Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Unsupervised Sentence-embeddings by Manifold Approximation and Projection (2021.eacl-main)
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| Challenge: | Existing methods to generate sentence-embeddings are task-agnostic and often lose information because of word-order. |
| Approach: | They propose to generate sentence-embeddings by projecting sentences onto a fixed-dimensional manifold with the objective of preserving local neighbourhoods in the original space. |
| Outcome: | The proposed method consistently performs similar to or better than several state-of-the-art approaches on six publicly available text-classification datasets. |
Contrastive Multi-document Question Generation (2021.eacl-main)
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Woon Sang Cho, Yizhe Zhang, Sudha Rao, Asli Celikyilmaz, Chenyan Xiong, Jianfeng Gao, Mengdi Wang, Bill Dolan
| Challenge: | Multi-document question generation focuses on generating a question that covers the common aspect of multiple documents, but a naive model trained only using the targeted document set may generate too generic questions that cover a larger scope than delineated by the document set. |
| Approach: | They propose a contrastive learning strategy where given ‘positive’ and ‘negative’ sets of documents, generate a question that is closely related to the ‘positive' set but far away from the ‘negative' set. |
| Outcome: | The proposed model significantly outperforms several strong baselines, as measured by automatic metrics and human evaluation. |
Disambiguatory Signals are Stronger in Word-initial Positions (2021.eacl-main)
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| Challenge: | lexical studies of word processing and lexicon access provide evidence of preferred nature of word-initial versus word-final segments . conjecture that languages have evolved to provide more information earlier in words is based on existing methods . |
| Approach: | They propose to use a new method to assess the informativeness of word-initial versus word-final segments. |
| Outcome: | The proposed measures avoid the confounds found in existing methods. |
On the (In)Effectiveness of Images for Text Classification (2021.eacl-main)
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| Challenge: | Existing studies have focused on text classification, but have shown that images do not improve NLP tasks. |
| Approach: | They focus on text classification, where images complement the text and the Wikipedia page can be in one of a number of different languages. |
| Outcome: | The proposed model trains without external pre-training, but when combined with BERT models pre-trained on large-scale external data, images contribute nothing. |
If you’ve got it, flaunt it: Making the most of fine-grained sentiment annotations (2021.eacl-main)
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| Challenge: | a recent study shows that annotating sentiments is difficult and difficult. |
| Approach: | They propose to integrate holder and expression information into sentiment analysis to improve target extraction . they perform experiments on eight English datasets to determine whether annotating expressions improves target extraction. |
| Outcome: | The proposed approach improves target extraction and classification on English datasets. |
Keep Learning: Self-supervised Meta-learning for Learning from Inference (2021.eacl-main)
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| Challenge: | A common approach to improve performance of machine learning algorithms involves self-supervised learning on large unlabeled data before fine-tuning on downstream tasks. |
| Approach: | They propose to use model's own class-balanced predictions to back-propagate the loss from the model''s class-balancing predictions (pseudo-labels) this method improves performance of standard backbones such as BERT, Electra, and ResNet-50 on a wide variety of tasks, including question answering on SQuAD and NewsQA . |
| Outcome: | The proposed method outperforms previous approaches on a wide variety of tasks including question answering on SQuAD and NewsQA, benchmark task SuperGLUE, conversation response selection on Ubuntu Dialog corpus v2.0, and image classification on MNIST and ImageNet. |
ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations (2021.eacl-main)
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Ritam Dutt, Sayan Sinha, Rishabh Joshi, Surya Shekhar Chakraborty, Meredith Riggs, Xinru Yan, Haogang Bao, Carolyn Rose
| Challenge: | Existing research has failed to account for resisting strategies employed to foil persuasion attempts. |
| Approach: | They propose a framework for identifying resisting strategies in persuasive conversations . they instantiate a dataset comprising persuasion and negotiation conversations based on a hierarchical sequence-labelling neural architecture . |
| Outcome: | The proposed framework is based on two persuasive conversation datasets and leverages a hierarchical sequence-labelling neural architecture to infer resisting strategies automatically. |
BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression (2021.eacl-main)
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| Challenge: | Existing methods to make exiting decisions are limited to classification tasks . large-scale pre-trained language models such as BERT have brought performance gain but at the cost of heavy computational burden. |
| Approach: | They propose a fine-tuning strategy and a learning-to-exit module to accelerate BERT inference . they propose to make trade-offs between model quality and efficiency by early exiting . |
| Outcome: | The proposed approach improves early exiting for BERT, with better trade-offs . it can be combined with other acceleration methods, and the proposed strategy can be applied to regression tasks. |
Telling BERT’s Full Story: from Local Attention to Global Aggregation (2021.eacl-main)
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| Challenge: | Recent work discouraging the use of attention distributions for explaining a model’s behaviour suggests that attention distribution can provide insights into local behaviour of attention heads. |
| Approach: | They propose a distinction between local patterns revealed by attention and global patterns that refer back to the input and analyze BERT from both angles. |
| Outcome: | The proposed model can explain local behaviour of attention heads by comparing local and global patterns from both angles. |
Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection (2021.eacl-main)
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| Challenge: | Lexical semantic change detection is a new and innovative research field. |
| Approach: | They propose to pre-train on large corpora and refine on diachronic target corpors to improve performance. |
| Outcome: | The proposed models improve on large corpora and diachronic target corpors . the proposed models are compared with existing models in a variety of learning scenarios . |
The Gutenberg Dialogue Dataset (2021.eacl-main)
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| Challenge: | Current open-domain dialogue datasets offer a trade-off between quality and size . we build a dataset of 14.8M utterances in English and smaller datasets in german, Dutch, Spanish, Portuguese, Italian, and Hungarian . |
| Approach: | They build a high-quality dialogue corpus of 14.8M utterances in English using public-domain books from Project Gutenberg. |
| Outcome: | The proposed datasets show that the extracted dialogues are more accurate and more accurate than the larger Opensubtitles dataset. |
On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search (2021.eacl-main)
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| Challenge: | Existing methods to rank documents in decreasing order of their probability of relevance are not well calibrated and have several sources of uncertainty. |
| Approach: | They propose to calibrate deterministic neural rankers for conversational search problems . they then use two techniques to model the uncertainty of neural ranker's uncertainty . |
| Outcome: | The proposed rankers output a predictive distribution of relevance as opposed to point estimates. |
Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples (2021.eacl-main)
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| Challenge: | Existing methods to detect adversarial examples are limited by the nature of these examples. |
| Approach: | They propose a frequency-guided word substitution algorithm that exploits adversarial word substitutions for the detection of adversarials. |
| Outcome: | The proposed algorithm outperforms existing detection methods by 13.0% on the SST-2 and IMDb sentiment datasets. |
Maximal Multiverse Learning for Promoting Cross-Task Generalization of Fine-Tuned Language Models (2021.eacl-main)
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| Challenge: | Recent studies suggest the use of general language models for improving natural language processing tasks. |
| Approach: | They propose a method that leverages the second phase to its fullest by applying an extensive number of parallel classifier heads, which are enforced to be orthogonal, while adaptively eliminating the weaker heads during training. |
| Outcome: | The proposed method improves the generalization ability of BERT, sometimes leading to a +9% gain in accuracy. |
Unification-based Reconstruction of Multi-hop Explanations for Science Questions (2021.eacl-main)
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| Challenge: | Existing approaches build explanations considering each question in isolation, but new approach leverages explanatory patterns emerging in scientific explanations. |
| Approach: | They propose a framework for reconstructing multi-hop explanations in science Question Answering . they integrate lexical relevance with the notion of unification power to rank atomic facts . |
| Outcome: | The proposed method achieves results competitive with Transformers, but is faster and scalable to large explanatory corpora. |
Dictionary-based Debiasing of Pre-trained Word Embeddings (2021.eacl-main)
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| Challenge: | Existing methods for learning word embeddings using dictionaries do not require access to training resources or knowledge regarding the word embeds used. |
| Approach: | They propose a method for debiasing pre-trained word embeddings using dictionaries . they learn constraints that must be satisfied by unbiased word embeds from dictionary definitions . |
| Outcome: | The proposed method removes unfair biases encoded in pre-trained word embeddings while preserving useful semantics. |
Belief-based Generation of Argumentative Claims (2021.eacl-main)
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| Challenge: | Existing methods to generate argument with the ability to encode beliefs are limited by the noise generated by the automatic collection of bag-of-words. |
| Approach: | They propose to augment argument generation technology with ability to encode beliefs . they model users' beliefs via their stances on big issues and extend text generation models with extra input reflecting user's beliefs. |
| Outcome: | The proposed approach is low in effectiveness because of the noise produced by the automatic collection of bag-of-words. |
Non-Autoregressive Text Generation with Pre-trained Language Models (2021.eacl-main)
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| Challenge: | Autoregressive generation models generate tokens in a left-to-right, token-by-token fashion, resulting in lag in inference. |
| Approach: | They propose to use BERT as the backbone of a non-autoregressive generation model for greatly improved performance. |
| Outcome: | The proposed model outperforms existing non-autoregressive models and achieves competitive performance with many strong autoregressive model. |
Multi-split Reversible Transformers Can Enhance Neural Machine Translation (2021.eacl-main)
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| Challenge: | Large-scale transformers have been shown to improve neural machine translation performance but training these wider and deeper networks could be extremely memory intensive. |
| Approach: | They propose a multi-split based reversible transformer and a backpropagation algorithm that does not need to store activations for most layers. |
| Outcome: | The proposed model outperforms the vanilla transformer by at least 1.4 BLEU points in three datasets. |
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference (2021.eacl-main)
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| Challenge: | Existing approaches to learning from examples are limited due to the vast number of languages, domains and tasks. |
| Approach: | They propose a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task. |
| Outcome: | The proposed approach outperforms supervised training and strong semi-supervised approaches in low-resource settings by a large margin. |
CDˆ2CR: Co-reference resolution across documents and domains (2021.eacl-main)
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| Challenge: | Current models for cross-document co-reference resolution assume that all documents are of the same type or fall under the same theme. |
| Approach: | They propose a task for cross-document cross-domain co-reference resolution (CD2CR) task aims to identify links between entities across heterogeneous document types. |
