposed framework significantly outperforms the state-of-the-art method on multiple answer retrieval datasets. |
A Neural Graph-based Local Coherence Model (2021.findings-emnlp)
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| Challenge: | Entity grids and entity graphs are two frameworks for modeling local coherence . many approaches to local cohesion modeling rely on entity relations between sentences . |
| Approach: | They propose to use Relational Graph Convolutional Networks to encode entity graphs for measuring local coherence. |
| Outcome: | The proposed model outperforms the neural grid-based model on two coherence evaluation tasks while using 50% fewer parameters. |
GiBERT: Enhancing BERT with Linguistic Information using a Lightweight Gated Injection Method (2021.findings-emnlp)
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| Challenge: | Recent pre-trained language models such as BERT have led to noticeable improvements in semantic similarity detection. |
| Approach: | They propose to explicitly inject linguistic information in the form of word embeddings into any layer of a pre-trained BERT. |
| Outcome: | The proposed method improves on multiple semantic similarity datasets and shows that it is beneficial and currently missing from the original model. |
RollingLDA: An Update Algorithm of Latent Dirichlet Allocation to Construct Consistent Time Series from Textual Data (2021.findings-emnlp)
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| Challenge: | Existing methods for generating time series on textual data are not efficient . |
| Approach: | They propose a rolling version of the Latent Dirichlet Allocation, called RollingLDA . they compute similarity of sequentially obtained topic and word distributions over consecutive time periods . |
| Outcome: | The proposed method is based on the popular model Latent Dirichlet Allocation . it is able to build time series consistent with previous states of the model . |
What If Sentence-hood is Hard to Define: A Case Study in Chinese Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Explicit Span-Sentence Predication solves location unit ambiguity problem in many languages, allowing model to determine which sentence contains the answer span when sentence itself has not been clearly defined at all. |
| Approach: | They propose a machine-learning reader with Explicit Span-Sentence Predication to solve this problem by analyzing Chinese sentences. |
| Outcome: | The proposed reader achieves state-of-the-art on Chinese MRC benchmark and shows great potential in dealing with other languages. |
Refining BERT Embeddings for Document Hashing via Mutual Information Maximization (2021.findings-emnlp)
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| Challenge: | Existing unsupervised document hashing methods are mostly established on generative models . due to the difficulties of capturing long dependency structures, these methods rarely model the raw documents directly . |
| Approach: | They propose to learn hash codes from BERT embeddings by modifying existing models . they use mutual information maximization principle to maximize mutual information . |
| Outcome: | The proposed method outperforms existing methods learned from BERT embeddings on three benchmark datasets. |
REBEL: Relation Extraction By End-to-end Language generation (2021.findings-emnlp)
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| Challenge: | Existing approaches to extract relation triplets from text often involve multiple-step pipelines that propagate errors or are limited to a small number of relation types. |
| Approach: | They propose to use autoregressive seq2seq models to simplify Relation Extraction by expressing triplets as a sequence of text and a model that performs end-to-end relation extraction for more than 200 different relation types. |
| Outcome: | The proposed model achieves state-of-the-art on an array of Relation Extraction and Relation Classification benchmarks and achieves top performance in most of them. |
Wine is not v i n. On the Compatibility of Tokenizations across Languages (2021.findings-emnlp)
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| Challenge: | Pretrained language models are used for performance and memory constraints, but there is little work that investigates the compatibility of tokenizations across languages. |
| Approach: | They propose a compatibility measure that reflects compatibility of tokenizations across languages. |
| Outcome: | The proposed measure prevents incompatible tokenizations, e.g., “wine” in English vs. “v i n” in French, which make it hard to learn good multilingual semantic representations. |
Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media (2021.findings-emnlp)
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| Challenge: | Language use differs between domains and even within a domain, language use changes over time. |
| Approach: | They propose to use social media comments to study temporal adaptations in pre-trained language models. |
| Outcome: | The proposed model performs better on past than on future test sets, whereas adapting to domain does not improve performance on the downstream task. |
Skim-Attention: Learning to Focus via Document Layout (2021.findings-emnlp)
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| Challenge: | Existing approaches to document understanding have high computational and memory costs. |
| Approach: | They propose a new attention mechanism that takes advantage of the structure of a document and its layout. |
| Outcome: | The proposed attention mechanism obtains lower perplexity than previous studies while being more computationally efficient. |
Attention-based Contrastive Learning for Winograd Schemas (2021.findings-emnlp)
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| Challenge: | Existing approaches to learn discriminative features using contrastive objective are lacking. |
| Approach: | They propose a self-supervised framework that leverages a contrastive loss directly at the level of self-attention. |
| Outcome: | The proposed framework outperforms all comparable unsupervised approaches while occasionally surpassing supervised ones. |
Give the Truth: Incorporate Semantic Slot into Abstractive Dialogue Summarization (2021.findings-emnlp)
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| Challenge: | Abstractive dialogue summarization suffers from a lot of factual errors due to scattered salient elements in multi-speaker information interaction process. |
| Approach: | They propose a slot-driven beam search algorithm to give priority to generating salient elements in a limited length by "filling-in-the-blanks". |
| Outcome: | The proposed algorithm improves the slot-driven beam search algorithm on different types of factual errors and human evaluation further verifies the results. |
Challenges in Detoxifying Language Models (2021.findings-emnlp)
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Johannes Welbl, Amelia Glaese, Jonathan Uesato, Sumanth Dathathri, John Mellor, Lisa Anne Hendricks, Kirsty Anderson, Pushmeet Kohli, Ben Coppin, Po-Sen Huang
| Challenge: | Prior work often relies on automatic evaluation of LM toxicity. |
| Approach: | They evaluate toxicity mitigation strategies for automated and human evaluations . they find human raters disagree with high automatic toxicity scores after strong toxicity reduction interventions . |
| Outcome: | The proposed methods reduce LM toxicity but lower coverage for marginalized texts . human raters disagree with high toxicity scores after strong toxicity reduction interventions . |
Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation (2021.findings-emnlp)
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| Challenge: | Recent studies have found evidence of gender bias in machine translation and coreference resolution models using mostly synthetic diagnostic datasets. |
| Approach: | They propose a semi-automatic method to vastly extend synthetic, small diagnostic datasets to include grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments. |
| Outcome: | The proposed method extends the existing dataset to 108K diverse English sentences. |
Competence-based Curriculum Learning for Multilingual Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing multilingual machine translation models face an imbalance problem due to the different learning competencies of different languages. |
| Approach: | They propose Competence-based Curriculum Learning for Multilingual Machine Translation, named CCL-M, to help schedule the high resource languages and low resource languages. |
| Outcome: | The proposed approach achieves a steady and significant performance gain compared to the previous state-of-the-art approach on the TED talks dataset. |
Informed Sampling for Diversity in Concept-to-Text NLG (2021.findings-emnlp)
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| Challenge: | Existing methods to encourage lexical diversity for language generation tasks produce repetitive outputs, but this often comes at a cost to the perceived fluency and adequacy of the output. |
| Approach: | They propose to augment the decoding process with a meta-classifier trained to distinguish which words at any given timestep will lead to high-quality output. |
| Outcome: | The proposed method achieves a high level of diversity with minimal effect on the output’s fluency and adequacy. |
Novel Natural Language Summarization of Program Code via Leveraging Multiple Input Representations (2021.findings-emnlp)
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| Challenge: | Existing work on code summarization shows that code descriptions are difficult to generate for developers unfamiliar with the code base. |
| Approach: | They propose a multi-task approach that trains two similar tasks to generate code descriptions for each line of code. |
| Outcome: | The proposed model improves over baselines and achieves the new state-of-the-art. |
WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER (2021.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is a key intermediate task in NLP. |
| Approach: | They propose a method which uses knowledge-based approaches and neural models to produce high-quality training corpora for NER. |
| Outcome: | The proposed method improves on standard benchmarks and yields significant improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation. |
Beyond Grammatical Error Correction: Improving L1-influenced research writing in English using pre-trained encoder-decoder models (2021.findings-emnlp)
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| Challenge: | Existing research on tools for improving writing focuses mostly on Grammatical Error Corrrection (GEC) but it does not adequately address fluency and complex linguistic issues. |
| Approach: | They propose a method for training a writing improvement model adapted to the writer’s first language (L1) without using annotated training data and use parallel corpora of reference translation aligned with machine translation. |
| Outcome: | The proposed model outperforms existing methods with corpora of academic papers written in English by L1 Portuguese and L1 Spanish scholars and a reference corpus of expert academic English. |
Classification and Geotemporal Analysis of Quality-of-Life Issues in Tenant Reviews (2021.findings-emnlp)
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| Challenge: | Real estate professionals often read tenant reviews to uncover property-related issues that are otherwise difficult to detect. |
| Approach: | They propose to use online tenant reviews to classify properties based on their tenant-perspective view. |
| Outcome: | The proposed method achieves a mean AUROC of 0.965 on 5.5 million tenant reviews and tens of thousands of multifamily properties. |
Probing Pre-trained Language Models for Semantic Attributes and their Values (2021.findings-emnlp)
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| Challenge: | Pretrained language models (PTLMs) are used for many tasks including syntax, semantics and commonsense. |
| Approach: | They propose to integrate semantic attributes and their values into pretrained language models to improve their performance on many natural language processing tasks. |
| Outcome: | The proposed model performs better on masked tokens than humans on this task. |
Uncovering the Limits of Text-based Emotion Detection (2021.findings-emnlp)
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| Challenge: | Identifying emotions from text is crucial for a variety of downstream tasks. |
| Approach: | They consider the two largest now-available corpora for emotion classification: GoEmotions and Vent. |
| Outcome: | The proposed models outperform the two largest corpora for emotion classification: GoEmotions and Vent. |
Named Entity Recognition for Entity Linking: What Works and What’s Next (2021.findings-emnlp)
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| Challenge: | Entity Linking (EL) systems have achieved impressive results on standard benchmarks thanks to the contextualized representations provided by recent pretrained language models. |
| Approach: | They propose to exploit Named Entity Recognition (NER) to narrow the gap between EL systems trained on high and low amounts of labeled data. |
| Outcome: | The proposed model can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. |
Learning Numeracy: A Simple Yet Effective Number Embedding Approach Using Knowledge Graph (2021.findings-emnlp)
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| Challenge: | Existing models for numeracy-intensive applications fail to learn numerability . existing models fail to handle numbers, resulting in performance problems . |
| Approach: | They propose a number embedding approach that embeds numbers into dimensional space . they construct a knowledge graph consisting of number entities and magnitude relations . |
| Outcome: | The proposed method is easy to implement and shows that it performs well on numeracy-related tasks. |
Weakly Supervised Semantic Parsing by Learning from Mistakes (2021.findings-emnlp)
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| Challenge: | Weakly supervised semantic parsing requires searching consistent logical forms in a huge space and dealing with spurious logical form. |
| Approach: | They propose a learning framework that trains parsers via utterance-denotation pairs . they use utterrance-logical form pairs created from mistakes to bootstrap parser . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on WikiSQL, TabFact and other datasets. |
CodeQA: A Question Answering Dataset for Source Code Comprehension (2021.findings-emnlp)
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| Challenge: | False. a free-form question answering dataset can serve as a useful research benchmark for source code comprehension. |
| Approach: | They propose a free-form question answering dataset for source code comprehension . they implement syntactic rules and semantic analysis to transform code comments into question-answer pairs. |
| Outcome: | The proposed dataset can serve as a useful research benchmark for source code comprehension. |
Subword Mapping and Anchoring across Languages (2021.findings-emnlp)
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| Challenge: | State-of-the-art multilingual systems rely on shared vocabularies that cover all considered languages. |
| Approach: | They propose a method to construct bilingual subword vocabularies by mapping and anchoring subwords together over multiple languages. |
| Outcome: | The proposed method improves zero-shot transfer to an unseen language without task-specific data, but only by sharing subword embeddings. |
CDLM: Cross-Document Language Modeling (2021.findings-emnlp)
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| Challenge: | Existing language models (LMs) provide powerful representations for internal text structure, but there are important applications for multi-text tasks. |
| Approach: | They propose a pretraining approach that incorporates two key ideas into the masked language modeling objective. |
| Outcome: | The proposed model improves over existing models and sets of long-range transformers and can be easily applied to multiple multi-text tasks. |
Patterns of Polysemy and Homonymy in Contextualised Language Models (2021.findings-emnlp)
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| Challenge: | a recent study has focused on homonymy, a variety of multiplicity of meanings exemplified by word forms with unrelated meanings. |
| Approach: | They investigate the extent to which contextualised embeddings reflect traditional distinctions of polysemy and homonymy. |
| Outcome: | The proposed model shows that it can distinguish between polysemy and homonymy . it shows that the model fails to replicate the results of the human-annotated dataset . |
Cross-Lingual Leveled Reading Based on Language-Invariant Features (2021.findings-emnlp)