| Outcome: | The proposed task outperforms current state-of-the-art models on CD2CR in cross-domain, cross-document setting. |
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization (2021.eacl-main)
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| Challenge: | Existing studies on redundancy are focused on salience alone. |
| Approach: | They propose to combine salience and novelty to score redundancy in extractive summarization systems . they also propose to balance saliance and redundancies by scoring redundants first . |
| Outcome: | Empirical results show that AREDSUM-CTX scores salience first, then learns to balance saliency and redundancy. |
“Talk to me with left, right, and angles”: Lexical entrainment in spoken Hebrew dialogue (2021.eacl-main)
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| Challenge: | entrainment is a widespread phenomenon in human interaction that leads interlocutors to adapt their linguistic productions to become more similar to each other. |
| Approach: | They propose to use existing measures to analyze Hebrew speakers interacting in a Map Task to find evidence of lexical entrainment. |
| Outcome: | The proposed study is the first to examine lexical entrainment in Hebrew using two existing measures. |
Recipes for Building an Open-Domain Chatbot (2021.eacl-main)
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Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Eric Michael Smith, Y-Lan Boureau, Jason Weston
| Challenge: | Existing work shows that scaling models in the number of parameters and the size of the data they are trained on gives improved results, but other factors are important. |
| Approach: | They propose to build open-domain chatbots that can be scaled to improve their performance . they use a blend of cognitive and cognitive skills to build a model that combines these skills . |
| Outcome: | The proposed models outperform existing approaches in multi-turn dialogue on engagingness and humanness measurements. |
Evaluating the Evaluation of Diversity in Natural Language Generation (2021.eacl-main)
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| Challenge: | Existing methods for controlling diversity by tuning a “decoding parameter” affect form but not meaning. |
| Approach: | They propose a framework that measures correlation between a diversity metric and a parameter that controls some aspect of diversity in generated text. |
| Outcome: | The proposed framework outperforms existing methods in estimating diversity . it shows that humans outperformed existing methods but affect form but not meaning . |
Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering (2021.eacl-main)
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| Challenge: | Existing work on question answering over knowledge bases limited the search space to a subset of KBs . a retrieval-and-rerank framework is used to access KB and rerank retrieved candidates with more powerful neural networks. |
| Approach: | They propose to share a BERT encoder across all three sub-tasks and define task-specific layers on top of the shared layer. |
| Outcome: | The proposed method improves accuracy and accuracy on the SimpleQuestions dataset and the FreebaseQA dataset. |
Implicitly Abusive Comparisons – A New Dataset and Linguistic Analysis (2021.eacl-main)
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| Challenge: | Using crowdsourcing, we can detect implicitly abusive comparisons . Abusive language is defined as hurtful, derogatory or obscene utterances made by one person to another . |
| Approach: | They propose to use crowdsourcing to generate a dataset for detecting implicitly abusive comparisons . they also use a range of linguistic features to better understand abusive comparison mechanisms . |
| Outcome: | The proposed dataset includes measures to obtain representative and unbiased comparisons. |
Exploiting Emojis for Abusive Language Detection (2021.eacl-main)
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| Challenge: | emojis can be used as a proxy for learning a lexicon of abusive words . eliot safina and samuel khan are the authors of this paper . |
| Approach: | They propose to use abusive emojis as a proxy for learning a lexicon of abusive words. |
| Outcome: | The proposed approach generates a lexicon that performs as well as the most advanced lexical induction method. |
A Systematic Review of Reproducibility Research in Natural Language Processing (2021.eacl-main)
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| Challenge: | Despite the recent progress in reproducibility, the field is far from reaching a consensus on how reproducibility should be defined, measured and addressed. |
| Approach: | They propose to provide a wide-angle snapshot of current work on reproducibility in NLP. |
| Outcome: | The proposed work will provide a wide-angle snapshot of current work on reproducibility in NLP. |
Bootstrapping Multilingual AMR with Contextual Word Alignments (2021.eacl-main)
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Janaki Sheth, Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Radu Florian, Salim Roukos, Todd Ward
| Challenge: | Abstract Meaning Representation (AMR) is a sentence-level graph that is biased towards English. |
| Approach: | They propose a technique for foreign-text-to-English AMR alignment using contextual word alignment between English and foreign language tokens. |
| Outcome: | The proposed technique outperforms the best results for German, Italian, Spanish and Chinese. |
Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks (2021.eacl-main)
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| Challenge: | Social networks face a major challenge in the form of rumors and fake news . rumor detection is suboptimal due to its rapidity and spread of information . |
| Approach: | They propose a semantic oppositeness model that captures elements of discord . they show that it is more resistant to variances introduced by randomness . |
| Outcome: | The proposed model achieves state-of-the-art on rumor detection task with extensive experiments on recent data sets. |
Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation (2021.eacl-main)
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| Challenge: | Existing methods for learning disentangled representations of real-world data focus on attribute labels or unsupervised methods that manipulate factorization in the latent space of models such as the variational autoencoder (VAE). |
| Approach: | They propose an approach that disentangles select attributes in the latent space based on proximity measures reflecting the similarity between data points with respect to these attributes. |
| Outcome: | The proposed method outperforms the VAE baseline and is competitive with state-of-the-art approaches while being more a general framework applicable to other attribute disentanglement tasks. |
ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation (2021.eacl-main)
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| Challenge: | Existing paraphrase datasets are mainly from news, novels, or social media platforms. |
| Approach: | They propose to build a large-scale paraphrase dataset using intra-paper and inter-paper methods . they use PDBERT as a general paraphrase discovering method to take advantage of paraphrased sentences . |
| Outcome: | The proposed dataset includes 33,981 paraphrase pairs from ACL and 316,063 pairs from arXiv . the major advantages of paraphrases lie in the prominent length and textual diversity . |
Discourse Understanding and Factual Consistency in Abstractive Summarization (2021.eacl-main)
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Saadia Gabriel, Antoine Bosselut, Jeff Da, Ari Holtzman, Jan Buys, Kyle Lo, Asli Celikyilmaz, Yejin Choi
| Challenge: | Existing abstractive summarization models often hallucinate information or generate factually incorrect summaries. |
| Approach: | They propose a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary. |
| Outcome: | The proposed framework generates abstracts with factual consistency and coherence significantly better than baselines. |
Knowledge Base Question Answering through Recursive Hypergraphs (2021.eacl-main)
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| Challenge: | Existing methods for Knowledge Base Question Answering (KBQA) do not explicitly incorporate the recursive relational group structure in the given knowledge base. |
| Approach: | They propose a method to model KBs through recursive hypergraphs using hypergraph data. |
| Outcome: | The proposed method is based on recursive hypergraphs and has been released on multiple benchmarks. |
FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary (2021.eacl-main)
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| Challenge: | Existing models for Word Sense Disambiguation struggle to disambiguate rare senses . current models struggle to learn senses with few training examples . |
| Approach: | They introduce a low-shot WSD dataset automatically extracted from example sentences in Wiktionary. |
| Outcome: | The proposed dataset outperforms baseline models on rare senses in existing datasets. |
MONAH: Multi-Modal Narratives for Humans to analyze conversations (2021.eacl-main)
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| Challenge: | In conversational analyses, humans manually weave multimodal information into the transcripts, which is significantly time-consuming. |
| Approach: | They propose a system that automatically expands the verbatim transcripts of video-recorded conversations using multimodal data streams. |
| Outcome: | The proposed system improves detecting rapport-building by expanding the range of multimodal annotations. |
Does Typological Blinding Impede Cross-Lingual Sharing? (2021.eacl-main)
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| Challenge: | Existing work on bridging the performance gap between high- and low-resource languages has only found minor benefits from using typological information. |
| Approach: | They propose to use typological features to train models in a cross-lingual setting to learn latent weights between languages. |
| Outcome: | The proposed model overshadows the utility of explicitly using typological features by ignoring them, and shows that encouraging sharing according to typology improves performance. |
AdapterFusion: Non-Destructive Task Composition for Transfer Learning (2021.eacl-main)
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| Challenge: | Existing methods for incorporating knowledge from multiple tasks suffer from catastrophic forgetting and difficulties in dataset balancing. |
| Approach: | They propose an algorithm that extracts and combine adapters in a knowledge composition step. |
| Outcome: | The proposed class outperforms traditional methods such as full fine-tuning and multi-task learning on 16 diverse NLU tasks. |
CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata (2021.eacl-main)
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Manoj Prabhakar Kannan Ravi, Kuldeep Singh, Isaiah Onando Mulang’, Saeedeh Shekarpour, Johannes Hoffart, Jens Lehmann
| Challenge: | Existing approaches to target end-to-end entity linking over knowledge bases are not efficient. |
| Approach: | They propose a modular approach to target end-to-end entity linking over knowledge bases. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches on two well-known knowledge bases. |
Grounding as a Collaborative Process (2021.eacl-main)
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| Challenge: | a new study shows that dialog requires a collaborative grounding approach to ground meaning . the problem is that dialog partners are not able to ground themselves . |
| Approach: | They argue that it is missing from current deep learning approaches to dialog . they argue that making mistakes and being able to recover from them is key . |
| Outcome: | The proposed model is based on the language acquisition and dialog systems literature . it shows that making mistakes and being able to recover from them is key . |
Does She Wink or Does She Nod? A Challenging Benchmark for Evaluating Word Understanding of Language Models (2021.eacl-main)
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| Challenge: | Existing probing datasets focus on knowledge about relations between words and entities. |
| Approach: | They propose a new probing task to evaluate word understanding directly using dictionary definitions of words. |
| Outcome: | The proposed task evaluates word understanding directly using dictionary definitions of words. |
Joint Coreference Resolution and Character Linking for Multiparty Conversation (2021.eacl-main)
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| Challenge: | Character linking is the task of linking mentioned people in conversations to the real world . human use of pronouns or normal entities makes it difficult to link mentioned people to real people . a critical step towards understanding conversations is grounding mentioned people - a goal of the natural language processing community . |
| Approach: | They propose to integrate richer context from the coreference relations among different mentions to help the linking task. |
| Outcome: | The proposed model outperforms all previous models on both tasks. |
Improving Factual Consistency Between a Response and Persona Facts (2021.eacl-main)
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| Challenge: | Neural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with persona facts. |
| Approach: | They propose to fine-tune these models by reinforcement learning and an efficient reward function that explicitly captures the consistency between a response and persona facts as well as semantic plausibility. |
| Outcome: | The proposed model improves the rate of responses that are factually consistent with persona facts over its supervised counterpart while maintaining the language quality of responses. |
PolyLM: Learning about Polysemy through Language Modeling (2021.eacl-main)
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| Challenge: | Existing methods to embed word senses have been overtaken by contextualized embeddings . alan ansell and jim koenig present a method which can be applied to downstream tasks . |
| Approach: | They propose a method which formulates learning sense embeddings as a language modeling problem. |
| Outcome: | The proposed method performs better than existing sense embedding methods on WSI tasks . it matches the current state-of-the-art specialized WSi method despite having six times fewer parameters . |
Predicting Treatment Outcome from Patient Texts:The Case of Internet-Based Cognitive Behavioural Therapy (2021.eacl-main)
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Evangelia Gogoulou, Magnus Boman, Fehmi Ben Abdesslem, Nils Hentati Isacsson, Viktor Kaldo, Magnus Sahlgren
| Challenge: | Existing methods to predict treatment outcome are limited to text categorisation, but they can be applied to patient texts. |
| Approach: | They propose to use patient text as the only signal for predicting treatment outcome in Internet-based cognitive behavioural therapy for depression, social anxiety, and panic disorder. |
| Outcome: | The proposed method beats stratified random guessing by using a simple Bag of Words to predict treatment success and failure. |
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning (2021.eacl-main)
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| Challenge: | Current methods for document set expansion for large collections are based on word-frequency or bag-of-words document similarity metrics. |
| Approach: | They propose to extend the IR approach by treating the problem as an instance of positive-unlabeled (PU) learning . they propose solutions for each challenge and empirically validate them with ablation tests . |
| Outcome: | The proposed method improves on a PubMed abstract retrieval task . it is compared with existing methods and empirically validated with ablation tests . |
The Role of Syntactic Planning in Compositional Image Captioning (2021.eacl-main)
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| Challenge: | Image captioning is a core task in multimodal NLP, where the aim is to automatically describe the content of an image in natural language. |
| Approach: | They propose to use syntactic tags and tokens to improve caption generalization . they also propose to model the syntakic structure of a caption to improve generalization. |
| Outcome: | The proposed models improve generalization and performance on standard metrics while requiring syntactic and semantic knowledge of the language. |
Is “hot pizza” Positive or Negative? Mining Target-aware Sentiment Lexicons (2021.eacl-main)
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| Challenge: | Existing sentiment lexicons assume words’ sentiments are invariant within a domain, but this assumption is weak for fine-granularity analyses of text sentiments. |
| Approach: | They propose a "perturb-and-see" method to extract commonsense sentiments from large-scale datasets by binding a word's sentiment to its collocation words instead of domain labels. |
| Outcome: | The proposed framework is able to achieve highly competitive performances on the unsupervised opinion relation extraction task. |
Quality Estimation without Human-labeled Data (2021.eacl-main)
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Yi-Lin Tuan, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Francisco Guzmán, Lucia Specia
| Challenge: | Quality estimation aims to measure the quality of translated content without access to a reference translation. |
| Approach: | They propose a method that uses synthetic training data to train supervised quality estimation models. |
| Outcome: | The proposed model outperforms models trained on human-annotated data for sentence and word-level prediction. |
How Fast can BERT Learn Simple Natural Language Inference? (2021.eacl-main)
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| Challenge: | Efficiency of learning of BERT is very slow due to hidden dataset bias . however, some studies show that it can learn with surface clues/patterns . |
| Approach: | They propose to use a simple entailment judgment case to test whether BERT can learn without hidden dataset bias. |
| Outcome: | The proposed case shows that BERT can learn without hidden bias without utilizing dataset bias. |
GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction (2021.eacl-main)
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| Challenge: | Document-level role-filler entity extraction (REE) is a classic problem in information extraction (IE) and NLP. |
| Approach: | They propose a document-level role-filler entity extraction framework that can make extraction decisions across sentence boundaries and implicitly recognizes noun phrase coreference structure. |
| Outcome: | The proposed framework performs better than previous models on a MUC-4 dataset. |
Cross-lingual Entity Alignment with Incidental Supervision (2021.eacl-main)
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| Challenge: | Existing methods to match entities in multilingual knowledge graphs are insufficient, resulting in inconsistent seed alignment between KGs. |
| Approach: | They propose a model that integrates multilingual KGs and monolingual text corpora in a shared embedding scheme and a self-learning based alignment learning process to induce correspondence between entities and lexemes. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods on benchmark datasets and significantly outpersts existing methods. |
Query Generation for Multimodal Documents (2021.eacl-main)
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| Challenge: | Existing approaches to find relevance for multimodal documents with images are expensive and require a lot of runtime overhead. |
| Approach: | They propose to attach generated queries to doc-uments and index them to narrow down to candidate matches using inverted index. |
| Outcome: | The proposed model improves relevance ranking for multimodal documents with images . the proposed model can achieve the state of the art in the first stage retrieval scenarios . |
End-to-End Argument Mining as Biaffine Dependency Parsing (2021.eacl-main)
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| Challenge: | Argumentation mining (AM) is a new field of research that uses dependency parsing to analyse arguments. |
| Approach: | They propose a neural end-to-end approach to argument mining based on dependency parsing . their model is biaffine dependency parsed and outperforms the current state-of-the-art . |
| Outcome: | The proposed model outperforms the state-of-the-art in component identification and relation identification. |
FakeFlow: Fake News Detection by Modeling the Flow of Affective Information (2021.eacl-main)
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| Challenge: | In short news articles, authors add exaggerations or fabricate events to manipulate readers' emotions. |
| Approach: | They propose to model the flow of affective information in fake news articles using a neural architecture and combine topic and affective data extracted from text. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four real-world datasets and shows that it can capture the flow of affective information in fake news articles. |
CTC-based Compression for Direct Speech Translation (2021.eacl-main)
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| Challenge: | Existing studies have shown that a dynamic phone-informed compression of the input audio is beneficial for speech translation (ST). |
| Approach: | They propose a method which performs a phone-informed compression of the input audio in direct ST models by exploiting the Connectionist Temporal Classification (CTC) they demonstrate that their method brings a 1.3-1.5 BLEU improvement over a strong baseline on two language pairs (English-Italian and English-German) |
| Outcome: | The proposed method brings a 1.3-1.5 BLEU improvement over a strong baseline on two language pairs (English-Italian and English-German) it reduces memory footprint by more than 10%, and is faster than previous approaches. |
A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline (2021.eacl-main)
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Yerbolat Khassanov, Saida Mussakhojayeva, Almas Mirzakhmetov, Alen Adiyev, Mukhamet Nurpeiissov, Huseyin Atakan Varol
| Challenge: | The Kazakh speech corpus contains over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders. |
| Approach: | They propose to build an open-source Kazakh speech corpus for the Kazakh language that contains over 153,000 transcribed audio . they describe the data collection and preprocessing procedures followed by a description of the database specifications. |
| Outcome: | The Kazakh speech corpus contains over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders. |
TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics (2021.eacl-main)
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| Challenge: | Recent efforts to extract tasks, datasets and evaluation metrics from scientific literature focus on abstracts only. |
| Approach: | They propose a corpus that contains domain expert annotations for Task (T), Dataset (D), Metric (M) entities extracted from NLP papers. |
| Outcome: | The proposed corpus contains domain expert annotations for Task (T), Dataset (D), Metric (M) entities extracted from NLP papers. |
Top-down Discourse Parsing via Sequence Labelling (2021.eacl-main)
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| Challenge: | Discourse analysis is a systematic way to understand how texts are segmented hierarchically into discourse units. |
| Approach: | They propose a top-down approach to discourse parsing that is conceptually simpler than its predecessors. |
| Outcome: | The proposed model eliminates the decoder and reduces the search space for splitting points. |
Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning (2021.eacl-main)
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| Challenge: | Recent advances in data-to-text generation have been focused on curriculum learning, which is a process of presenting training data in a specific order, starting from easy examples and moving on to more difficult ones, as the learner becomes more competent. |
| Approach: | They propose to use a curriculum learning process to change the order of training samples in a model based on the model's competence to improve model performance and convergence speed. |
| Outcome: | The proposed model shows faster convergence speed and reduced training time by 38.7% and performance by 4.84 BLEU. |
TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation (2021.eacl-main)
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| Challenge: | Existing content-based recommendations fail when new users use them or a new article is created. |
| Approach: | They propose a model to transfer knowledge from a source corpus to a target corpus and use it to generate representations for unseen users in the future. |
| Outcome: | The proposed model can be used to generate representations for unseen users in the future. |
Dialogue Act-based Breakdown Detection in Negotiation Dialogues (2021.eacl-main)
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| Challenge: | Recent studies have succeeded in modeling a negotiating agent in natural language that can control both text generation and reasoning in goal-oriented dialogue systems. |
| Approach: | They propose a human-human negotiation dialogue dataset that features increased complexities in terms of the number of possible solutions and a utility function. |
| Outcome: | The proposed method performs comparable to text-based approaches in existing corpora and better results in the proposed dataset. |
Neural Data-to-Text Generation with LM-based Text Augmentation (2021.eacl-main)
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| Challenge: | Neural data-to-text generation is a difficult task for many new applications because of a lack of training data. |
| Approach: | They propose a few-shot approach that augments the data available for training by generating new text samples based on replacing specific values by alternative ones from the same category and pairing the new text with data samples. |
| Outcome: | The proposed approach outperforms fully supervised sequence-to-sequence models with less than 10% of the training set on both datasets. |
Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling (2021.eacl-main)
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| Challenge: | Existing approaches to fine-tune pre-trained language models for downstream tasks require labeled data. |
| Approach: | They propose to self-train pre-trained language models to improve performance on data-scarce varieties by as large as 10% F1 and 2% accuracy. |
| Outcome: | The proposed model improves zero-shot MSA-to-DA transfer by as large as 10% F1 (NER) and 2% accuracy (POS tagging). |