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| Challenge: | Leveled reading (LR) aims to automatically classify texts by the cognitive levels of readers. |
| Approach: | They propose to use adversarial training and cross-lingual pre-training methods to transfer LR knowledge from annotated data in resource-rich English to Chinese. |
| Outcome: | The proposed method captures language-invariant features between English and Chinese. |
Controlled Neural Sentence-Level Reframing of News Articles (2021.findings-emnlp)
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| Challenge: | a news article is framed from a specific perspective, but reframing can be difficult . a framed article can be used to communicate with opposing camps of audiences . |
| Approach: | They propose to reframe news articles using a media frame corpus to achieve this . they propose three strategies to train neural models for reframing . |
| Outcome: | The proposed techniques maintain coherence of sentences and reframe them correctly . the proposed techniques are effective but have tradeoffs . |
DialogueTRM: Exploring Multi-Modal Emotional Dynamics in a Conversation (2021.findings-emnlp)
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| Challenge: | Existing studies focus on the self and inter-personal dependencies in multi-modal conversations, but they ignore the temporal and spatial dependencies. |
| Approach: | They propose a Dialogue Transformer for simultaneously modeling the intra-modal and inter-modal emotion dynamics. |
| Outcome: | The proposed models outperform the state-of-the-art on three benchmark datasets. |
Adversarial Examples for Evaluating Math Word Problem Solvers (2021.findings-emnlp)
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| Challenge: | Existing MWP solvers do not understand language and its relation with numbers, and their accuracy is unclear. |
| Approach: | They propose two methods to generate adversarial attacks to evaluate the robustness of existing MWP solvers. |
| Outcome: | The proposed method reduces the accuracy of existing MWP solvers by over 40% on two benchmark datasets. |
Improving Numerical Reasoning Skills in the Modular Approach for Complex Question Answering on Text (2021.findings-emnlp)
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| Challenge: | Neural Module Networks (NMNs) is an end-to-end differentiable model in the programmer-interpreter paradigm. |
| Approach: | They propose to make the interpreter question-aware and capture the relationship between entities and numbers in both questions and paragraphs. |
| Outcome: | The proposed models outperform the original models on the DROP dataset and are interpertable by nature. |
Retrieval Augmented Code Generation and Summarization (2021.findings-emnlp)
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| Challenge: | Software developers often recall parts of source code or code summaries that they had written in the past while implementing software or documenting them. |
| Approach: | They propose a retrieval augmented framework that retrieves relevant code or summaries from a database and provides them as a supplement to code generation or summarization models. |
| Outcome: | The proposed framework can search for relevant code or summaries from retrieval databases and can work with unimodal (only code or natural language description) or bimodal instances (code-description pairs). |
Multilingual Translation via Grafting Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing methods to graft pre-trained (masked) language models to multilingual data are limited, and they lack cross-attention component. |
| Approach: | They propose to graft separately pre-trained (masked) language models for machine translation using monolingual data and parallel data. |
| Outcome: | The proposed method achieves average improvements of 5.8 BLEU in x2en and 2.9 BLUE in en2x directions compared with the multilingual Transformer of the same size. |
AEDA: An Easier Data Augmentation Technique for Text Classification (2021.findings-emnlp)
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| Challenge: | AEDA is an easier data augmentation technique than EDA. |
| Approach: | They propose an augmentation technique that includes only random insertion of punctuation marks into the original text. |
| Outcome: | The proposed method is easier to implement for data augmentation than EDA method. |
A Comprehensive Comparison of Word Embeddings in Event & Entity Coreference Resolution. (2021.findings-emnlp)
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| Challenge: | Existing methods for Coreference Resolution rely on word embeddings for word representation, but performance of different embeddables is largely overlooked. |
| Approach: | They frame their study in the context of Event and Entity Coreference Resolution (EvCR & EnCR) they examine whether there is a trade-off between performance and embedding size . |
| Outcome: | The embeddings achieve 86% of the performance of the largest model while being 1.2% of its size. |
Wav-BERT: Cooperative Acoustic and Linguistic Representation Learning for Low-Resource Speech Recognition (2021.findings-emnlp)
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| Challenge: | Existing methods to learn the transfer from speech to text are unexplored . how to solve the representation discrepancy of speech and text is unexplorable . |
| Approach: | They propose a cooperative acoustic and linguistic representation learning method to fuse and utilize contextual information of speech and text. |
| Outcome: | The proposed method outperforms existing methods on low-resource speech recognition. |
Multilingual AMR Parsing with Noisy Knowledge Distillation (2021.findings-emnlp)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is a broad-coverage semantic formalism that encodes the meaning of a sentence as a rooted, directed, and labeled graph. |
| Approach: | They propose to use existing English parser to learn and improve multilingual AMR parsers . their results show that noisy input and precise output are key to successful distillation . |
| Outcome: | The proposed model outperforms the current state-of-the-art English-only parser on four different languages. |
Open-Domain Contextual Link Prediction and its Complementarity with Entailment Graphs (2021.findings-emnlp)
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| Challenge: | Existing methods for linking knowledge graphs only use textual contexts . contextual link prediction is useful for finding context-dependent entailments . |
| Approach: | They propose a task of open-domain contextual link prediction which uses textual context and KG structure to perform link prediction. |
| Outcome: | The proposed model can ground the triples in the context of the original dataset and infer missing relations in context. |
Analysis of Language Change in Collaborative Instruction Following (2021.findings-emnlp)
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| Challenge: | Prior work has found that language complexity is reduced along multiple dimensions as conventions are formed. |
| Approach: | They analyze language change over time in a collaborative task where utility-maximizing participants form conventions and increase their expertise. |
| Outcome: | The study shows that instructors increase language complexity along dimensions to collaborate with skill followers. |
Counter-Interference Adapter for Multilingual Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing approaches to multilingual machine translation suffer from performance degradation, resulting in a single model being inferior to separately trained bilingual models on resource-rich languages. |
| Approach: | They propose a transformer-based model with a small parameter overhead for multilingual machine translation that outperforms strong multilingual baselines on 64 of 66 language directions. |
| Outcome: | The proposed model outperforms strong multilingual baselines on 64 of 66 language directions, 42 of which have above 0.5 BLEU improvement. |
Progressive Transformer-Based Generation of Radiology Reports (2021.findings-emnlp)
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Farhad Nooralahzadeh, Nicolas Perez Gonzalez, Thomas Frauenfelder, Koji Fujimoto, Michael Krauthammer
| Challenge: | Existing approaches to generate radiology reports are based on image-to-text generation. |
| Approach: | They propose a sequential (i.e., image-to-text-totext) generation framework that integrates high-level concepts into the generation process. |
| Outcome: | The proposed model outperforms existing models on two benchmark datasets. |
“Be nice to your wife! The restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification? (2021.findings-emnlp)
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| Challenge: | a new study examines the impact of gender stereotype detection on sexism classification . GS is defined as "pictures in our heads" and is used to describe social group members . |
| Approach: | They propose to use tweets as a dataset to detect sexist hate speech . they propose a method for data augmentation based on sentence similarity with external datasets . |
| Outcome: | The proposed method detects sexist hate speech in tweets and then uses it for sexism classification. |
Automatic Discrimination between Inherited and Borrowed Latin Words in Romance Languages (2021.findings-emnlp)
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| Challenge: | Existing approaches to discriminate between inherited and borrowed Latin words have been used to investigate the problem of automatic discrimination between a language's sound shifts. |
| Approach: | They propose a new dataset to investigate the problem of automatically discriminating between inherited and borrowed Latin words in Romance languages. |
| Outcome: | The proposed model can automatically discriminate between inherited and borrowed Latin words on two versions of the dataset, orthographic and phonetic. |
Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections (2021.findings-emnlp)
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| Challenge: | Large pre-trained language models (LMs) have a surprising ability to perform zero-shot learning. |
| Approach: | They propose to fine-tune pre-trained language models to optimize the zero-shot learning objective by aggregating 43 existing datasets and annotating 441 label descriptions in a question-answering format. |
| Outcome: | The proposed model outperforms a same-sized QA model and the previous SOTA zero-shot learning system on unseen tasks. |
Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations (2021.findings-emnlp)
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| Challenge: | Emotion Recognition in Conversation models neglect direct utterance-knowledge interaction and use emotion-indirect auxiliary tasks to augment semantic information. |
| Approach: | They propose a Knowledge-Interactive Network with sentiment polarity intensity-aware multi-task learning which leverages both commonsense knowledge and sentiment lexicon to augment semantic information. |
| Outcome: | The proposed model shows 1.04% performance improvement over the state-of-the-art model on the IEMOCAP dataset. |
Minimizing Annotation Effort via Max-Volume Spectral Sampling (2021.findings-emnlp)
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| Challenge: | Spectral sampling strategies that minimize the number of annotations required to train a model are proposed. |
| Approach: | They propose a method that maximizes the amount of information useful for the learning algorithm by minimizing redundancy of samples in the selection. |
| Outcome: | The proposed method maximizes the amount of information useful for the learning algorithm or minimizes redundancy of samples in the selection. |
On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Experimental results show that PT and BT are nicely complementary to each other. |
| Approach: | They introduce two probing tasks for PT and BT respectively and investigate their complementarity. |
| Outcome: | The proposed methods establish state-of-the-art on the WMT16 English-Romanian and English-Russian benchmarks. |
Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation (2021.findings-emnlp)
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| Challenge: | Existing methods for Chinese word segmentation have high performance on benchmarks but are limited by the small-scale annotated corpus. |
| Approach: | They propose a framework that incorporates a lexicon-based graph convolutional network into the Transformer encoder to improve Chinese word segmentation (CWS) Chinese word is an essential and pre-processing step for many downstream NLP tasks. |
| Outcome: | The proposed framework captures the information of candidate words and improves performance on benchmarks and datasets. |
KFCNet: Knowledge Filtering and Contrastive Learning for Generative Commonsense Reasoning (2021.findings-emnlp)
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| Challenge: | Pre-trained language models have led to substantial gains over a broad range of NLP tasks, but have limitations for high-quality tasks such as commonsense generation and ad keyword generation. |
| Approach: | They propose a Knowledge Filtering and Contrastive learning Network which references external knowledge and achieves better generation performance. |
| Outcome: | The proposed model outperforms the current state of the art on the CommonGen benchmark by a large margin. |
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus (2021.findings-emnlp)
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| Challenge: | Acceptability judgments are the most significant source of data in linguistics . however, there are still many open issues regarding methods for collecting and evaluating them. |
| Approach: | They propose to create a corpus of sentences with acceptability judgments using the same approach and the same steps as the English corpus. |
| Outcome: | The proposed corpus contains almost 10,000 sentences with acceptability judgments. |
Hyperbolic Hierarchy-Aware Knowledge Graph Embedding for Link Prediction (2021.findings-emnlp)
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| Challenge: | Existing knowledge graph embedding methods are built on Euclidean space, which are difficult to handle hierarchical structures. |
| Approach: | They propose a KGE model with extended Poincaré Ball and polar coordinate system to capture hierarchical structures. |
| Outcome: | The proposed model captures hierarchical relationships with extended Poincaré Ball and polar coordinate system in hyperbolic space and achieves state-of-the-art results on part of link prediction tasks. |
A Discourse-Aware Graph Neural Network for Emotion Recognition in Multi-Party Conversation (2021.findings-emnlp)
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| Challenge: | Prior research focuses on exploring sequential information but ignores the discourse structures of conversations. |
| Approach: | They propose a discourse-aware graph neural network (ERMC-DisGCN) that leverages contextual cues and speaker-specific features for ERMC. |
| Outcome: | The proposed method outperforms multiple baselines showing that discourse structures are of great value to ERMC. |
MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection (2021.findings-emnlp)
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| Challenge: | Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens. |
| Approach: | They propose a hierarchical message-encoder pre-trained over Twitter for stance prediction task. |
| Outcome: | The proposed model achieves 67% performance on stance prediction task using a pre-trained message-encoder over Twitter. |
LMSOC: An Approach for Socially Sensitive Pretraining (2021.findings-emnlp)
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| Challenge: | Large-scale pretraining models have been shown to learn effective linguistic representations for many NLP tasks, but there are many real-world contextual aspects of language that current approaches do not capture. |
| Approach: | They propose to integrate speaker social context into the learned representations of large-scale language models by using graph representation learning algorithms and primed language model pretraining with these social context representations. |
| Outcome: | The proposed approach improves on geographically sensitive language modeling tasks by more than 100% relative lift on MRR compared to baselines. |
Extract, Integrate, Compete: Towards Verification Style Reading Comprehension (2021.findings-emnlp)
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| Challenge: | VGaokao is a verification style reading comprehension dataset for Chinese language tests requiring advanced language understanding skills. |
| Approach: | They propose a new extract-integration-compete approach to extract complementary evidence from Chinese Language tests of Gaokao and a pairwise competition to push models to learn the subtle difference between similar text pieces. |