Multiple Tasks Integration: Tagging, Syntactic and Semantic Parsing as a Single Task (2021.eacl-main)
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| Challenge: | Existing systems that bypass intermediate levels of analysis are prone to error propagation and are therefore free from interference. |
| Approach: | They propose a multitask paradigm orthogonal to weight sharing that uses multiple tasks to process input iteratively but concurrently at multiple levels of analysis. |
| Outcome: | The proposed model uses reinforcement learning and release from sequential constraints to improve the quality of the syntactic and semantic parses. |
Coordinate Constructions in English Enhanced Universal Dependencies: Analysis and Computational Modeling (2021.eacl-main)
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| Challenge: | Enhanced Universal Dependencies (UD) parses are used to represent syntactic dependencies between words, prioritizing links between content words. |
| Approach: | They propose to use a heuristic rule-based converter to propagate conjuncts from conjunction heads to other conjunctions to determine which set of links to propagation from a semantic perspective. |
| Outcome: | The proposed method outperforms existing methods using basic-layer tree parsers and converters. |
Ellipsis Resolution as Question Answering: An Evaluation (2021.eacl-main)
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| Challenge: | Existing models for ellipsis resolution in English are expensive and cumbersome . ellipas are hard, open problems in NLP, and can cause errors in translation, question answering, and dialogue understanding. |
| Approach: | They propose an alternative approach to ellipsis resolution based on question answering architectures. |
| Outcome: | The proposed model outperforms the current state of the art for ellipsis resolution in English . it shows that annotations can be useful for a subset of the known ellipas . |
Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling (2021.eacl-main)
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| Challenge: | Neural natural language generation and understanding models are data-hungry and require massive amounts of annotated data to be competitive. |
| Approach: | They propose a framework that automatically synthesizes weak labels from large-scale weakly-labeled data with a fine-tuned GPT-2 and adapts parameter updates to the models according to the estimated label-quality. |
| Outcome: | The proposed framework outperforms benchmark systems on the E2E and Weather datasets when 100% of the training data is used. |
Continuous Learning in Neural Machine Translation using Bilingual Dictionaries (2021.eacl-main)
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| Challenge: | Recent advances in neural machine translation have led to astonishing translation quality of research systems. |
| Approach: | They propose to integrate one-shot learning methods with different word representations to assess the ability of neural machine translation to continuously learn new phrases. |
| Outcome: | The proposed framework improves translation quality of bilingual dictionaries from 30% to 70%. |
Adv-OLM: Generating Textual Adversaries via OLM (2021.eacl-main)
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| Challenge: | Recent studies have pointed out the vulnerability of deep learning models to adversarial attacks. |
| Approach: | They propose a black-box attack method that adapts the idea of Occlusion and Language Models to the current state of the art attack methods. |
| Outcome: | The proposed method outperforms existing methods on several text classification tasks. |
Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks (2021.eacl-main)
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| Challenge: | Existing knowledge graphs are widely used for (complex) conversational question answering . LASAGNE improves the F1-score on eight out of ten question types . |
| Approach: | They propose a multi-task neural semantic parsing approach for (complex) conversational question answering over a knowledge graph using a transformer model and a Graph Attention Networks model. |
| Outcome: | The proposed approach outperforms baselines on eight out of ten question types on a standard dataset for complex sequential question answering. |
DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting (2021.eacl-main)
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| Challenge: | Recent work in this area has focused on author stylized rewriting but is limited by the lack of explicit control of target attributes and being data-driven. |
| Approach: | They propose a Director-Generator framework to rewrite input text in the target author’s style, specifically focusing on certain target attributes. |
| Outcome: | The proposed framework has better meaning retention and results in more fluent generations on a small corpus of text authored by three distinct authors. |
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering (2021.eacl-main)
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| Challenge: | Existing approaches to extracting answer from text are expensive to train and train. |
| Approach: | They investigate how much models benefit from retrieving text passages . they obtain state-of-the-art results on the Natural Questions and TriviaQA open benchmarks ." |
| Outcome: | The proposed model performs better when retrieving more passages than previously thought . |
Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration (2021.eacl-main)
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Betty van Aken, Jens-Michalis Papaioannou, Manuel Mayrdorfer, Klemens Budde, Felix Gers, Alexander Loeser
| Challenge: | Clinical decision support systems can help in situations where the patient's development is predicted based on textual data. |
| Approach: | They propose to use clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources into the models. |
| Outcome: | The proposed model improves performance against several baselines and demonstrates that it is transferable and can be used in clinical decision support systems. |
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification (2021.eacl-main)
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| Challenge: | Semi-supervised learning and multilingual pretraining have been shown to be effective for task-specific labelled data shortages. |
| Approach: | They propose to combine semi-supervised deep generative models and multi-lingual pretraining to form a pipeline for document classification task. |
| Outcome: | The proposed method outperforms state-of-the-art models in low-resource settings across several languages and outperformed existing models in English. |
Multi-facet Universal Schema (2021.eacl-main)
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| Challenge: | Existing methods to solve relation extraction tasks violate USchema's assumption that sentence patterns that share the same entity pairs are similar to each other. |
| Approach: | They propose a multi-facet universal schema that embeds multiple sentence patterns as facets and encourages one to be close to that of another if they co-occur with the same entity pair. |
| Outcome: | The proposed model outperforms its single-facet embedding counterpart in relation extraction tasks. |
Exploring Transitivity in Neural NLI Models through Veridicality (2021.eacl-main)
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| Challenge: | Despite recent success of deep neural networks in natural language processing, the extent to which they can demonstrate human-like generalization capacities remains unclear. |
| Approach: | They propose an analysis method to evaluate whether models can draw inferences composed of veridical inference and arbitrary inference types. |
| Outcome: | The proposed model performs poorly on transitivity inference tasks, suggesting it lacks generalization capacity for drawing composite inferences from training examples. |
A Neural Few-Shot Text Classification Reality Check (2021.eacl-main)
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| Challenge: | Modern few-shot text classification models struggle when the amount of annotated data is scarce. |
| Approach: | They compare neural few-shot classification models with NLP and computer vision models with transformers to test their performance. |
| Outcome: | The proposed models perform almost equally on ARSC dataset, but not on the intent detection task. |
Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders (2021.eacl-main)
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| Challenge: | State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages. |
| Approach: | They propose an encoder-decoder approach that can be extended to new languages by learning their corresponding modules. |
| Outcome: | The proposed approach outperforms the universal encoder-decoder by 3.28 BLEU points on average while allowing to add new languages without retraining the rest of the modules. |
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set (2021.eacl-main)
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| Challenge: | Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools. |
| Approach: | They introduce a Large Romanian Sentiment Data Set which is composed of 15,000 positive and negative reviews collected from the largest Romanian e-commerce platform. |
| Outcome: | The proposed data set is composed of 15,000 positive and negative reviews from the largest Romanian e-commerce platform. |
Elastic weight consolidation for better bias inoculation (2021.eacl-main)
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| Challenge: | Recent studies have shown that the lack of suitable inductive biases in sentence-pair classification models can cause misclassifications on training datasets. |
| Approach: | They propose to use elastic weight consolidation (EWC) to fine-tune models to mitigate biases while being less susceptible to catastrophic forgetting. |
| Outcome: | The proposed model improves on fact verification and stress tests while maintaining the original task accuracy. |
Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection (2021.eacl-main)
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| Challenge: | Existing methods to fact-check information focus on word-level attention or evidence-level focus, which may result in suboptimal performance. |
| Approach: | They propose a Hierarchical Multi-head Attentive Network to fact-check textual claims using word-level attention and document-level focus. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two real-word datasets. Improvements over baselines are from 6% to 18%. |
Identifying Named Entities as they are Typed (2021.eacl-main)
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| Challenge: | Named Entity Recognition (NER) systems are not applicable to systems that process text in real time as the text is typed. |
| Approach: | They propose a new experimental setup for evaluating Named Entity Recognition systems that evaluates named entities as they are typed on a sentence level . they propose to adapt existing evaluation setups to suit the new setup . |
| Outcome: | The proposed setup shows that the best systems that are evaluated on each token after its typed reach performance within 1–5 F1 points of systems that were evaluated at the end of the sentence. |
SANDI: Story-and-Images Alignment (2021.eacl-main)
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| Challenge: | a method for selecting images from an image collection and aligning them with text paragraphs of a story is presented . judiciously placed images are used for multimodal descriptions and narration in stories . |
| Approach: | They propose a method for automatically selecting images from an image collection and aligning them with text paragraphs of a story. |
| Outcome: | The proposed method can select and align images with texts with high quality of semantic fit. |
Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets (2021.eacl-main)
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| Challenge: | a recent study examines the ability of Open-Domain Question Answering models to produce answers to factoid questions . a large number of models have been used to study the performance of open-domain QA datasets . |
| Approach: | They evaluate open-domain question answering models to see what they can generalize . they find that all models perform substantially worse on questions that cannot be memorized from train sets . |
| Outcome: | The proposed model outperforms a closed-book QA model on the open-domain datasets. |
El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing (2021.eacl-main)
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| Challenge: | Code-switching (CS) is the alternation of languages within an utterance or conversation. |
| Approach: | They propose to use translation-and-align and augment with a generation model followed by match-and filter to improve CS generalizability of cross-lingual models when data for only one language is available. |
| Outcome: | The proposed models improve when only English data is available alongside zero or a few CS training instances. |
Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs (2021.eacl-main)
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| Challenge: | Paraphrase generation requires many annotated paraphrase pairs, which are expensive to obtain. |
| Approach: | They propose a model that learns to disentangle the semantics and syntax of a sentence from unannotated texts. |
| Outcome: | The proposed model learns to disentangle the semantics and syntax of a sentence from a collection of unannotated texts. |
Data Augmentation for Hypernymy Detection (2021.eacl-main)
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| Challenge: | Existing methods for supervised inference have limited quality training data. |
| Approach: | They propose two techniques which generate new training examples from existing ones . they combine linguistic principles of hypernym transitivity and intersective modifier-noun composition . |
| Outcome: | The proposed techniques generate new training examples from existing datasets. |
Few-shot learning through contextual data augmentation (2021.eacl-main)
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| Challenge: | Various strategies have been explored to learn from a journalist's post-edits . state-of-the-art APE systems require large numbers of post- edits for training . |
| Approach: | They propose to teach a pre-trained machine translation model to translate previously unseen words accurately . they extend a data augmentation approach to create training examples with similar contexts . |
| Outcome: | The proposed model improves accuracy on the scale of one to five examples with only 1 to 5 examples. |
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering (2021.eacl-main)
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| Challenge: | Existing methods for Dialog State Tracking do not generalize well to new domains and unseen slots. |
| Approach: | They propose an ontology-free framework that queries for unseen constraints and slots in multi-domain task-oriented dialogs using a conditional language model pre-trained on substantive English sentences. |
| Outcome: | The proposed framework improves goal accuracy in zero-shot domain adaptation settings by up to 9% over the previous state-of-the-art on the MultiWOZ 2.1 dataset. |
Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation (2021.eacl-main)
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| Challenge: | Recent advances in neural retrieval have led to advancements on document, passage and knowledge-base benchmarks. |
| Approach: | They propose an approach to zero-shot learning for passage retrieval that uses synthetic question generation to close this gap. |
| Outcome: | The proposed approach can exceed term-based techniques on document retrieval benchmarks by using domain-targeted synthetic question generation. |
Discourse-Aware Unsupervised Summarization for Long Scientific Documents (2021.eacl-main)
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| Challenge: | Existing extractive models for short news summarization are weak, despite recent advances in abstractive summarizing. |
| Approach: | They propose an unsupervised graph-based ranking model that uses a hierarchical graph representation to determine sentence importance. |
| Outcome: | The proposed model outperforms strong unsupervised baselines by wide margins in automatic metrics and human evaluation. |
MIDAS: A Dialog Act Annotation Scheme for Open Domain HumanMachine Spoken Conversations (2021.eacl-main)
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| Challenge: | Existing dialog act schemes are designed for human-human conversations, but are not suitable for automatic speech recognition. |
| Approach: | They propose a dialog act annotation scheme for open-domain human-machine conversations . they collected 24K utterances from a large open- domain spoken conversation dataset . |
| Outcome: | The proposed scheme achieves an F1 score of 0.79 on a 24K spoken conversation dataset. |
Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models (2021.eacl-main)
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| Challenge: | a large-scale unsupervised pretraining has been shown to greatly boost the performance of natural language processing models. |
| Approach: | They propose an intuitive finetuning strategy to regularize the finetune process . they propose a mix-review strategy to alleviate the forgetting problem . |
| Outcome: | The proposed strategy regularizes the finetuning process, and the forgetting problem is alleviated . the proposed strategy also improves the performance of the resulting model . |
Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yolóxochitl Mixtec (2021.eacl-main)
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Jiatong Shi, Jonathan D. Amith, Rey Castillo García, Esteban Guadalupe Sierra, Kevin Duh, Shinji Watanabe
| Challenge: | End-to-end ASR systems that eschew linguistic resources but are more dependent on large-data settings are suggested as a solution to EL documentation bottlenecks. |
| Approach: | They propose to build an end-to-end ASR system that is reproducible by the ASR community and propose a novice transcription correction task. |
| Outcome: | The proposed method would mitigate bottlenecks and shortages in transcribers . it is based on a Yoloxóchitl Mixtec corpus and is reproducible by the ASR community. |
Mode Effects’ Challenge to Authorship Attribution (2021.eacl-main)
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| Challenge: | Existing studies on authorship attribution have shown that authorial style changes with respect to sentence length, word use, readability, and certain part-of-speech ratios. |
| Approach: | They propose to measure the effect of writing mode on authorial style in a corpus of documents composed online and offline using a traditional word processor. |
| Outcome: | The authors show that online writing differs from offline writing in terms of sentence length, word use, readability, and certain part-of-speech ratios. |
Generative Text Modeling through Short Run Inference (2021.eacl-main)
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| Challenge: | Latent variable models for text capture global semantic and syntactic features when trained correctly. |
| Approach: | They propose a short run dynamics for inference that initializes from the prior distribution of the latent variable and runs a small number of Langevin dynamics steps guided by its posterior distribution. |
| Outcome: | The proposed model is able to generate coherent sentences with smooth transition and shows no sign of posterior collapse. |
Detecting Extraneous Content in Podcasts (2021.eacl-main)
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| Challenge: | Podcast episodes often contain extraneous material interleaved within the audio and the written descriptions . authors present classifiers that leverage both textual and listening patterns to detect such content . |
| Approach: | They propose a classifier that leverages both textual and listening patterns to detect extraneous material in podcast descriptions and audio transcripts. |
| Outcome: | The proposed classifiers improve ROUGE scores and reduce extraneous content in podcast summarization tasks. |
Randomized Deep Structured Prediction for Discourse-Level Processing (2021.eacl-main)
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| Challenge: | Expressive text encoders have been at the center of recent NLP work . however, some tasks require complex structural dependencies between texts . |
| Approach: | They propose to leverage deep structured prediction and expressive neural encoders for argumentation mining tasks. |
| Outcome: | The proposed framework can be used for argumentation mining tasks without expensive inference tools. |
Automatic Data Acquisition for Event Coreference Resolution (2021.eacl-main)
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| Challenge: | lexical paraphrases and high precision rules informed by news discourse structure can be used to collect coreferential and non-coreferential event pairs from unlabeled English news articles. |
| Approach: | They propose to use lexical paraphrases and news discourse structure to automatically collect coreferential and non-coreferential event pairs from unlabeled English news articles. |
| Outcome: | The proposed model performs better than the supervised model on evaluation datasets with different event domains and text genres. |
Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network (2021.eacl-main)
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| Challenge: | Existing approaches for table annotation with entities and types capture the syntactic structure of tables using graphical models or learn embeddings of table entries without accounting for the complete syntaktic structure. |
| Approach: | They propose a Graph Convolutional Network that captures the complete structure of tables, knowledge graph and the training annotations and jointly learns embeddings for table elements as well as the entities and types. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods on 5 benchmark datasets while showing promising performance on downstream table-related applications. |
Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation (2021.eacl-main)
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Wanrong Zhu, Xin Wang, Tsu-Jui Fu, An Yan, Pradyumna Narayana, Kazoo Sone, Sugato Basu, William Yang Wang
| Challenge: | Outdoor vision-and-language navigation (VLN) tasks require visual grounding to generate correct actions. |
| Approach: | They propose a multimodal text style transfer learning approach to mitigate data scarcity in outdoor vision-and-language navigation tasks. |
| Outcome: | The proposed approach outperforms baseline models on the outdoor vision-and-language navigation task, improving task completion rate by 8.7% relative to the baseline models. |
ECOL-R: Encouraging Copying in Novel Object Captioning with Reinforcement Learning (2021.eacl-main)
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| Challenge: | Novel Object Captioning is a zero-shot Image Caption task requiring describing objects not seen in the training captions, but for which information is available from external object detectors. |
| Approach: | They propose a novel captioning model that encourages copying of object labels with reinforcement learning that encourage a copy-augmented transformer model to accurately describe the object labels. |
| Outcome: | The proposed model sets new state-of-the-art on the nocaps and held-out COCO benchmarks. |
Enriching Non-Autoregressive Transformer with Syntactic and Semantic Structures for Neural Machine Translation (2021.eacl-main)
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| Challenge: | Existing non-autoregressive models have boosted the efficiency of neural machine translation, but their performance is significantly worse than that of autoregressive counterparts. |
| Approach: | They propose to incorporate syntactic and semantic structures among natural languages into a non-autoregressive Transformer for the task of neural machine translation. |
| Outcome: | The proposed model achieves faster speed and keeps translation quality compared with other models. |
NLQuAD: A Non-Factoid Long Question Answering Data Set (2021.eacl-main)
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| Challenge: | Existing data sets for document-level question answering are limited in their ability to detect short text and require multiple-sentence descriptive answers and opinions. |
| Approach: | They introduce a new data set with baseline methods for non-factoid long question answering . they compare BERT, RoBERTa, and Longformer models to establish baseline performances . |
| Outcome: | Experimental results show that Longformer outperforms the other architectures but human evaluations show that it is far behind the human upper bound. |
Debiasing Pre-trained Contextualised Embeddings (2021.eacl-main)
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| Challenge: | a study of contextualised word embeddings shows discriminative biases are encoded in contextualised embeddables. |
| Approach: | They propose a fine-tuning method that can be applied at token- or sentence-levels to debias pre-trained contextualised embeddings. |
| Outcome: | The proposed method can be applied at token- or sentence-levels to debias pre-trained models without requiring retrains. |
Language Models for Lexical Inference in Context (2021.eacl-main)
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| Challenge: | Lexical inference in context (LIiC) is a variant of the natural language inference task focused on lexical semantics. |
| Approach: | They propose three approaches based on pretrained language models for LIiC . they propose a few-shot NLI classifier and a relation induction approach based upon handcrafted patterns expressing the semantics of lexical inference. |
| Outcome: | The proposed approaches outperform the previous state of the art and show their potential for LIiC. |
Few-Shot Semantic Parsing for New Predicates (2021.eacl-main)
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| Challenge: | a recent study shows that state-of-the-art neural semantic parsers are less accurate when there is only a handful of utterance-logical form pairs per predicate. |
| Approach: | They propose to use a meta-learning method to train a few-shot learning problem . they also propose to regularize attention scores with alignment statistics and apply a smoothing technique . |
| Outcome: | The proposed method outperforms baselines in one and two-shot settings. |
Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models (2021.eacl-main)
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| Challenge: | Existing dialog system models require extensive human annotations and are difficult to generalize to different tasks. |