| Outcome: | The proposed approach outperforms baselines on VGaokao with retrieved complementary evidence while having the merits of efficiency and explainability. |
Comparing learnability of two dependency schemes: ‘semantic’ (UD) and ‘syntactic’ (SUD) (2021.findings-emnlp)
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| Challenge: | Several studies have suggested that choosing syntactic criteria for assigning heads in dependency trees improves the performance of dependency parsers. |
| Approach: | They propose to use syntactic criteria to assign heads to dependency trees to improve the performance of dependency parsers by using a selection of 21 treebanks. |
| Outcome: | The proposed approach favours content words over function words as heads of dependency relations, while the other favours syntactic heads. |
Argumentation-Driven Evidence Association in Criminal Cases (2021.findings-emnlp)
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| Challenge: | Existing evidence association methods focus on extracting rich semantic representation and then calculate cosine distance between text representations. |
| Approach: | They propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step. |
| Outcome: | The proposed method can be used to calculate distance between evidence pairs on a real-world dataset. |
Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction (2021.findings-emnlp)
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| Challenge: | Aspect-level sentiment classification (ALSC) is a practical setting in aspect-based sentiment analysis due to no opinion term labeling needed, but it fails to interpret why a sentiment polarity is derived for the aspect. |
| Approach: | They propose a span-based anti-bias aspect representation learning framework that eliminates the sentiment bias in the aspect embedding by adversarial learning against aspects’ prior sentiment. |
| Outcome: | The proposed framework achieves state-of-the-art performance on five benchmarks, with the capability of unsupervised opinion extraction. |
Data Efficient Masked Language Modeling for Vision and Language (2021.findings-emnlp)
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| Challenge: | Masked language modeling (MLM) is one of the key sub-tasks in vision-language pretraining. |
| Approach: | They propose a masking strategy that masks tokens with a 15% probability for text-only data. |
| Outcome: | The proposed masking strategy outperforms the baseline model on a prompt-based probing task designed to elicit image objects. |
Improving Multilingual Neural Machine Translation with Auxiliary Source Languages (2021.findings-emnlp)
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| Challenge: | Prior work has shown that translating from multiple source languages improves translation quality. |
| Approach: | They propose to exploit multiple source sentences from auxiliary languages to improve multilingual translation in a more common scenario by using synthetic multi-source corpora. |
| Outcome: | Extensive experiments on Chinese/English-Japanese and a large-scale multilingual translation benchmark show that the proposed model outperforms the baseline model significantly by +4.0 BLEU. |
How Does Fine-tuning Affect the Geometry of Embedding Space: A Case Study on Isotropy (2021.findings-emnlp)
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| Challenge: | Existing methods for fine-tuning pre-trained language models are ineffective, despite their potential, pre-training models suffer from important weaknesses. |
| Approach: | They analyze the extent to which the isotropy of the embedding space changes after fine-tuning. |
| Outcome: | The proposed model improves the isotropy of embedding space after fine-tuning . the model can encode linguistic properties, but lacks the social bias needed to improve it . |
Locality Preserving Sentence Encoding (2021.findings-emnlp)
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| Challenge: | Existing word embeddings can be used to learn sentence embedds on the sentence level. |
| Approach: | They propose a sentence embedding method that uses the inner product to compute semantic similarity between sentences. |
| Outcome: | The proposed method encodes sentences better in the sense of semantic structures. |
Knowledge Representation Learning with Contrastive Completion Coding (2021.findings-emnlp)
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| Challenge: | Existing knowledge representation learning methods suffer from immaturity on tackling potentially-imperfect knowledge graphs and highly-imbalanced positive-negative instances during training. |
| Approach: | They propose a framework for knowledge representation learning that incorporates two functional components to achieve robust embedding for each entity/relation. |
| Outcome: | The proposed framework achieves better convergence against state-of-the-art methods on several benchmarks. |
Knowledge-Enhanced Evidence Retrieval for Counterargument Generation (2021.findings-emnlp)
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| Challenge: | Existing models lack the reasoning abilities needed to find complex counterevidence. |
| Approach: | They propose a natural language inference model that finds counterevidence from diverse sources on the Web. |
| Outcome: | The proposed model outperforms baseline models for NLI tasks and finds complex counterevidence better. |
Investigating Numeracy Learning Ability of a Text-to-Text Transfer Model (2021.findings-emnlp)
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| Challenge: | Existing pre-trained models fail to capture a better understanding of numbers. |
| Approach: | They propose to use a text-to-text transfer learning model (T5) to learn numeracy in four numeration tasks. |
| Outcome: | The model outperforms its predecessors in four numeracy tasks, but struggle in extrapolation setting. |
Modeling Mathematical Notation Semantics in Academic Papers (2021.findings-emnlp)
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| Challenge: | Existing models that can predict mathematical notation are unable to analyze mathematical notations reliably. |
| Approach: | They propose two tasks that can be used to train a model that selectively masks notation tokens and encodes left and/or right sentences as context. |
| Outcome: | The proposed model performs better than baseline models trained by masked language modeling compared to baseline models, but is less accurate than token-level models . |
Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lens (2021.findings-emnlp)
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| Challenge: | Statistically significant results demonstrate that people with disabilities can be disadvantaged. |
| Approach: | They used a large-scale BERT language model to predict word predictions and found that people with disabilities can be disadvantaged. |
| Outcome: | The results show that people with disabilities can be disadvantaged and that gender and race identities can be discriminated against. |
Constructing Emotional Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation (2021.findings-emnlp)
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| Challenge: | Existing models for dialogue empathy focus on the emotion flow in one direction, from context to response. |
| Approach: | They propose a dual-generative model to construct emotional consensus and use unpaired data to produce pseudo paired empathetic samples. |
| Outcome: | The proposed model outperforms baseline models in producing coherent and empathetic responses. |
Automatic rule generation for time expression normalization (2021.findings-emnlp)
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| Challenge: | Existing SOTA methods for normalization rely on expert-designed rules or grammars . current methods are domain sensitive and not sufficient on emerging corpora . |
| Approach: | They propose a method that generates normalization rules from annotated data without expert intervention. |
| Outcome: | The proposed method surpasses existing rule-based methods on the Tweets benchmark and on the TempEval-3 benchmark. |
RW-KD: Sample-wise Loss Terms Re-Weighting for Knowledge Distillation (2021.findings-emnlp)
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| Challenge: | Knowledge Distillation (KD) is used to compress the pre-training and task-specific fine-tuning phases of large neural language models. |
| Approach: | They propose a sample-wise loss weighting method that re-weights the two losses for each sample. |
| Outcome: | The proposed method outperforms existing methods on 7 datasets of the GLUE benchmark. |
Visual Cues and Error Correction for Translation Robustness (2021.findings-emnlp)
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| Challenge: | Existing robustness techniques fail when faced with unseen types of noise and their performance degrades on clean texts. |
| Approach: | They propose visual context to improve translation robustness for noisy texts . they also propose an error correction training regime that can be used as an auxiliary task . |
| Outcome: | The proposed training regime improves translation robustness on noisy texts while maintaining translation quality on clean texts. |
Beyond the Tip of the Iceberg: Assessing Coherence of Text Classifiers (2021.findings-emnlp)
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| Challenge: | Large-scale, pre-trained language models achieve human-level and superhuman accuracy on existing language understanding tasks, but statistical bias in benchmark data and probing studies has recently called into question their true capabilities. |
| Approach: | They propose to evaluate systems through a measure of prediction coherence by using two existing language understanding benchmarks with different properties to demonstrate its versatility. |
| Outcome: | The proposed evaluation framework is quick, effective, and versatile to provide insight into the coherence of machines’ predictions. |
Does Pretraining for Summarization Require Knowledge Transfer? (2021.findings-emnlp)
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| Challenge: | Existing theories claim that pretraining models learn linguistic knowledge from the pretraining corpus, but scientific explanations for these benefits remain unknown. |
| Approach: | They propose to use random character n-grams to test models on real corpora to see if the small residual benefit of using real data could be accounted for by the structure of the pretraining task. |
| Outcome: | The proposed task performs on documents consisting of character n-grams, whereas pretrained models perform on real corpora with no residual benefit. |
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits (2021.findings-emnlp)
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| Challenge: | Training data for machine translation (MT) is often sourced from multiple large corpora that are multi-faceted in nature. |
| Approach: | They propose to optimize the balance between translationese and natural training data to relieve system developers from manual schedule design. |
| Outcome: | The proposed model relieves system developers from manual schedule design. |
Sometimes We Want Ungrammatical Translations (2021.findings-emnlp)
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| Challenge: | Recent advances in Neural Machine Translation (NMT) systems focus on improving translation quality and improving robustness to perturbations. |
| Approach: | They propose a way to quantify faithfulness to the original text by focusing on word-order perturbations. |
| Outcome: | The proposed method aims to measure faithfulness and robustness in word-order perturbations without deleting or injecting tokens. |
An animated picture says at least a thousand words: Selecting Gif-based Replies in Multimodal Dialog (2021.findings-emnlp)
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| Challenge: | Increasingly, image-based responses such as memes and animated gifs serve as culturally recognized and often humorous responses in conversation. |
| Approach: | They propose a multimodal conversational model for selecting gif responses from a text-gif conversation turn dataset and a randomized controlled trial. |
| Outcome: | The proposed model produces relevant and high-quality gif responses and is significantly better received by the community. |
SciCap: Generating Captions for Scientific Figures (2021.findings-emnlp)
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| Challenge: | Low-quality captions are common in scientific articles and can decrease understanding . this paper aims to develop an end-to-end neural framework to generate informative, high-quality figure captions for scientific figures and charts. |
| Approach: | They propose an end-to-end neural framework to automatically generate captions for scientific figures from a large-scale dataset . they used figure-type classification, sub-figure identification, text normalization, and caption text selection to build models that caption graph plots, the dominant figure type. |
| Outcome: | The proposed model can generate high-quality captions for scientific figures and charts from a large figure-caption dataset from arXiv. |
SentNoB: A Dataset for Analysing Sentiment on Noisy Bangla Texts (2021.findings-emnlp)
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| Challenge: | Bangla is the sixth most spoken language worldwide and the second Indo-Aryan language after Hindi. |
| Approach: | They propose an annotated sentiment analysis dataset made of informally written Bangla texts. |
| Outcome: | The proposed dataset is compared with neural networks and pretrained models . it shows that hand-crafted lexical features provide superior performance than neural networks . |
Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data (2021.findings-emnlp)
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| Challenge: | Existing methods to improve semantic parsing performance on target languages are limited. |
| Approach: | They propose a Translate-and-Fill method that produces silver training data for a multilingual semantic parser. |
| Outcome: | The proposed method produces silver training data for a multilingual parser. |
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application (2021.findings-emnlp)
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| Challenge: | Existing language models are pre-trained and distilled on general corpus like Wikipedia, which has gaps with the news domain and may be suboptimal for news intelligence. |
| Approach: | They propose a method to distill existing language models on Wikipedia to enable efficient news intelligence. |
| Outcome: | The proposed model can be used to build and test a news intelligence application on Wikipedia and Wikipedia. |
SD-QA: Spoken Dialectal Question Answering for the Real World (2021.findings-emnlp)
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| Challenge: | Existing QA benchmarks do not account for errors that speech recognition models might introduce . evaluating production-ready QA systems on data that is not representative of real-world inputs is problematic . |
| Approach: | They construct a multi-dialect, spoken QA benchmark on five languages with 68k audio prompts in 24 dialects from 255 speakers. |
| Outcome: | The proposed model is based on 68k audio prompts in 24 dialects from 255 speakers. |
The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation (2021.findings-emnlp)
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| Challenge: | Extending state-of-the-art language models to low-resource languages requires addressing what we call the low-Resource double bind. |
| Approach: | They propose a low-resource double bind to refer to the co-occurrence of data limitations and compute resource constraints. |
| Outcome: | The proposed model improves performance on frequent sentences but disparates on infrequent ones. |
Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence (2021.findings-emnlp)
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| Challenge: | Existing models for text segmentation use supervised and unsupervised learning to perform tasks such as text summarization and keyword extraction. |
| Approach: | They propose a transformer over transformer framework to perform neural text segmentation. |
| Outcome: | The proposed framework outperforms state-of-the-art models in terms of semantic coherence measure . bottom-level sentence encoders pre-trained on specific languages yield better performance . |
Self-Supervised Neural Topic Modeling (2021.findings-emnlp)
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| Challenge: | Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text. |
| Approach: | They propose a self-supervised neural topic model that learns a topic representation jointly from three co-occurring words and a document that the triple originates from. |