| Approach: | They propose a framework that uses pre-trained language to model each speaker separately . it can be generalized to more challenging, non-collaborative tasks such as persuasion . |
| Outcome: | The proposed framework outperforms or is on par with state-of-the-art methods on two popular datasets: CamRest676 and MultiWOZ. |
On the Evaluation of Vision-and-Language Navigation Instructions (2021.eacl-main)
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| Challenge: | Existing instruction generators have not been evaluated using human wayfinders . BLEU, ROUGE, METEOR and CIDEr are ineffective for evaluating grounded navigation instructions. |
| Approach: | They propose an instruction-trajectory compatibility model that operates without reference instructions to improve wayfinding performance. |
| Outcome: | The proposed model shows the highest correlation with human wayfinding outcomes when scoring individual instructions. |
Cross-lingual Visual Pre-training for Multimodal Machine Translation (2021.eacl-main)
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Ozan Caglayan, Menekse Kuyu, Mustafa Sercan Amac, Pranava Madhyastha, Erkut Erdem, Aykut Erdem, Lucia Specia
| Challenge: | Pre-trained language models have been shown to improve performance in many natural language tasks. |
| Approach: | They propose to combine cross-lingual and visual pre-training to learn visually-grounded cross-linguistic representations using masked region classification and three-way parallel vision & language corpora. |
| Outcome: | The proposed models obtain state-of-the-art performance when fine-tuned for multimodal machine translation. |
Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation (2021.eacl-main)
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| Challenge: | Public datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP). |
| Approach: | They identify leakage of training data into test data on several publicly available datasets used to evaluate NLP tasks, including named entity recognition and relation extraction. |
| Outcome: | The proposed model can memorize and generalize data on several publicly available datasets and is compared against previously unseen data. |
An Expert Annotated Dataset for the Detection of Online Misogyny (2021.eacl-main)
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| Challenge: | Existing studies have found that misogynistic content is pervasive on some Reddit communities, but a training dataset for misogorical classification has not been created with the data. |
| Approach: | They propose a hierarchical taxonomy and an expert labelled dataset to enable automatic classification of online misogynistic content. |
| Outcome: | The proposed taxonomy and an expert labelled dataset are made freely available for future research. |
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia (2021.eacl-main)
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| Challenge: | a new approach to extract parallel sentences from Wikipedia articles is proposed . the approach is based on multilingual sentence embeddings, but does not limit it to English . |
| Approach: | They propose to automatically extract parallel sentences from Wikipedia articles in 96 languages . they train neural MT baseline systems on the mined data and evaluate them on the TED corpus . |
| Outcome: | The proposed approach extracts parallel sentences from Wikipedia articles in 96 languages . the extracted sentences achieve strong BLEU scores for many language pairs . |
ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain (2021.eacl-main)
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| Challenge: | Using a novel annotation scheme, we identify anaphoric references in chemical patents and determine the chemical relation between linked entities. |
| Approach: | They propose a neural approach to anaphora resolution based on coreference and bridging links in chemical patents. |
| Outcome: | The proposed framework can be used to identify anaphoric references in chemical patents and determine the chemical relation between linked entities. |
Syntactic Nuclei in Dependency Parsing – A Multilingual Exploration (2021.eacl-main)
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| Challenge: | Existing models for syntactic dependency parsing assume words are elementary units that enter into dependency relations. |
| Approach: | They propose to use composition functions to make a transition-based dependency parser aware of the notion of nucleus. |
| Outcome: | The proposed concept of nucleus gives small but significant improvements in parsing accuracy on 12 languages. |
Searching for Search Errors in Neural Morphological Inflection (2021.eacl-main)
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| Challenge: | Neural sequence-to-sequence models are the predominant choice for language generation tasks. |
| Approach: | They find that on word-level tasks, the empty string is often the global optimum . they suggest that the poor calibration of many neural models may stem from characteristics of a specific subset of tasks rather than general ill-suitedness of such models for language generation. |
| Outcome: | The results suggest that the poor calibration of many neural models may stem from characteristics of a specific subset of tasks rather than general ill-suitedness of such models for language generation. |
Quantifying Appropriateness of Summarization Data for Curriculum Learning (2021.eacl-main)
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| Challenge: | Summarization datasets are noisy, and summaries often do not reflect what is written in the source texts. |
| Approach: | They propose a method of curriculum learning to train summarization models from noisy data. |
| Outcome: | The proposed method improves the performance of pretrained and non-pretrained models on human evaluation. |
Evaluating language models for the retrieval and categorization of lexical collocations (2021.eacl-main)
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| Challenge: | Lexical collocations are idiosyncratic combinations of two syntactically bound lexical items. |
| Approach: | They perform an exhaustive analysis of current language models for collocation understanding . they first construct a dataset of apparitions of lexical collocations in context . |
| Outcome: | The proposed models perform well in distinguishing light verb constructions, especially if the collocation’s first argument acts as subject, but often fail to distinguish, first, different syntactic structures within the same semantic category, and second, fine-grained semantic categories which restrict the use of small sets of valid collocates for a given base. |
BART-TL: Weakly-Supervised Topic Label Generation (2021.eacl-main)
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| Challenge: | Existing methods for topic labeling use weak labelers to train rankers . recent studies show that weakly-supervised methods can produce meaningful labels . |
| Approach: | They propose a weakly-supervised method for assigning topic labels to models by using weak labelers. |
| Outcome: | The proposed model can generate valuable and novel labels in a weakly-supervised manner and can be improved by adding other weak labelers or distant supervision on similar tasks. |
Dynamic Graph Transformer for Implicit Tag Recognition (2021.eacl-main)
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| Challenge: | Existing studies focus on using explicit information in articles and do not consider the implicit information. |
| Approach: | They propose a dynamic graph transformer that distills the textual information and the entity relations on the fly. |
| Outcome: | The proposed model can extract the textual information and the entity relations on the fly. |
Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning (2021.eacl-main)
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| Challenge: | Existing approaches to language modeling use autoregressive methods, but they can produce repetitive results. |
| Approach: | They propose to add a loss function for regularization to avoid unwanted properties, such as contradiction or repetition, to a language model by using policy gradient reinforcement learning. |
| Outcome: | The proposed method reduces repetition without impacting the language model quality. |
Civil Rephrases Of Toxic Texts With Self-Supervised Transformers (2021.eacl-main)
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| Challenge: | et al., 2018a): a poor phrasing may make the conversation go awry. |
| Approach: | They propose a model that can help suggest rephrasings of toxic comments in a more civil manner. |
| Outcome: | The proposed model generates sentences that are more fluent and better at preserving the initial content compared to earlier systems and human evaluation. |
Generating Weather Comments from Meteorological Simulations (2021.eacl-main)
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Soichiro Murakami, Sora Tanaka, Masatsugu Hangyo, Hidetaka Kamigaito, Kotaro Funakoshi, Hiroya Takamura, Manabu Okumura
| Challenge: | generating weather-forecast comments from meteorological simulations is labor intensive and requires a solid knowledge of meteorology. |
| Approach: | They propose a data-to-text model that incorporates three types of encoders for numerical forecast maps, observation data, and meta-data. |
| Outcome: | The proposed model performs best against baselines in terms of informativeness . it is available online and the results are available to the general public . |
SICK-NL: A Dataset for Dutch Natural Language Inference (2021.eacl-main)
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| Challenge: | Having a parallel dataset for Natural Language Inference in Dutch is problematic for some NLP systems. |
| Approach: | They propose to translate a SICK dataset from English into Dutch to compare models for both languages. |
| Outcome: | The proposed dataset compares models on English and Dutch on two tasks. |
A phonetic model of non-native spoken word processing (2021.eacl-main)
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| Challenge: | Compared to native speakers, non-native speakers perform differently in a variety of tasks related to auditory language processing, both at the phone and at the word level. |
| Approach: | They train a computational model of phonetic learning which has no access to phonology on either one or two languages and test it on a spoken word processing task. |
| Outcome: | The proposed model exhibits predictable behaviors on phone-level and word-level discrimination tasks and on a spoken word processing task. |
Bootstrapping Relation Extractors using Syntactic Search by Examples (2021.eacl-main)
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| Challenge: | Existing methods for supervised relation extraction still require a large quantity of training data. |
| Approach: | They propose a process for bootstrapping training datasets which can be performed quickly by non-NLP-experts. |
| Outcome: | The proposed method outperforms models trained on manual and distant data augmentation techniques and the search-based approach with the NLG method. |
Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMR (2021.eacl-main)
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| Challenge: | Abstract meaning representations are typically evaluated using surface matching metrics . however, there are problems with these metrics, since they allow multiple surface realizations . |
| Approach: | They propose a decomposable metric that measures the distance between the original and reconstructed AMRs. |
| Outcome: | The proposed metric reduces the complexity of the evaluation process by allowing for multiple surface realizations. |
The Source-Target Domain Mismatch Problem in Machine Translation (2021.eacl-main)
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Jiajun Shen, Peng-Jen Chen, Matthew Le, Junxian He, Jiatao Gu, Myle Ott, Michael Auli, Marc’Aurelio Ranzato
| Challenge: | Despite the interconnected world we live in, people in different places talk about different things in different parts of the world. |
| Approach: | They propose a metric to quantify the effect of local context in machine translation and propose measurable results. |
| Outcome: | The proposed metric can be used to quantify the effect of local context on the use of language in machine translation systems on low resource languages. |
Cross-Topic Rumor Detection using Topic-Mixtures (2021.eacl-main)
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| Challenge: | Existing work on rumor detection models has explored network structures, propagation paths, user credibility and fusion of heterogeneous data. |
| Approach: | They propose a method that adapts a rumor detection model trained on source to target topics to make rumour predictions. |
| Outcome: | The proposed method outperforms baseline debiasing methods in a cross-topic setting. |
Understanding Pre-Editing for Black-Box Neural Machine Translation (2021.eacl-main)
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| Challenge: | a study has demonstrated the effectiveness of pre-editing for black-box neural MT, but a deep understanding of what it is and how it works for black box NMT is lacking. |
| Approach: | They investigated 6,652 instances of pre-editing across three translation directions, two MT systems and four text domains. |