| Outcome: | The proposed model outperforms existing topic models in coherence metrics and document clustering accuracy. |
Coreference-aware Surprisal Predicts Brain Response (2021.findings-emnlp)
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| Challenge: | Existing studies have shown that coreference resolution is a key component of language processing and has been used to manipulate variables of interest. |
| Approach: | They propose to enable the parser to process subword information that might better approximate human morphological knowledge and extend evaluation of coreference effects from self-paced reading to human brain imaging data. |
| Outcome: | The proposed model enables the parser to process subword information that might better approximate human morphological knowledge and extends evaluation of coreference effects from self-paced reading to human brain imaging data. |
Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversation (2021.findings-emnlp)
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| Challenge: | generative models are less practical for building real-time conversation systems due to high latency and large memory footprint. |
| Approach: | They propose a method that preserves the efficiency of a retrieval model while leveraging the conversational ability of generative models. |
| Outcome: | The proposed method preserves the efficiency of a retrieval model while leveraging the conversational ability of generative models. |
Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability (2021.findings-emnlp)
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| Challenge: | Abuse on the Internet is an important societal problem of our time. |
| Approach: | They propose to use user and community information to enhance detection of abusive language . they propose to propose properties that an explainable method should aim to exhibit . |
| Outcome: | The proposed methods leverage user and community information to enhance detection of abusive language. |
Detecting Community Sensitive Norm Violations in Online Conversations (2021.findings-emnlp)
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Chan Young Park, Julia Mendelsohn, Karthik Radhakrishnan, Kinjal Jain, Tushar Kanakagiri, David Jurgens, Yulia Tsvetkov
| Challenge: | Existing efforts to identify unacceptable behavior have focused on toxicity as the sole form of community norm violation. |
| Approach: | They propose a dataset that focuses on a more complete spectrum of community norms and their violations in local conversational and global contexts. |
| Outcome: | The proposed model improves the detection of community norm violations in local conversational and global contexts. |
SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations (2021.findings-emnlp)
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| Challenge: | SupCL-Seq extends contrastive learning from computer vision to sequence classification tasks. |
| Approach: | They propose a supervised alternative to Masked Language Modeling (MLM) that extends contrastive learning to sequence optimization in NLP by altering the dropout mask probability in standard Transformer architectures. |
| Outcome: | The proposed method leads to large gains on the GLUE benchmark, including 6% absolute improvement on CoLA, 5.4% on MRPC, 4.7% on RTE and 2.6% on STS-B. |
mDAPT: Multilingual Domain Adaptive Pretraining in a Single Model (2021.findings-emnlp)
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| Challenge: | Existing domain-specific multilingual pretraining data is difficult to obtain due to regulations, legislation, or simply a lack of language- and domain- specific text. |
| Approach: | They propose to continue pretraining a language model on domain-specific unlabelled text . this allows for better modelling of text for downstream tasks within the domain . |
| Outcome: | The proposed approach outperforms the general multilingual model and performs close to its monolingual counterpart. |
COSMic: A Coherence-Aware Generation Metric for Image Descriptions (2021.findings-emnlp)
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| Challenge: | Existing methods to evaluate captions have limited learning of their output . previous methods focused on n-gram measures of similarity to reference output based on a ngram of similarities to the output metric. |
| Approach: | They propose a first discourse-aware learned generation metric for evaluating image descriptions. |
| Outcome: | The proposed metric predicts human ratings of captions on out-of-domain images. |
Relation-Guided Pre-Training for Open-Domain Question Answering (2021.findings-emnlp)
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| Challenge: | Existing QA datasets are imbalanced in some types of relations, which hurts generalization performance over long-tail questions. |
| Approach: | They propose a relation-guided pre-training framework to infer latent relations from a QA dataset . they then propose RGPT-QA to conduct extractive QA to get the target answer entity . |
| Outcome: | The proposed framework improves Exact Match accuracy on natural questions, TriviaQA, and WebQuestions. |
MURAL: Multimodal, Multitask Representations Across Languages (2021.findings-emnlp)
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Aashi Jain, Mandy Guo, Krishna Srinivasan, Ting Chen, Sneha Kudugunta, Chao Jia, Yinfei Yang, Jason Baldridge
| Challenge: | Image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages. |
| Approach: | They propose a dual encoder that integrates image-text matching and translation pairs to solve two tasks by learning from billions of pairs. |
| Outcome: | The proposed encoder outperforms ALIGN's cross-modal retrieval performance on well-resourced languages and significantly improves on under-resource languages. |
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing datasets are limited to providing the degree of idiomaticity of expressions along with the literal and, where applicable, (a single) non-literal interpretation of MWEs. |
| Approach: | They propose to use a dataset to test the effectiveness of a language model in generating representations of sentences containing idioms. |
| Outcome: | The proposed model performs reasonably well on the one-shot and few-shot scenarios, but there is scope for improvement in the zero-shot scenario. |
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration (2021.findings-emnlp)
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| Challenge: | Persuasion dialogue systems have long-standing problems of dialogue repetition and inconsistency which could impact user experience and impede the persuaded outcome. |
| Approach: | They propose to refine a language model baseline without user simulators and distill sentence-level information about repetition, inconsistency, and task relevance through rewards. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic metrics and human evaluation results on a donation persuasion task and generates more diverse, consistent and persuasive conversations according to user feedback. |
A Computational Exploration of Pejorative Language in Social Media (2021.findings-emnlp)
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| Challenge: | In this paper, we examine the problem of pejorative language, an under-explored topic in computational linguistics. |
| Approach: | They propose to automatically disambiguate pejorative usage in social media . they leverage online dictionaries to build a multilingual lexicon of pejorativ terms . |
| Outcome: | The proposed model can automatically disambiguate pejorative usage in social media posts . the proposed model is based on dictionaries and tweets . |
Evidence-based Fact-Checking of Health-related Claims (2021.findings-emnlp)
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| Challenge: | Existing evidence-based factchecking datasets contain synthetic claims and lack real-world verification. |
| Approach: | They propose a dataset for evidence-based fact-checking of health-related claims that evaluates their truthfulness against scientific articles. |
| Outcome: | The proposed dataset evaluates real-world claims against scientific articles. |
Learning and Analyzing Generation Order for Undirected Sequence Models (2021.findings-emnlp)
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| Challenge: | Undirected neural sequence models generate monotonically from left to right in machine translation tasks. |
| Approach: | They train a policy that learns the generation order for a pre-trained, undirected translation model via reinforcement learning. |
| Outcome: | The proposed policy outperforms heuristic generation orders on three out of four language pairs. |
Automatic Bilingual Markup Transfer (2021.findings-emnlp)
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| Challenge: | Existing work on markup transfer is performed with machine translation . a human translator generates the target translation without markup, and then the system infers the placement of markup tags. |
| Approach: | They propose two metrics and evaluate several approaches to bilingual markup transfer . best approach achieves an average accuracy of 94.7% across six language pairs . |
| Outcome: | The proposed approach achieves an average accuracy of 94.7% across six language pairs . it is a novel approach that can be applied to a structured document translation corpus . |
Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media (2021.findings-emnlp)
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| Challenge: | Adverse Events (AEs) are harmful events resulting from the use of medical products. |
| Approach: | They propose a model that combines sequence-to-sequence learning with language transfer capabilities to improve model robustness. |
| Outcome: | The proposed approach achieves strong performance over baselines on English benchmarks. |
Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders (2021.findings-emnlp)
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| Challenge: | Disentangled representation learning aims to provide an interpretable representation of latent features and a framework for controlling the change of specific features. |
| Approach: | They propose a Variational Autoencoder based method which models language features as discrete variables and encourages independence between variables for learning disentangled representations. |
| Outcome: | The proposed model outperforms baselines on several qualitative and quantitative benchmarks and on a text style transfer downstream application. |
MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding (2021.findings-emnlp)
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| Challenge: | Current knowledge distillation models are limited and lack performance on multimodal datasets. |
| Approach: | They propose a multimodal knowledge distillation framework to transfer knowledge from a teacher on multimodal tasks by learning the teacher's behavior within each modality. |
| Outcome: | The proposed framework achieves better performance than KD on four multimodal datasets. |
Do UD Trees Match Mention Spans in Coreference Annotations? (2021.findings-emnlp)
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| Challenge: | Existing methods to annotate mention spans are based on delimiting token intervals, but there is no syntactic representation of the mention span. |
| Approach: | They propose to integrate coreference annotation with syntactic annotation to make them convergent in the long term. |
| Outcome: | The proposed approach could be advantageous in the long term, the authors argue. |
Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference (2021.findings-emnlp)
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Sneha Kudugunta, Yanping Huang, Ankur Bapna, Maxim Krikun, Dmitry Lepikhin, Minh-Thang Luong, Orhan Firat
| Challenge: | Sparse Mixture-of-Experts (MoE) is a successful approach for scaling multilingual translation models to billions of parameters without a proportional increase in training computation. |
| Approach: | They propose to use a task-level routing approach to extract smaller, ready-to-deploy sub-networks from large sparse models by ignoring distillation. |
| Outcome: | Experiments on WMT and a web-scale dataset show that task-level routing outperforms token-level MoE models by +1.0 BLEU on average across 30 language pairs. |
TAG: Gradient Attack on Transformer-based Language Models (2021.findings-emnlp)
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Jieren Deng, Yijue Wang, Ji Li, Chenghong Wang, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, Caiwen Ding
| Challenge: | Recent studies show that publicly shared gradients in the training process can reveal the private training data to a third-party. |
| Approach: | They propose a gradient attack algorithm to reconstruct the local training data using GLUE benchmarks. |
| Outcome: | The proposed algorithm achieves 1.5x recover rate and 2.5x ROUGE-2 over previous methods without the need of ground truth label. |
Generating Realistic Natural Language Counterfactuals (2021.findings-emnlp)
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| Challenge: | Existing methods to explain ML tasks for natural language text are either unrealistic or introduce imperceptible changes. |
| Approach: | They propose a method that combines a conditional GAN and embeddings of a pretrained BERT encoder to model-agnostically generate realistic natural language text counterfactuals for explaining regression and classification tasks. |
| Outcome: | The proposed method outperforms baseline methods on fidelity and human judgments of naturalness across multiple datasets and multiple predictive models. |
Unsupervised Chunking as Syntactic Structure Induction with a Knowledge-Transfer Approach (2021.findings-emnlp)
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| Challenge: | Existing methods for predicting linguistic structures require labeled data . unsupervised chunking is useful for understanding linguistic structure of human languages . |
| Approach: | They propose a knowledge-transfer approach that heuristically induces chunk labels from unsupervised parsing models and a hierarchical recurrent neural network (HRNN) they show that their approach bridges the gap between supervised and unsupervised chunking. |
| Outcome: | The proposed method bridges the gap between supervised and unsupervised chunking. |
Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects (2021.findings-emnlp)
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| Challenge: | a popular approach to decompose the neural bases of language requires large and costly data sets to obtain. |
| Approach: | They propose a model-based approach to decompose the neural bases of language that can be used to correlate brain responses to different stimuli. |
| Outcome: | The proposed model-based approach replicates the seminal study of Lerner et al. (2011), which revealed the hierarchy of language areas by comparing the functional-magnetic resonance imaging (fMRI) of seven subjects listening to 7min of both regular and scrambled narratives. |
Gated Transformer for Robust De-noised Sequence-to-Sequence Modelling (2021.findings-emnlp)
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| Challenge: | Noisy texts are common in user-generated texts that appear abundant in social media platforms like SMS, online chat, email, blogs, wikis etc. |
| Approach: | They propose a sequence-to-sequence architecture that uses a gating mechanism to detect types of corrections required from English texts. |
| Outcome: | The proposed architecture performs better than non-gated models on machine translation and Summarization tasks. |
Token-wise Curriculum Learning for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing curriculum learning approaches to Neural Machine Translation (NMT) require sampling sufficient amounts of “easy” samples from training data at the early stage of training. |
| Approach: | They propose a token-wise curriculum learning approach that creates sufficient amounts of easy samples from training data. |
| Outcome: | The proposed approach outperforms baselines on five language pairs on low-resource languages. |
RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification (2021.findings-emnlp)
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| Challenge: | Existing relationships between entities can be reliable indicators for classifying sensitive information, such as commercially sensitive information. |
| Approach: | They propose to represent entities and relations within a single embedding to better capture the relationship between the entities. |
| Outcome: | The proposed method significantly improves the effectiveness of sensitivity classification compared to existing methods. |
Post-Editing Extractive Summaries by Definiteness Prediction (2021.findings-emnlp)
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| Challenge: | Abstract: Extractive summarization has been the mainstay of automatic summarizing for decades, but it still suffers from coreference issues arising from extracting sentences away from their original context. |
| Approach: | They propose a post-editing step that generates linguistic decisions that lead to improved extractive summaries by predicting definiteness of noun phrases. |
| Outcome: | The proposed system generates linguistic decisions that improve the quality of the extractive summaries. |
Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations (2021.findings-emnlp)
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Longxiang Zhang, Renato Negrinho, Arindam Ghosh, Vasudevan Jagannathan, Hamid Reza Hassanzadeh, Thomas Schaaf, Matthew R. Gormley
| Challenge: | Using pretrained transformer models for automatically summarizing doctor-patient conversations presents challenges . limited training data, domain shift, long and noisy transcripts, and high target summary variability are challenges compared to human annotators. |
| Approach: | They propose a method for fine-tuning pretrained transformer models for automatically summarizing doctor-patient conversations directly from transcripts. |
| Outcome: | The proposed method surpasses the performance of an average human annotator and the quality of previous published work for the task. |
Distilling Knowledge for Empathy Detection (2021.findings-emnlp)
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| Challenge: | Empathy is the link between self and others. |
| Approach: | They employ multi-task training with knowledge distillation to integrate knowledge from available resources to detect empathy from the natural language in different domains. |
| Outcome: | The proposed approach yields better results on an existing news-related empathy dataset compared to strong baselines. |
Adapting Entities across Languages and Cultures (2021.findings-emnlp)
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| Challenge: | a structured knowledge base adapts named entities using their shared properties. |
| Approach: | They propose automatic methods to adapt named entities using shared properties . they compare them to human adaptations using a new dataset of human adaptation data . |
| Outcome: | The proposed methods compare to human adaptations using a new dataset. |
ODIST: Open World Classification via Distributionally Shifted Instances (2021.findings-emnlp)
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| Challenge: | Existing work to achieve open-world classification capability in natural language processing and computer vision focuses on decision boundary finding. |
| Approach: | They propose a method that can create out-of-domain instances from in-domain training instances with the help of a pre-trained generative language model. |
| Outcome: | The proposed method can create out-of-domain instances from the in-domain training instances with the help of a pre-trained generative language model. |
LAMAD: A Linguistic Attentional Model for Arabic Text Diacritization (2021.findings-emnlp)
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| Challenge: | In Arabic, diacritics are often omitted from written texts increasing the number of possible meanings and pronunciations. |
| Approach: | They propose a linguistic attentional model for Arabic text diacritization which captures key linguistic features from Arabic text. |
| Outcome: | The proposed model outperforms existing state-of-the-art models on three datasets with different sizes. |
Sequence-to-Lattice Models for Fast Translation (2021.findings-emnlp)
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| Challenge: | Existing non-autoregressive machine translation models have decoders that are difficult to port to NAT models. |
| Approach: | They propose a sequence-to-lattice model that replaces the decoder with a search lattice. |
| Outcome: | The proposed model is faster than past non-autoregressive generation approaches and more accurate than reducing the number of decoder layers. |
Towards Realistic Single-Task Continuous Learning Research for NER (2021.findings-emnlp)
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Justin Payan, Yuval Merhav, He Xie, Satyapriya Krishna, Anil Ramakrishna, Mukund Sridhar, Rahul Gupta
| Challenge: | Academic datasets are often static and contain data that is annotated all at once based on fixed annotation guidelines. |
| Approach: | They propose to build a single-task continuous learning dataset from an existing dataset and release it along with the code to the research community. |
| Outcome: | The proposed model is based on an existing dataset and released to the research community. |
Retrieval Augmentation Reduces Hallucination in Conversation (2021.findings-emnlp)
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| Challenge: | State-of-the-art dialogue models suffer from factual incorrectness and hallucination of knowledge. |
| Approach: | They propose to use neural-retrieval-in-the-loop architectures to optimize knowledge-grounded dialogue by retrieving, ranking, and encoder-decoders. |
| Outcome: | The proposed architectures exhibit open-domain conversational capabilities and generalize effectively to scenarios not within the training data. |
Towards Automatic Bias Detection in Knowledge Graphs (2021.findings-emnlp)
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| Challenge: | Recent studies have shown that knowledge graphs are prone to various social biases, and have proposed multiple methods for debiasing them. |
| Approach: | They propose a framework for identifying biases present in knowledge graph embeddings based on numerical bias metrics. |
| Outcome: | The proposed framework can be extended to further bias definitions and applications. |
Searching for More Efficient Dynamic Programs (2021.findings-emnlp)
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| Challenge: | a combinatorial model of human language often involves dynamic programming. |
| Approach: | They propose to search for a sequence of semantics-preserving transformations to improve the initial program's running time. |
| Outcome: | The proposed algorithm can find speed-ups in the initial program, and it can be used to improve it. |
Revisiting Robust Neural Machine Translation: A Transformer Case Study (2021.findings-emnlp)
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| Challenge: | Recent advances in NMT have shown promising results but are vulnerable to noise. |
| Approach: | They propose a data-driven technique called Target Augmented Fine-tuning to incorporate noise during training. |
| Outcome: | The proposed techniques perform with no degradation where up to 10% of entire test words are infected by noise. |
Can NLI Models Verify QA Systems’ Predictions? (2021.findings-emnlp)
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| Challenge: | Recent question answering systems perform well on benchmark datasets, but are not always well-calibrated to spot spurious answers under distribution shifts. |
| Approach: | They propose to use natural language inference to verify whether answers are correct . they leverage large pre-trained models and recent prior datasets to construct powerful question conversion and decontextualization modules. |
| Outcome: | The proposed approach improves the confidence estimation of a QA model across different domains, evaluated in a selective QA setting. |
Parameter-Efficient Domain Knowledge Integration from Multiple Sources for Biomedical Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing domain-specific pre-trained language models (PLMs) rely on self-supervised learning over large amounts of domain text, without explicitly integrating domain- specific knowledge. |
| Approach: | They propose to integrate domain knowledge from diverse sources into PLMs by using adapters that are pre-trained for individual domain knowledge sources and integrated via an attention-based knowledge controller. |
| Outcome: | The proposed architecture integrates domain knowledge from diverse sources into PLMs in a parameter-efficient way. |
Uncovering Implicit Gender Bias in Narratives through Commonsense Inference (2021.findings-emnlp)
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| Challenge: | Pre-trained language models learn harmful biases from their training corpora and may repeat these biase if used for generation. |
| Approach: | They focus on gender biases associated with the protagonist in model-generated stories and use a commonsense reasoning engine to uncover them. |
| Outcome: | The proposed model-generated stories are based on a commonsense reasoning engine and are able to uncover gender biases in the protagonist's motivations, attributes, mental states, and implications on others. |
Contrastive Document Representation Learning with Graph Attention Networks (2021.findings-emnlp)
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| Challenge: | Existing methods for document representation learning are significantly affected by the scarcity of document-level data. |
| Approach: | They propose to use a graph attention network on top of the available pretrained Transformers model to learn document embeddings. |
| Outcome: | Empirically, the proposed approach is effective in document classification and document retrieval tasks. |
Convex Aggregation for Opinion Summarization (2021.findings-emnlp)
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| Challenge: | Recent advances in text autoencoders have significantly improved the quality of the latent space, allowing models to generate consistent text from aggregated latent vectors. |
| Approach: | They develop a framework which searches input-output word overlap for latent vector aggregation. |
| Outcome: | The proposed framework improves the quality of the latent space and establishes state-of-the-art performance on two opinion summarization benchmarks. |
Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings (2021.findings-emnlp)
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| Challenge: | Recent studies suggest different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces. |
| Approach: | They propose to use Optimal Transport as an alignment objective during fine-tuning to improve multilingual contextualized representations for downstream cross-lingual transfer. |
| Outcome: | The proposed method achieves better performance on two tasks (XNLI and XQuAD) and is competitive with existing methods. |
Uncertainty-Aware Machine Translation Evaluation (2021.findings-emnlp)
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| Challenge: | Several neural-based metrics have been proposed to evaluate machine translation quality, but they are trained on noisy, biased and scarce human judgements. |
| Approach: | They propose a method to evaluate machine translation quality using point estimates . they combine COMET framework with Monte Carlo dropout and deep ensembles . |
| Outcome: | The proposed methods perform well across multiple language pairs and with references. |
Neural Unification for Logic Reasoning over Natural Language (2021.findings-emnlp)
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| Challenge: | Automated Theorem Proving (ATP) is a computer program that can show that conjectures are logical consequences of a set of axioms. |
| Approach: | They propose a transformer-based architecture for deriving conjectures given axioms . they propose 'neural unifier' and relative training procedure to train the model . |
| Outcome: | The proposed architectures are able to answer queries with deep queries with a relatively low training time. |
From None to Severe: Predicting Severity in Movie Scripts (2021.findings-emnlp)
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| Challenge: | Existing rating systems only provide simple age restrictions and do not include suitability level on a specific aspect of the content. |
| Approach: | They propose to categorize ordinal severity of movies on 5 aspects using dialogue script data . they propose to use a siamese network-based multitask framework to improve interpretability . |
| Outcome: | The proposed method outperforms the state-of-the-art model and provides useful information to interpret predictions. |
Benchmarking Meta-embeddings: What Works and What Does Not (2021.findings-emnlp)
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| Challenge: | Existing methods to build meta-embeddings have been evaluated using a variety of methods and datasets, which makes it difficult to draw meaningful conclusions regarding the merits of each approach. |
| Approach: | They propose a unified framework for a fair and objective meta-embedding evaluation using intrinsic and extrinsic tasks. |
| Outcome: | The proposed framework outperforms existing methods on intrinsic and extrinsic evaluation benchmarks and outperformed existing methods. |
A Plug-and-Play Method for Controlled Text Generation (2021.findings-emnlp)
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| Challenge: | Existing methods for controlling language generation are not able to produce fluent text . current methods require additional models or fine-tuning to ensure specific words are included . |
| Approach: | They propose a plug-and-play decoding method that allows for controlled language generation . they add a shift in the probability distribution over our vocabulary towards semantically similar words . |
| Outcome: | The proposed method outperforms competing methods in human evaluations and does not impact fluency. |
A Corpus-based Syntactic Analysis of Two-termed Unlike Coordination (2021.findings-emnlp)
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| Challenge: | a phenomenon of language that conjoins two or more terms or phrases using a coordinating conjunction is still largely elusive and widely debated amongst linguists. |
| Approach: | They propose to use a computational corpus-based approach to study two-termed unlike coordinations where the two conjuncts of the coordination phrase form valid constituents but have distinct categories. |
| Outcome: | The proposed analysis shows that the two conjuncts within unlike coordinations display different properties based on their position, supporting an antisymmetric view of the structure of coordination. |
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation (2021.findings-emnlp)
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| Challenge: | Radiology report generation aims at generating descriptive text from radiology images automatically. |
| Approach: | They propose a weakly supervised contrastive loss method that generates descriptive text from radiology images automatically. |
| Outcome: | The proposed method outperforms previous work on correctness and text generation metrics for two public benchmarks. |
NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions (2021.findings-emnlp)
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| Challenge: | Existing conversational systems are agent-centric, which assumes the user utterances will closely follow the system ontology. |
| Approach: | They build a dataset that maps user preferences to an ontology and model user preferences as estimated distributions over the system ontologies. |
| Outcome: | The proposed system can be used to push existing research from agent-centric to user-centric. |
Table-based Fact Verification With Salience-aware Learning (2021.findings-emnlp)
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| Challenge: | Existing methods for fact verification use tabular data with tokens, but training requires labeled training data. |
| Approach: | They propose a system that identifies token-level salience in the statement with probing-based saliency estimation. |
| Outcome: | The proposed system improves on TabFact benchmark by replacing non-salient terms with tokens. |
Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage (2021.findings-emnlp)
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Isidora Tourni, Lei Guo, Taufiq Husada Daryanto, Fabian Zhafransyah, Edward Edberg Halim, Mona Jalal, Boqi Chen, Sha Lai, Hengchang Hu, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
| Challenge: | Journalists have been using both text and images to frame news stories . lead images may carry additional background knowledge about the event . |
| Approach: | They find that combining lead images and contextual information with text improves news framing . they release the first multimodal news framming dataset related to gun violence in the u.s. |
| Outcome: | The study shows that combining lead images with text improves prediction of news frames . it also shows that using multiple modes of information improves frame image relevance . |
Multi-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event (2021.findings-emnlp)
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| Challenge: | Social media is a platform for people to share their concerns and report information as eyewitnesses of events. |