| Outcome: | The proposed method can be used in MT systems with black-box neural MT (NMT) but it is not yet fully understood in the literature. |
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding (2021.eacl-main)
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| Challenge: | Existing methods for embedding entities and relations in knowledge graphs are heuristically motivated and theoretical understanding of such embeddables is underdeveloped. |
| Approach: | They extend the random walk model of word embeddings to Knowledge Graph Embeddings (KGEs) they propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph. |
| Outcome: | The proposed learning objective is motivated by the theoretical analysis to learn KGEs from a given knowledge graph. |
Few-shot Learning for Slot Tagging with Attentive Relational Network (2021.eacl-main)
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| Challenge: | Recent studies have used metric-based learning in computer vision but not slot tagging. |
| Approach: | They propose a metric-based learning architecture that extends relation networks by leveraging pretrained contextual embeddings such as ELMO and BERT and by using attention mechanism. |
| Outcome: | The proposed method outperforms state-of-the-art methods on SNIPS data on a slot tagging task with a large amount of hand-labeled data. |
SpanEmo: Casting Multi-label Emotion Classification as Span-prediction (2021.eacl-main)
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| Challenge: | Current approaches to ER ignore potential ambiguities, in which multiple emotions overlap. |
| Approach: | They propose a model "SpanEmo" which casts multi-label emotion classification as span-prediction and introduces a loss function focused on modelling multiple co-existing emotions in a sentence. |
| Outcome: | The proposed model can predict multiple co-existing emotions in a sentence and improve model performance and learning meaningful associations between labels and words in the sentence. |
Exploiting Position and Contextual Word Embeddings for Keyphrase Extraction from Scientific Papers (2021.eacl-main)
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| Challenge: | Existing methods for keyphrase extraction are either supervised or unsupervised. |
| Approach: | They propose an unsupervised algorithm that exploits contextual word embeddings and positional information to create a biased PageRank. |
| Outcome: | The proposed algorithm outperforms previous approaches and strong baselines on five benchmark datasets. |
Benchmarking Machine Reading Comprehension: A Psychological Perspective (2021.eacl-main)
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| Challenge: | MRC is a task that tests the ability of a machine to read and understand unstructured text. |
| Approach: | They propose a theoretical basis for the design of MRC datasets based on psychology and psychometrics and propose shortcut-proof questions and explanations as a part of the task design. |
| Outcome: | The proposed datasets should evaluate the model's ability to understand context-dependent situations and ensure substantive validity by shortcut-proof questions and explanation as a part of the task design. |
Multilingual Neural Machine Translation with Deep Encoder and Multiple Shallow Decoders (2021.eacl-main)
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| Challenge: | Recent work in multilingual translation has improved translation quality surpassing bilingual baselines using deep transformer models with increased capacity. |
| Approach: | They propose a deep encoder with multiple shallow decoders to reduce inference latency while maintaining translation quality. |
| Outcome: | The proposed model achieves 1.8x speedup on average compared to a standard transformer model with no drop in translation quality. |
With Measured Words: Simple Sentence Selection for Black-Box Optimization of Sentence Compression Algorithms (2021.eacl-main)
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| Challenge: | Sentence Compression is the task of generating a shorter, yet grammatical, version of a given sentence, preserving the essence of the original sentence. |
| Approach: | They propose a Black-Box Optimizer for Compression to find the best candidates for compression . they use a black-box compression algorithm to predict how well each sentence could be compressed . |
| Outcome: | The proposed algorithm improves both accuracy and Rouge-F1-score on three datasets. |
WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Context (2021.eacl-main)
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| Challenge: | Existing benchmarks for Word Sense Disambiguation are limited to those systems in which sense distinctions are defined according to an underlying sense inventory. |
| Approach: | They propose a framework for Target Sense Verification of Words in Context which grounds its uniqueness as binary classification task and independent of external sense inventories. |
| Outcome: | The proposed framework is highly flexible for evaluation of diverse models and systems in and across domains. |
Self-Supervised and Controlled Multi-Document Opinion Summarization (2021.eacl-main)
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| Challenge: | Existing unsupervised methods for summarizing reviews are based on bootstrapping and require a combination of loss functions or hierarchical latent variables to ensure that the generated summaries remain on-topic. |
| Approach: | They propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. |
| Outcome: | The proposed setup makes training simpler than previous approaches by relying only on standard log-likelihood loss and mainstream models. |
NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles (2021.eacl-main)
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| Challenge: | Previous work on target-dependent sentiment classification (TSC) has focused on reviews, social media, and other domains where authors tend to express their opinions explicitly. |
| Approach: | They propose a high-quality dataset for TSC on news articles with key differences compared to established datasets. |
| Outcome: | The proposed model improves the state-of-the-art from 81.7 to 83.1 (real-world sentiment distribution) and 82.5 (multi-target sentences) compared to established datasets. |
Cross-lingual Contextualized Topic Models with Zero-shot Learning (2021.eacl-main)
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| Challenge: | Existing topic models are language-specific and cannot be transferred in a transferable manner. |
| Approach: | They propose a zero-shot cross-lingual topic model that learns topics on one language and predicts them for unseen documents in different languages. |
| Outcome: | The proposed model learns topics on one language and predicts them for unseen documents in different languages. |
Dependency parsing with structure preserving embeddings (2021.eacl-main)
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| Challenge: | Modern neural approaches to dependency parsing are trained to predict a tree structure by learning a contextual representation for tokens in a sentence and a head–dependent scoring function. |
| Approach: | They propose to combine a contextual representation for tokens and a head–dependent scoring function to learn interpretable representations by training a parser to explicitly preserve structural properties of a tree. |
| Outcome: | The proposed approach yields strong tree distance preservation and parsing performance on par with a competitive graph-based parser. |
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates (2021.eacl-main)
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Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova, Denis Belyakov, Daniil Larionov, Nikita Khromov, Olga Kozlova, Ekaterina Artemova, Dmitry V. Dylov, Alexander Panchenko
| Challenge: | Annotating training data for sequence tagging of texts is usually very time-consuming . active learning can help to reduce the amount of annotation required to train a good model by multiple times . |
| Approach: | They are the first to thoroughly investigate active learning and transfer learning for natural language processing . they propose to combine active learning with active learning to improve model acquisition . |
| Outcome: | The proposed combination of active learning and Bayesian uncertainty estimation improves performance and reduces obstacles for applying it in practice. |
MultiHumES: Multilingual Humanitarian Dataset for Extractive Summarization (2021.eacl-main)
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| Challenge: | a new multilingual summarization model is being developed to help humanitarian experts process large amounts of secondary data to derive situational awareness and guide decision-making. |
| Approach: | They propose to use multilingual documents and annotated snippets to improve extraction of secondary data for humanitarian response experts. |
| Outcome: | The proposed model provides multilingual documents with informative snippets that have been annotated by humanitarian analysts over the past four years. |
Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale (2021.eacl-main)
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| Challenge: | Existing research on predicting argument quality based on subjective assessments of human annotators ignores this limitation. |
| Approach: | They propose to compare different revisions of the same claim to assess their quality . they use logistic regression and transformer-based neural networks to learn quality indicators . |
| Outcome: | The proposed tasks show that the learned indicators generalize well across topics. |
Few Shot Dialogue State Tracking using Meta-learning (2021.eacl-main)
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| Challenge: | Existing methods for transferring knowledge from resource-rich domains to unknown domains are data hungry . a meta-learning algorithm is proposed to solve the problem of zero/few-shot DST . |
| Approach: | They propose a meta-learner for the problem of zero/few-shot DST . they propose to agnostically train any existing chatbot system to improve its performance . |
| Outcome: | The proposed meta-learner improves on baseline in a low-data setting. |
BERT Prescriptions to Avoid Unwanted Headaches: A Comparison of Transformer Architectures for Adverse Drug Event Detection (2021.eacl-main)
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| Challenge: | Pretrained transformer-based models are a common choice for identifying drug events from social media texts. |
| Approach: | They propose to compare transformer-based models with in-domain language pretraining to find out which one is better at ADE detection. |
| Outcome: | The proposed models outperform SpanBERT and PubMedBERT on two benchmarks. |
Semantic Parsing of Disfluent Speech (2021.eacl-main)
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| Challenge: | Semantic parsing is a key component for understanding user utterances in voice assistants . however, most research on disfluent speech is focused on written text . |
| Approach: | They investigate semantic parsing of disfluent speech with the ATIS dataset . they add real and synthetic disfluencies at training time to improve model performance . |
| Outcome: | The proposed parser outperforms the state-of-the-art parsers on the ATIS dataset in terms of performance and accuracy. |
Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models (2021.eacl-main)
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| Challenge: | Existing calibration methods rescale posterior distributions of classifiers after training. |
| Approach: | They propose to use a noise contrastive estimation technique to train an energy-based model during finetuning of pretrained text encoders. |
| Outcome: | The proposed model can reach a better calibration competitive to strong baselines with little or no loss in accuracy. |
What Sounds “Right” to Me? Experiential Factors in the Perception of Political Ideology (2021.eacl-main)
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| Challenge: | a recent study has suggested that political ideology is inherently built into text . a new study examines the impact of experiential factors on annotator perceptions of political ideology . |
| Approach: | They propose to investigate the impact of experiential factors on annotator perceptions of political ideology by analyzing an annotated corpus of political discussion in the U.S. They find that these factors may influence consistency of how political ideologies are perceived by annotators. |
| Outcome: | The findings challenge the assumption that political ideology is built into text . they show that experiential factors may influence how ideologies are perceived . |
Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries (2021.eacl-main)
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| Challenge: | Pretrained language models have been suggested as an alternative or complement to structured knowledge bases . however, this paradigm has only been considered in a very limited setting . |
| Approach: | They propose a paradigm that allows LMs to store a large number of entities . they propose LM-as-KB paradigm which allows querying stored facts . |
| Outcome: | The proposed paradigm allows handling 21k entities whose name is found in common LM vocabularies . the proposed paradigm has only been considered in a very limited setting . |
Globalizing BERT-based Transformer Architectures for Long Document Summarization (2021.eacl-main)