| Approach: | They propose a multi-task learning approach to leverage available annotated data for several related tasks from the crisis domain to improve performance on a main task with limited annotation. |
| Outcome: | The proposed approach improves performance on a task with limited annotated data. |
Graph-Based Decoding for Task Oriented Semantic Parsing (2021.findings-emnlp)
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| Challenge: | Existing paradigms for semantic parsing are sequence-to-sequence and AMR parsers. |
| Approach: | They propose to formulate parsing as a sequence-to-sequence task using graph-based decoding techniques developed for syntactic parsers. |
| Outcome: | The proposed approach is competitive with sequence decoders on the standard setting and offers significant improvements in data efficiency and data availability. |
Expected Validation Performance and Estimation of a Random Variable’s Maximum (2021.findings-emnlp)
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| Challenge: | In this paper, we analyze three statistical estimators for expected validation performance . Often researchers only report the performance of the best-found model during a hyperparameter search . |
| Approach: | They analyze three estimators for expected validation performance to compare models . they find that the estimator with the smallest variance has the largest bias . |
| Outcome: | The proposed model has the highest variance and the estimator with the smallest variance has the largest bias. |
How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks (2021.findings-emnlp)
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| Challenge: | Recent studies have focused on rule-based and neural sequence-to-sequence (seq2sequ) TS is a technique that reduces text complexity for human consumption. |
| Approach: | They evaluate two possible uses of neural TS: simplifying input texts at prediction time and augmenting training data to provide machines with additional information during training. |
| Outcome: | The proposed approach improves performance on two datasets. |
Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers (2021.findings-emnlp)
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| Challenge: | Recent improvements in NLP tasks can be attributed to the Transformer model. |
| Approach: | They propose to use parameter-sharing methods to reduce parameter budgets in generative models by using sandwich-style parameter sharing and self-attentive embedding factorization. |
| Outcome: | The proposed model outperforms the current RNN model even with significantly fewer parameters. |
Leveraging Information Bottleneck for Scientific Document Summarization (2021.findings-emnlp)
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| Challenge: | Existing methods to extract salient sentences from document are unsupervised and rely on graph-based methods for sentence ranking. |
| Approach: | They propose an unsupervised extractive approach to document level summarization based on the Information Bottleneck principle. |
| Outcome: | The proposed framework can be extended to a multi-view framework by different signals. |
Reconsidering the Past: Optimizing Hidden States in Language Models (2021.findings-emnlp)
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| Challenge: | In this paper, we present Hidden-State Optimization (HSO) for language models at inference time. |
| Approach: | They propose a method that uses the log-probability gradient to update hidden states rather than the model parameters to improve the performance of transformer language models. |
| Outcome: | The proposed method improves performance of transformer language models at inference time. |
Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots (2021.findings-emnlp)
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| Challenge: | Few-shot table-to-text generation is a task of composing fluent and faithful sentences to convey table content using limited data. |
| Approach: | They propose a novel approach to generate faithful table-to-text sentences using limited data . they aim to exploit table structure and natural linguistic information to generate accurate sentences . |
| Outcome: | The proposed approach generates higher qualified sentences when compared with state-of-the-art models on humans, songs, and books. |
ARCH: Efficient Adversarial Regularized Training with Caching (2021.findings-emnlp)
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Simiao Zuo, Chen Liang, Haoming Jiang, Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Tuo Zhao
| Challenge: | Existing approaches to regularize models require generating a perturbation for each sample in each epoch. |
| Approach: | They propose an adversarial regularization method where perturbations are generated and cached once every several epochs. |
| Outcome: | The proposed method significantly eases the computational burden (saves up to 70% of computational time) it produces a notably better (in most of the tasks) or comparable model generalization. |
Probing Commonsense Explanation in Dialogue Response Generation (2021.findings-emnlp)
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| Challenge: | Currently, response generation (RG) models do not understand human communication intents. |
| Approach: | They propose to examine commonsense reasoning implicitly to determine whether RG models produce coherent responses in conversations. |
| Outcome: | The proposed probing settings show that RG models fail to capture the logical relations between commonsense explanations and responses and fine-tuning on in-domain data do not lead to understanding of CSR for RG. |
NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset (2021.findings-emnlp)
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| Challenge: | Existing question answering datasets lack numerical reasoning and reasoning processes . current research on numerical reasoning focuses on simple calculations . |
| Approach: | They propose a conversational and bilingual question answering dataset with numerical reasoning with compound mathematical expressions. |
| Outcome: | The proposed model achieves 55.5 exact match scores while human performance is 89.7. |
Textual Time Travel: A Temporally Informed Approach to Theory of Mind (2021.findings-emnlp)
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| Challenge: | Existing models that attribute mental states to oneself and others perform poorly on false belief tasks where beliefs differ from reality. |
| Approach: | They propose a temporally informed approach for improving the theory of mind capability of memory-augmented neural models by integrating priors about entities’ minds and tracking their mental states over time through an extended passage. |
| Outcome: | The proposed model improves performance on false belief tasks where beliefs differ from reality, especially when the dataset contains distracting sentences. |
Detect and Perturb: Neutral Rewriting of Biased and Sensitive Text via Gradient-based Decoding (2021.findings-emnlp)
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| Challenge: | Written language carries explicit and implicit biases that can distract from meaningful signals; at worst they can lead to unfair outcomes. |
| Approach: | They propose a gradient-based rewriting framework that detects and perturbs sensitive components and regenerates fluent alternatives that are neutral in the sensitive attribute while maintaining the semantics of other attributes. |
| Outcome: | The proposed framework regenerates fluent alternatives that are neutral in the sensitive attribute while maintaining the semantics of other attributes. |
HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning (2021.findings-emnlp)
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| Challenge: | Existing taxonomies have limited coverage due to expensive manual curation process. |
| Approach: | They propose an algorithm that expands existing taxonomies to preserve their structure in a more expressive hyperbolic embedding space and learns to represent concepts and their relations with a hyperbolical Graph Neural Network. |
| Outcome: | The proposed algorithm outperforms baseline models with representation learning in a Euclidean feature space and achieves state-of-the-art performance on the taxonomy expansion benchmarks. |
Want To Reduce Labeling Cost? GPT-3 Can Help (2021.findings-emnlp)
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| Challenge: | Data annotation is labor-intensive and time-consuming for many NLP tasks. |
| Approach: | They propose to use GPT-3 to train models which are deployed for inference . they propose to combine pseudo labels from GPT3 with human labels . |
| Outcome: | The proposed method can be generalizable to many practical applications. |
Written Justifications are Key to Aggregate Crowdsourced Forecasts (2021.findings-emnlp)
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| Challenge: | aggregating crowdsourced forecasts benefits from modeling written justifications . a majority of respondents support the idea that crowds are more reliable than experts . |
| Approach: | They propose to model written justifications for crowdsourced questions by analyzing their results in a literature review. |
| Outcome: | The results show that the written justifications are beneficial to call a question throughout its life except in the last quarter. |
Cleaning Dirty Books: Post-OCR Processing for Previously Scanned Texts (2021.findings-emnlp)
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| Challenge: | a large amount of work is required to clean digitized books for NLP analysis because of errors in the scanned text and duplicate volumes in the corpora. |
| Approach: | They propose methods to handle optical character recognition errors in scanned texts . they identify the canonical version for each of 17,136 repeatedly-scanned books . |
| Outcome: | The proposed method corrects over six times as many errors as it introduces, the authors show . the authors evaluate a collection of 19,347 texts from the Gutenberg dataset and 96,635 from the HathiTrust Library . |
Bag of Tricks for Optimizing Transformer Efficiency (2021.findings-emnlp)
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| Challenge: | Improving Transformer efficiency has become increasingly attractive in recent years. |
| Approach: | They propose to combine pruning, quantization, new architectures and training strategies to improve Transformer efficiency. |
| Outcome: | The proposed methods improve the inference efficiency of a strong Transformer system by 3.80x on CPU and 2.52x on GPU. |
Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | kNN-MT is a non-parametric method that uses nearest neighbor retrieval to translate out-of-domain sentences, rare words, etc. |
| Approach: | They propose a framework that directly uses in-domain monolingual sentences to build an effective datastore for k-nearest-neighbor retrieval. |
| Outcome: | The proposed framework improves translation accuracy with target-side monolingual data while achieving comparable performance with back-translation. |
The Topic Confusion Task: A Novel Evaluation Scenario for Authorship Attribution (2021.findings-emnlp)
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| Challenge: | Autorship attribution is the problem of identifying the most plausible author of an anonymous text from a set of candidate authors. |
| Approach: | They propose a topic confusion task where they switch the author-topic configuration between training and testing sets and propose attribution errors that are caused by the topic shift and by the features’ inability to capture the writing styles. |
| Outcome: | The proposed task combines author-topic configuration with other features to lower topic confusion and higher attribution accuracy. |
Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health (2021.findings-emnlp)
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| Challenge: | Existing statistical models are not explainable, struggle in low-resource scenarios and cannot be reused for multiple tasks. |
| Approach: | They propose a micromodel architecture that embeds domain knowledge and provides explanations throughout the model’s decision process. |
| Outcome: | The proposed model is validated on depression classification, PTSD classification, and suicidal risk assessment tasks. |
Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Understanding written laws is difficult because the abstract rules must account for a variety of situations, even those not yet encountered. |
| Approach: | They constructed a dataset of 26,959 sentences and labeled them in terms of their usefulness for explaining selected legal concepts. |
| Outcome: | The proposed models outperform the prior approaches and can learn surprisingly sophisticated features. |
FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning (2021.findings-emnlp)
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| Challenge: | Existing neural dialogue models only capture syntactic and semantic information, but fail to model the logical consistency between the dialogue history and the generated response. |
| Approach: | They propose a fine-grained comparison model to capture syntactic and semantic information and then compare each candidate's representation with the whole history to obtain a history consistency representation. |
| Outcome: | The proposed model obtains higher ranking scores than baseline models on two public dialogue datasets. |
Reference-based Weak Supervision for Answer Sentence Selection using Web Data (2021.findings-emnlp)
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| Challenge: | Existing solutions for QA use weakly-supervised data from Web. |
| Approach: | They propose a data pipeline that harvests weakly-supervised answer sentences from Web data . they train TANDA models, which are the state of the art for AS2 . |
| Outcome: | The proposed pipeline improves on three different datasets and sets the state-of-the-art models to P@1=90.1% and MAP=92.9%. |
A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation (2021.findings-emnlp)
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| Challenge: | Recent advances in disentanglement work on coarse levels in the disenanglement of closely related properties, such as syntax and semantics in human languages. |
| Approach: | They propose a deep decomposable model based on VAE to disentangle syntax and semantics by using total correlation penalties on KL divergences. |
| Outcome: | The proposed model significantly improves the disentanglement quality between syntactic and semantic representations for semantic similarity tasks and syntaktic similarity task. |
Improved Word Sense Disambiguation with Enhanced Sense Representations (2021.findings-emnlp)
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| Challenge: | Existing supervised word sense disambiguation systems do not provide enough information about word senses. |
| Approach: | They propose to incorporate synonyms, example phrases or sentences showing usage of word senses and sense gloss of hypernyms into the sense representations. |
| Outcome: | The proposed system achieves an F1 score of 82.0% on the standard benchmark test dataset of the English all-words WSD task. |
Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent Variables (2021.findings-emnlp)
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| Challenge: | Existing methods to achieve zero-shot translation suffer from spurious correlations between output language and language invariant semantics. |
| Approach: | They propose a method that denoizes the autoencoder objective based on pivot language into traditional training objective to improve translation accuracy on zero-shot directions. |
| Outcome: | The proposed method eliminates spurious correlations and outperforms state-of-the-art methods on two benchmark machine translation datasets. |
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition (2021.findings-emnlp)
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Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Wenjie Liu, Linquan Liu, Xiang-Yang Li, Tao Qin, Edward Lin, Tie-Yan Liu
| Challenge: | Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence. |
| Approach: | They propose a fast correction model that takes multiple ASR candidates as input for better correction accuracy. |
| Outcome: | The proposed model can reduce the word error rate (WER) with multiple candidates by 3.2% and 2.6%. |
Task-Oriented Clustering for Dialogues (2021.findings-emnlp)
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| Challenge: | Existing methods for task-oriented dialogue clustering are difficult to apply directly due to inherent differences between them. |
| Approach: | They propose a Dialogue Task Clustering Network model for task-oriented clustering . they use context-aware utterance representations and cross-dialogue utterrance cluster representations . |
| Outcome: | The proposed model outperforms baselines on three public datasets on all metrics. |
Mitigating Data Poisoning in Text Classification with Differential Privacy (2021.findings-emnlp)
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| Challenge: | Data poisoning attacks can plant a backdoor in a model by injecting poisoned examples into training data, causing the model to misclassify test instances which include a specific pattern. |
| Approach: | They propose a generic defence mechanism that makes training robust to poisoning attacks by smoothing the gradient from each training example. |
| Outcome: | The proposed method is highly effective in mitigating, or even eliminating, poisoning attacks on text classification, with only a small cost in predictive accuracy. |
Does Vision-and-Language Pretraining Improve Lexical Grounding? (2021.findings-emnlp)
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| Challenge: | Large pretrained language models (LMs) have been criticized for lack of grounding, i.e., connecting words to their meanings in the physical world. |
| Approach: | They compare vision-and-language (VL) models trained jointly on text and image or video data to find out how they compare to text-only counterparts. |
| Outcome: | The proposed model outperforms the text-only variants on a commonsense question answering task. |
Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages (2021.findings-emnlp)
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| Challenge: | Existing models for syntactic acquisition are word-based and do not inspect functional affixes. |
| Approach: | They propose a computer-based induction model that allows a clean ablation of the influence of subword information in grammar induction. |
| Outcome: | The proposed model is more accurate in morphologically richer languages with subword information than word-based models. |
Block-wise Word Embedding Compression Revisited: Better Weighting and Structuring (2021.findings-emnlp)
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| Challenge: | Existing methods for word embedding compression are limited . word embeds have a considerable size and need to be compressed to deploy on edge devices . |
| Approach: | They propose a block-wise low-rank approximation method for word embedding called GroupReduce . they propose 'frequency-inverse document frequency method' and a differentiable method for weighting . |
| Outcome: | The proposed algorithm more effectively finds word weights than competitors in most cases. |
Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching (2021.findings-emnlp)
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| Challenge: | Code-switching (CS) is a phenomenon of switching between multiple languages . current models cannot handle CS due to lack of annotated data and limited resources. |
| Approach: | They propose a self-training method to repurpose existing models using a switch-point bias by leveraging unannotated data to reduce the gap between the switch point performance and retain overall performance on two distinct language pairs. |
| Outcome: | The proposed model reduces the gap between the switch point performance while retaining the overall performance on two distinct language pairs. |
Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates (2021.findings-emnlp)
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| Challenge: | Existing approaches to interpret black-box models to learn spurious correlations are not well understood. |
| Approach: | They propose a procedure that leverages model interpretations to update parameters towards a plausible interpretation rather than an interpretation that relies on spurious patterns in data. |
| Outcome: | The proposed procedure outperforms baseline methods that use adversarial training in a controlled setup. |
Learning Task Sampling Policy for Multitask Learning (2021.findings-emnlp)
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| Challenge: | Existing methods to train multi-task models with auxiliary tasks are limited by the number of combinations and the importance of each auxiliary task is not known a priori. |
| Approach: | They propose a search method that automatically assigns importance weights to auxiliary tasks to improve the target task quality. |
| Outcome: | The proposed method outperforms uniform sampling and the corresponding single-task baseline on XNLI and GLUE. |
Competing Independent Modules for Knowledge Integration and Optimization (2021.findings-emnlp)
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| Challenge: | Existing knowledge sources are not available for sentiment analysis, but are used for many tasks. |
| Approach: | They propose a framework of untied independent modules for integrating off the shelf knowledge sources such as language models, lexica, POS information, and dependency relations. |
| Outcome: | The proposed framework is suitable for optimizing BERT-like language models even without external knowledge sources. |
An Exploratory Study on Long Dialogue Summarization: What Works and What’s Next (2021.findings-emnlp)
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Yusen Zhang, Ansong Ni, Tao Yu, Rui Zhang, Chenguang Zhu, Budhaditya Deb, Asli Celikyilmaz, Ahmed Hassan Awadallah, Dragomir Radev
| Challenge: | Existing models for dialogue summarization focus on extracting the main events of short conversations, but real-world dialogues are difficult to train. |
| Approach: | They propose three strategies to deal with the lengthy input problem and locate relevant information using long dialogue datasets. |
| Outcome: | The retrieve-then-summarize pipeline models yield the best performance on three long dialogue datasets. |
Improving Text Auto-Completion with Next Phrase Prediction (2021.findings-emnlp)
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| Challenge: | Language models such as GPT-2 require considerable training effort to adapt to specific writing domains (e.g., medical). |
| Approach: | They propose an intermediate training strategy that encourages language models to complete partial queries with enriched phrases and eventually improve their text auto-completion performance. |
| Outcome: | The proposed approach outperforms baselines in auto-completion tasks for email and academic-writing domains with only around 1.2B tokens. |
MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets (2021.findings-emnlp)
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Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
| Challenge: | a growing number of harmful memes are being used for trolling, cyberbullying and abuse . a new approach to detect harmful meme images and texts is emerging . |
| Approach: | They propose a multimodal deep neural network that detects harmful memes . they extend the recently released HarMeme dataset with additional memes and a new topic . |
| Outcome: | The proposed framework outperforms rival methods in detecting harmful memes and their target social entities. |
NICE: Neural Image Commenting with Empathy (2021.findings-emnlp)
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Kezhen Chen, Qiuyuan Huang, Daniel McDuff, Xiang Gao, Hamid Palangi, Jianfeng Wang, Kenneth Forbus, Jianfeng Gao
| Challenge: | Emotion and empathy are examples of human qualities lacking in many human-machine interactions. |
| Approach: | They propose to generate images with human-generated comments with enhanced emotion and empathy while minimizing inappropriate or offensive outputs. |
| Outcome: | The proposed model generates more human-like and engaging image comments on two images with human-generated comments and human annotations while minimizing inappropriate or offensive outputs. |
HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks (2021.findings-emnlp)
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| Challenge: | In computational notebooks, one documentation in a markdown cell often corresponds to multiple code cells, and these code cells have an inherent structure. |
| Approach: | They propose a new task of code documentation generation for computational notebooks that uses hierarchical attention mechanism to consider code cells and code tokens information when generating documentation. |
| Outcome: | The proposed model outperforms baseline models on a corpus constructed from well-documented Kaggle notebooks. |
A multilabel approach to morphosyntactic probing (2021.findings-emnlp)
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| Challenge: | Morphologically rich languages present unique challenges to natural language processing . morphological supervision can improve the quality of multilingual language models . |
| Approach: | They propose a multilabel probing task to assess morphosyntactic representations of multilingual word embeddings. |
| Outcome: | The proposed probing task makes it easy to explore morphosyntactic representations . it also allows the study of how language models handle co-occurring features . |
Co-Teaching Student-Model through Submission Results of Shared Task (2021.findings-emnlp)
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| Challenge: | Shared tasks require participants to submit only system outputs and descriptions. |
| Approach: | They propose to utilize all system outputs in a shared task to build a unified system that performs better than the task's single best system. |
| Outcome: | The proposed scheme outperforms the best system in the SHINRA2019-JP shared task with nine participants. |
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained Relationships (2021.findings-emnlp)
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| Challenge: | Existing knowledge-enhanced pretrained language models focus on entity information and ignore fine-grained relationships between entities. |
| Approach: | They propose to incorporate KG into the language learning process to obtain a KG-enhanced pretrained Language Model. |
| Outcome: | The proposed model improves on several knowledge-driven tasks, such as entity typing and relation classification, compared with the state-of-the-art knowledge-enhanced PLMs. |
Do We Know What We Don’t Know? Studying Unanswerable Questions beyond SQuAD 2.0 (2021.findings-emnlp)
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| Challenge: | Recent work has attempted to improve extractive QA performance by enriching the dataset with unanswerable questions. |
| Approach: | They build an out-of-domain corpus of competitive and non-competitive questions . they compare the results with the results of the Recognizing Textual Entailments task . |
| Outcome: | The proposed model fails even in the case of simpler questions . the proposed model can be used to address more realistic situations in reading comprehension . |
Glyph Enhanced Chinese Character Pre-Training for Lexical Sememe Prediction (2021.findings-emnlp)
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| Challenge: | Sememes are defined as the atomic units to describe the semantic meaning of concepts. |
| Approach: | They propose a method which incorporates internal Chinese character information to help sememe prediction. |
| Outcome: | The proposed method outperforms existing non-external information models on howNet, a famous sememe knowledge base. |
Active Learning for Rumor Identification on Social Media (2021.findings-emnlp)
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| Challenge: | Existing methods for rumor tracking depend on a significant amount of labeled data. |
| Approach: | They propose an Active-Transfer Learning strategy to identify rumors with limited amount of annotated data. |
| Outcome: | The proposed approach achieves faster convergence in terms of the F-score while requiring fewer annotated samples (42% of the whole dataset for the best model). |
Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text (2021.findings-emnlp)
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| Challenge: | Existing methods for named entity disambiguation are limited by coarse-grained structural resources in biomedical knowledge bases and training datasets that provide low coverage over uncommon resources. |
| Approach: | They propose a method that integrates structural knowledge from general text knowledge bases to the medical domain. |
| Outcome: | The proposed method improves disambiguation accuracy on two benchmark medical NED datasets by up to 57 points. |
Self-Training using Rules of Grammar for Few-Shot NLU (2021.findings-emnlp)
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| Challenge: | Existing methods for learning natural language understanding are limited in low-resource settings. |
| Approach: | They propose to use rules of grammar to construct and expand rules of grammatical structure of data without human involvement. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in three benchmark datasets. |
Aspect-based Sentiment Analysis in Question Answering Forums (2021.findings-emnlp)
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| Challenge: | Existing studies on aspects-based sentiment analysis focus on a single opinionated sentence. |
| Approach: | They propose a model to combine aspects and their sentiments for QA forums . they use cross-sentence aspect-opinion interaction modeling to align the aspect mentioned in the question and associated opinion clues in the answer. |
| Outcome: | The proposed model outperforms baseline models on three real-world datasets. |
ForumSum: A Multi-Speaker Conversation Summarization Dataset (2021.findings-emnlp)
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| Challenge: | Abstractive summarization quality has been improved but there is a lack of data for conversation summarizing applications. |
| Approach: | They propose to build a conversation summarization dataset with human written summaries from internet forums. |
| Outcome: | The proposed dataset can be easily expanded to improve conversation summarization applications. |
Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework (2021.findings-emnlp)
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| Challenge: | EMQAP is an automated question answering system for electronics devices . it uses a supervised multitask learning framework to identify the section in the E-manual where the answer can be found and the exact answer span within that section. |
| Approach: | They develop an algorithm to exploit data from E-manuals and pretrain RoBERTa on it. |
| Outcome: | The proposed algorithm improves ROUGE-L F1 scores over most competitive baseline. |
Comprehensive Punctuation Restoration for English and Polish (2021.findings-emnlp)
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| Challenge: | Punctuation restoration is a fundamental requirement for the readability of text derived from Automatic Speech Recognition systems. |
| Approach: | They evaluate several methods in the comprehensive punctuation reconstruction task by comparing two languages with a model to determine the quality of the punctuated word. |
| Outcome: | The proposed model improves on two languages with relatively simple and complex morphologies. |
Syntactically Diverse Adversarial Network for Knowledge-Grounded Conversation Generation (2021.findings-emnlp)
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| Challenge: | Existing conversation models produce meaningless and generic responses, which significantly reduce the user experience. |
| Approach: | They propose to fuse knowledge to improve informativeness and adopt latent variables to enhance the diversity of responses. |
| Outcome: | The proposed model can generate syntactically diverse and knowledge-accurate responses while maintaining the knowledge accuracy. |
QACE: Asking Questions to Evaluate an Image Caption (2021.findings-emnlp)
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| Challenge: | Existing metric for image captioning evaluation is based on n-gram similarity metrics but these fail to capture semantic errors in captions. |
| Approach: | They propose a new metric based on Question Answering for Caption Evaluation to evaluate image captioning based upon Question Generation and Question Answers systems. |
| Outcome: | The proposed metric is multi-modal, reference-less and explainable. |
Secoco: Self-Correcting Encoding for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Neural machine translation (NMT) is a challenging field due to the wide variety of noises in real-world scenarios. |
| Approach: | They propose a framework that explicitly deals with noisy inputs for robust neural machine translation by introducing self-correcting predictors. |
| Outcome: | The proposed framework can correct noisy inputs and delete specific errors with the translation decoding process. |
Simple or Complex? Complexity-controllable Question Generation with Soft Templates and Deep Mixture of Experts Model (2021.findings-emnlp)
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| Challenge: | Existing work on complex questions does not consider controlling complexity of generated questions. |
| Approach: | They propose an end-to-end neural complexity-controllable question generation model that incorporates a mixture of experts as the selector of soft templates to capture question similarity while avoiding the expensive construction of actual templates. |
| Outcome: | The proposed model is superior to state-of-the-art methods in both automatic and manual evaluations on two benchmark QA datasets. |
Predicting Anti-Asian Hateful Users on Twitter during COVID-19 (2021.findings-emnlp)
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| Challenge: | Xenophobia and polarization have accompanied widespread social media usage in many nations, attracting many researchers. |
| Approach: | They apply natural language processing techniques to characterize Twitter users who began to post anti-Asian hate messages during COVID-19. |
| Outcome: | The results show that it is possible to predict who later posted anti-Asian slurs on Twitter and Reddit. |
Fine-grained Typing of Emerging Entities in Microblogs (2021.findings-emnlp)
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| Challenge: | Graus et al., 2018) defined emerging entities as those that appear in contexts that emphasize their novelty, and attempted to discover emerging entities from microblogs. |
| Approach: | They propose a task that assigns a fine-grained type to each emerging entity when a burst of posts containing that entity is first observed in a microblog. |
| Outcome: | The proposed model can type 'homographic' emerging entities without relying on prior knowledge of the target entity. |
Data-Efficient Language Shaped Few-shot Image Classification (2021.findings-emnlp)
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| Challenge: | Existing studies have shown that language is helpful guider for image understanding by neural networks. |
| Approach: | They propose a language-shaped learning method that makes the best use of the few-shot images and the language available only in training. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a few-shot dataset with limited training data. |
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Quality Estimation (QE) is an essential role in applications of Machine Translation (MT). |
| Approach: | They propose to fuse uncertainty quantification into a pre-trained cross-lingual language model to predict the translation quality. |
| Outcome: | The proposed method achieves state-of-the-art on the datasets of WMT 2020 QE shared task. |
Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech (2021.findings-emnlp)
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| Challenge: | Existing methods for hate speech detection are limited in size and lack of labeled datasets. |
| Approach: | They employ pretrained language models to generate large amounts of hate speech sequences from available labeled examples. |
| Outcome: | The proposed model improves generalization significantly and consistently within and across data distributions. |
AutoEQA: Auto-Encoding Questions for Extractive Question Answering (2021.findings-emnlp)
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| Challenge: | Extractive question answering models are reliant on annotations of answer-spans in the corresponding passages. |
| Approach: | They propose a method that auto-encodes a question and generates corresponding questions from it. |
| Outcome: | The proposed method performs well in a zero-shot setting and can provide an additional loss to boost performance for extractive question answering (EQA). |
A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation (2021.findings-emnlp)
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| Challenge: | Existing methods treat multi-label learning problem as a single label . Existing approaches focus on measuring semantic similarity of questions and candidate relations . |
| Approach: | They propose to solve multi-hop relation detection problem by generating sequences of hops and labels. |
| Outcome: | The proposed method is effective in KBQA, despite the unknown number of labels and hops. |
Don’t Discard All the Biased Instances: Investigating a Core Assumption in Dataset Bias Mitigation Techniques (2021.findings-emnlp)
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| Challenge: | Existing techniques for mitigating dataset bias often leverage a biased model to identify biased instances. Existing methods for mitiging dataset bias use shallow patterns that can be exploited by the model. |
| Approach: | They propose to use partial-input and limited-capacity models to detect biased instances and reduce their role during training to enhance its robustness to out-of-distribution data. |
| Outcome: | The proposed method outperforms existing methods for mitigating dataset bias on two well-known datasets in the domain, MNLI and FEVER. |
Stacked AMR Parsing with Silver Data (2021.findings-emnlp)
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| Challenge: | Lack of large-scale annotated data is one main challenge for abstract meaning representation (AMR) parsing. |
| Approach: | They propose to use silver data to train a pre-trained abstract meaning representation model. |
| Outcome: | The proposed model outperforms previous models on the AMR2.0 dataset and is faster than the SOTA model. |
Speculative Sampling in Variational Autoencoders for Dialogue Response Generation (2021.findings-emnlp)
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| Challenge: | Existing studies have tried to improve variational models but they fail to learn proper mappings. |
| Approach: | They propose to use a variable-based sampling technique to find the most probable one from redundantly sampled latent variables to tie up the variable with a given response. |
| Outcome: | The proposed method is effective in response generation with massive dialogue data constructed from Twitter posts. |
Perceived and Intended Sarcasm Detection with Graph Attention Networks (2021.findings-emnlp)
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| Challenge: | Existing sarcasm detection systems focus on exploiting linguistic markers, context, or user-level priors, but social studies suggest that the relationship between the author and the audience can be equally relevant for the sarkasmal usage and interpretation. |
| Approach: | They propose a framework leveraging a user context from their historical tweets together with social information from a users neighborhood in an interaction graph to contextualize the interpretation of the post. |
| Outcome: | The proposed framework combines a user context from their historical tweets with social information from a users neighborhood in an interaction graph to contextualize the interpretation of the post. |
Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation (2021.findings-emnlp)
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| Challenge: | Exemplar-Guided Paraphrase Generation (EGPG) aims to generate a target sentence which conforms to the style of the given exemplar while encapsulating the content information of the source sentence. |
| Approach: | They propose a method to generate a target sentence which conforms to the style of the given exemplar while encapsulating the content information of the source sentence. |
| Outcome: | The proposed method can generate a target sentence which conforms to the style of the given exemplar while encapsulating the content information of the source sentence. |
MAD-G: Multilingual Adapter Generation for Efficient Cross-Lingual Transfer (2021.findings-emnlp)
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Alan Ansell, Edoardo Maria Ponti, Jonas Pfeiffer, Sebastian Ruder, Goran Glavaš, Ivan Vulić, Anna Korhonen
| Challenge: | Massively multilingual transformers (MMTs) have benefited from additional training of language-specific adapters, but this approach is not viable for the vast majority of languages due to limitations in their corpus size or compute budgets. |
| Approach: | They propose a multilingual ADapter generation approach which contextually generates language adapters from language representations based on typological features. |
| Outcome: | The proposed method improves cross-lingual transfer performance on part-of-speech tagging, dependency parsing, and named entity recognition tasks while remaining cost-effective. |
Sustainable Modular Debiasing of Language Models (2021.findings-emnlp)
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| Challenge: | Existing debiasing methods modify all of the PLM parameters, which is costly and leads to (catastrophic) forgetting of useful language knowledge. |
| Approach: | They propose a modular debiasing approach based on dedicated adapters that inject adapter modules into the original PLM layers and update only the adapters. |
| Outcome: | The proposed approach is based on dedicated adapters and retains fairness even after large-scale training. |
A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering (2021.findings-emnlp)
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| Challenge: | Existing methods for relation detection only detect one path to obtain the answer without considering other correct paths. |
| Approach: | They propose a divide-and-conquer approach for multi-label multi-hop relation detection . they propose 'path sampling mechanism' to generate diverse relation paths . |
| Outcome: | The proposed approach outperforms other competitive approaches on the FreebaseQA benchmark dataset. |
Counterfactual Adversarial Learning with Representation Interpolation (2021.findings-emnlp)
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| Challenge: | Existing models with statistical bias are prone to memorized correlations . large pre-trained models such as BERT have revolutionized the model development paradigm in natural language processing . |
| Approach: | They propose a framework to tackle the problem from a causal perspective using a latent space interpolation approach. |
| Outcome: | Extensive experiments show that CAT achieves substantial performance improvement over SOTA across different downstream tasks, including sentence classification, natural language inference and question answering. |
‘Just What do You Think You’re Doing, Dave?’ A Checklist for Responsible Data Use in NLP (2021.findings-emnlp)
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| Challenge: | a key part of the NLP ethics movement is responsible use of data, but what that means is unclear . a proposed checklist for responsible data (re-)use could standardise peer review of submissions . |
| Approach: | They propose a checklist for responsible data use that could standardise peer review . they propose implementing a standard for data (re-)use across NLP conferences . |
| Outcome: | The proposed checklist would standardise peer review of submissions and enable more in-depth view of published research across the community. |
Counter-Contrastive Learning for Language GANs (2021.findings-emnlp)
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| Challenge: | Generative Adversarial Networks (GANs) have proven to be difficult to generate natural language due to the uninformative learning signals passed from the discriminator. |
| Approach: | They propose to adopt the counter-contrastive learning method to support the generator’s training in language GANs by pulling the language representations of generated and real samples together and pushing apart representations. |
| Outcome: | The proposed method outperforms existing GANs on synthetic and real benchmarks and yields competitive performance compared to previous methods. |
Incorporating Circumstances into Narrative Event Prediction (2021.findings-emnlp)
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| Challenge: | Existing studies focus on mining the inter-events relationships while ignoring how the events happened. |
| Approach: | They propose to incorporate event circumstances into the narrative event prediction by combining two multi-head attention modules and regularizing attention weights. |
| Outcome: | The proposed model outperforms baseline models by 12.2%. |
MultiFix: Learning to Repair Multiple Errors by Optimal Alignment Learning (2021.findings-emnlp)
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| Challenge: | Existing approaches fix a single error in a line, but it is inevitable to iterate until no errors remain. |
| Approach: | They propose a sequence-to-sequence learning framework for fixing multiple program errors at once . they pare an erroneous program with an optimal alignment to the correct program . |
| Outcome: | The proposed approach achieves state-of-the-art on a dataset of 6,975 erroneous C programs . the proposed framework is based on an edit-distance-based data labeling approach . |
HOTTER: Hierarchical Optimal Topic Transport with Explanatory Context Representations (2021.findings-emnlp)
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Sabine Wehnert, Christian Scheel, Simona Szakács-Behling, Maret Nieländer, Patrick Mielke, Ernesto William De Luca
| Challenge: | Natural language processing (NLP) is often the backbone of today’s systems for user interactions, information retrieval and others. |
| Approach: | They propose an extension to a specific emerging hybrid document distance metric which combines topic models and word embeddings. |
| Outcome: | The proposed method is competitive on public datasets and the language model BERT is used for a document categorization task. |
Grammatical Error Correction with Contrastive Learning in Low Error Density Domains (2021.findings-emnlp)
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| Challenge: | grammatical error correction (GEC) is a text generation task . performance on low error density domains where texts written by native speakers can be improved. |
| Approach: | They propose a contrastive learning approach to encourage the GEC model to assign a higher probability to a correct sentence while reducing the probability of incorrect sentences that the model tends to generate. |
| Outcome: | The proposed approach significantly improves the performance of GEC models in low error density domains. |
Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference (2021.findings-emnlp)
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| Challenge: | Recent methods based on pre-trained language models have shown strong supervised performance on commonsense reasoning. |
| Approach: | They propose to use a common framework to solve commonsense reasoning tasks using a dataset from NLI. |
| Outcome: | The proposed method achieves state-of-the-art unsupervised performance on two commonsense reasoning tasks. |
Does Putting a Linguist in the Loop Improve NLU Data Collection? (2021.findings-emnlp)
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Alicia Parrish, William Huang, Omar Agha, Soo-Hwan Lee, Nikita Nangia, Alexia Warstadt, Karmanya Aggarwal, Emily Allaway, Tal Linzen, Samuel R. Bowman
| Challenge: | Many datasets for training and evaluating natural language understanding (NLU) models contain systematic artifacts that are identified only after data collection is complete. |
| Approach: | They propose to have linguists identify artifacts and gaps in the data and communicate with non-expert crowdworkers to adjust task instructions and incentives. |
| Outcome: | The proposed protocol does not increase accuracy on out-of-domain test sets, and adds a chatroom does not. |
Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding (2021.findings-emnlp)
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| Challenge: | Large-scale, pre-trained language models (LMs) have achieved human-level performance on a breadth of language understanding tasks. |
| Approach: | They propose a commonsense reasoning dataset with dense annotations that allows multi-tiered evaluation of machines’ reasoning process. |
| Outcome: | The proposed model can achieve high end performance but struggle to support predictions with valid supporting evidence. |
Making Heads and Tails of Models with Marginal Calibration for Sparse Tagsets (2021.findings-emnlp)
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| Challenge: | despite high accuracy, modern neural networks can still suffer from severe miscalibration. |
| Approach: | They propose to use tag frequency grouping to measure calibration error in different frequency bands to reduce error. |
| Outcome: | The proposed techniques reduce calibration error across the marginal distribution for two existing sequence taggers. |
GeDi: Generative Discriminator Guided Sequence Generation (2021.findings-emnlp)
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Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, Nazneen Fatema Rajani
| Challenge: | Existing methods for controlling LMs have limitations. |
| Approach: | They propose a class-conditional LM that uses a control code to control text generation. |
| Outcome: | The proposed algorithm is much faster than the existing methods for generating from the LM directly. |