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| Challenge: | Existing approaches to fine-tune a large language model on downstream tasks show several limitations when the target task requires to reason with long documents. |
| Approach: | They propose a hierarchical approach where the input is divided in multiple blocks independently processed by the scaled dot-attentions and combined between the successive layers. |
| Outcome: | The proposed approach performs well on three extractive summarization corpora of scientific papers and news articles. |
Through the Looking Glass: Learning to Attribute Synthetic Text Generated by Language Models (2021.eacl-main)
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| Challenge: | Recent advances in natural language processing have enabled synthetic text generation that is often comparable to the organic text. |
| Approach: | They propose and test several ML-based methods to attribute authorship of synthetic text to language models (LMs) they propose to use a fine-tuned version of XLNet to achieve excellent accuracy . |
| Outcome: | The proposed method achieves excellent accuracy (91% to near perfect 98%) across a range of experiments where the synthetic text may be generated using pre-trained LMs, fine-tuned LM, or by varying text generation parameters. |
We Need To Talk About Random Splits (2021.eacl-main)
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| Challenge: | argued that random splits, like standard splits lead to overly optimistic performance estimates. |
| Approach: | They argue that random splits, like standard splits lead to overly optimistic performance estimates. |
| Outcome: | The proposed method leads to more realistic performance estimates than standard splits. |
How Certain is Your Transformer? (2021.eacl-main)
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Artem Shelmanov, Evgenii Tsymbalov, Dmitri Puzyrev, Kirill Fedyanin, Alexander Panchenko, Maxim Panov
| Challenge: | Obtaining reliable uncertainty estimations for such neural networks (NNs) is challenging due to the huge number of parameters in these deep learning models. |
| Approach: | They propose to use Monte Carlo dropout to estimate uncertainty for Transformer-based models and construct inexpensive estimates using Determinantal Point Processes. |
| Outcome: | The proposed estimates improve the quality of detection of error-prone instances. |
Alignment verification to improve NMT translation towards highly inflectional languages with limited resources (2021.eacl-main)
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| Challenge: | Existing approaches to improve translation quality using limited training data are phrase-based and syntax-based approaches. |
| Approach: | They propose to combine a neural MT system with an open source module to improve translation quality. |
| Outcome: | The proposed method improves translation quality over the best individual NMT and the standard ensemble system provided in the Marian-NMT system. |
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase (2021.eacl-main)
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| Challenge: | a data augmentation technique is used to boost performance on spoken language understanding tasks. |
| Approach: | They propose a data augmentation technique based on byte pair encoding and a BERT-like self-attention model to boost performance on spoken language understanding tasks. |
| Outcome: | The proposed method performs well on domain and intent classification tasks for a voice assistant and in a user-study focused on utterance naturalness and semantic similarity. |
How to Evaluate a Summarizer: Study Design and Statistical Analysis for Manual Linguistic Quality Evaluation (2021.eacl-main)
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| Challenge: | Current manual evaluation methods for text summarization have low correlation with human judgements on summary quality. |
| Approach: | They conduct two evaluation experiments on two aspects of summaries’ linguistic quality (coherence and repetitiveness) they find that study parameters such as the total number of annotators and distribution of annotes to annotation items are often not fully reported. |
| Outcome: | The proposed methods can inflate type I errors up to eight-fold and the overall number of annotators can have a strong impact on study power. |
Open-Mindedness and Style Coordination in Argumentative Discussions (2021.eacl-main)
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| Challenge: | Previous research has shown that linguistic accommodation correlates with gaps in the power and status of the speakers and the way it promotes approval and discussion efficiency. |
| Approach: | They propose a novel perspective on linguistic accommodation, exploring its correlation with the open-mindedness of a speaker, rather than to her social status. |
| Outcome: | The proposed approach improves the open-mindedness of a speaker and lowers discussion efficiency. |
Error Analysis and the Role of Morphology (2021.eacl-main)
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| Challenge: | Using morphological features does improve error prediction across tasks, but is less pronounced in morphology-complex languages. |
| Approach: | They propose to use morphological features to improve error prediction across four different tasks and up to 57 languages to test their hypothesis. |
| Outcome: | The proposed model is more discriminative in morphologically simple languages than in simple ones. |
Applying the Transformer to Character-level Transduction (2021.eacl-main)
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| Challenge: | morphological inflection generation and historical text normalization tasks are character-level tasks that outperform recurrent models. |
| Approach: | They propose a technique to handle feature-guided character-level transduction that further improves performance. |
| Outcome: | The transformer outperforms recurrent models on morphological inflection and historical text normalization tasks. |
Exploring Supervised and Unsupervised Rewards in Machine Translation (2021.eacl-main)
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| Challenge: | Autoregressive sequence-to-sequence (seq2sequ) neural architectures have become the de facto approach in Machine Translation (MT). |
| Approach: | They propose to make models less reliant on cross-entropy loss and evaluation metrics . they propose an entropicity-regularised RL method that explores the action space . |
| Outcome: | The proposed method exploits the action space and unsupervised reward function to balance between exploration and exploitation. |
Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions (2021.eacl-main)
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| Challenge: | Populist rhetoric has risen across the political sphere in recent years, but computational approaches to it have been scarce. |
| Approach: | They propose a dataset of 6861 reddit comments annotated for populist attitudes and a set of multi-task learning models that leverage emotion and group identification as auxiliary tasks. |
| Outcome: | The proposed models leverage emotion and group identification as auxiliary tasks to model populist rhetoric tasks. |
Multilingual Entity and Relation Extraction Dataset and Model (2021.eacl-main)
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| Challenge: | HERBERTa is a pipeline for a multilingual task involving two separate BERT models. |
| Approach: | They propose a dataset and a model that combines two independently pretrained BERT models for a multilingual setting to approach the task of Joint Entity and Relation Extraction. |
| Outcome: | The proposed dataset achieves micro F1 81.49 for English on the SMiLER dataset . the proposed pipeline is close to the current SOTA on CoNLL, SpERT . |
A New View of Multi-modal Language Analysis: Audio and Video Features as Text “Styles” (2021.eacl-main)
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| Challenge: | Fig. 1 shows how style-transferred multi-modal features can be used in sentiment analysis and emotion recognition. |
| Approach: | They propose to use adaptive normalization to impose style onto text to learn richer representations for multi-modal utterances. |
| Outcome: | The proposed model achieves performance on par with state-of-the-art but using less than a third of the model parameters. |
Multilingual and cross-lingual document classification: A meta-learning approach (2021.eacl-main)
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| Challenge: | Existing methods to document classification in low-resource languages are under-resourced . 6% of the world's languages are spoken, and many have inadequate resources . |
| Approach: | They propose a meta-learning approach to document classification in low-resource languages . they propose 'nuclear-shot' cross-lingual adaptation to previously unseen languages based on limited data . |
| Outcome: | The proposed method performs on-par on some languages while under-resourced in others. |
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies (2021.eacl-main)
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| Challenge: | Biomedical question-answering (QA) provides users with high-quality information from a vast scientific literature. |
| Approach: | They propose to use a biomedical entity-aware masking strategy to fine-tune masked language models to their domains. |
| Outcome: | The proposed approach is an adaptation process for masked LMs, not memory or components. |
Attention-based Relational Graph Convolutional Network for Target-Oriented Opinion Words Extraction (2021.eacl-main)
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| Challenge: | empirical results show that our model significantly outperforms all existing models on four benchmark datasets. |
| Approach: | They propose a novel attention-based relational graph convolutional neural network to exploit syntactic information over dependency graphs. |
| Outcome: | The proposed model outperforms existing models on four benchmark datasets. |
“Laughing at you or with you”: The Role of Sarcasm in Shaping the Disagreement Space (2021.eacl-main)
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| Challenge: | Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. |
| Approach: | They propose to use a corpus annotated with argumentative moves and sarcasm to model sarcastic relationships using deep learning architectures. |
| Outcome: | The proposed setup improves the argumentative relation classification task using deep learning architectures. |
Learning Relatedness between Types with Prototypes for Relation Extraction (2021.eacl-main)
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| Challenge: | Existing datasets have no intrinsic Ontology for relation types. |
| Approach: | They propose to use prototypical examples to represent each relation type and use them to augment related types from a different dataset. |
| Outcome: | The proposed model improves on a baseline with multi-task learning between datasets to obtain better representation for relations. |
I Beg to Differ: A study of constructive disagreement in online conversations (2021.eacl-main)
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| Challenge: | Disagreements are pervasive in human communication. |
| Approach: | They construct a corpus of Wikipedia Talk page conversations that contain content disputes and define the task of predicting whether disagreements will be escalated to mediation by a moderator. |
| Outcome: | The proposed model outperforms feature-based models in predicting whether disagreements will escalate to mediation by a moderator. |
Acquiring a Formality-Informed Lexical Resource for Style Analysis (2021.eacl-main)
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| Challenge: | lexico-statistics analysis of formality levels in written communication has long been dominated by application concerns, such as authorship and plagiarism assignment problems. |
| Approach: | They propose a lexicon with entries ordered by their degree of (in)formality and let crowdworkers assess the enlarged set of lexical items on a continuous informal-formal scale as a gold standard for evaluation. |
| Outcome: | The proposed lexicon is evaluated on a German-language email corpus and is then evaluated by crowdworkers. |
Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents (2021.eacl-main)
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| Challenge: | Existing methods for event reason extraction are far from resolving this problem. |
| Approach: | They propose a task to extract causal explanations from document-level texts . they use a dataset FinReason for evaluation to provide Reasons annotation for financial events . |
| Outcome: | The proposed task performs better than existing methods on a dataset of 8,794 documents, 12,861 financial events and 11,006 reason spans. |
Language Modelling as a Multi-Task Problem (2021.eacl-main)
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| Challenge: | Using multitask learning, humans are optimising their behaviour towards a multitude of objectives to reach their goals in dayto-day life. |
| Approach: | They propose to study language modelling as a multi-task problem by examining the generalisation behaviour of language models as they learn the linguistic concept of Negative Polarity Items. |
| Outcome: | The proposed model is able to learn the linguistic concept of Negative Polarity Items (NPIs) and is a multi-task learning model. |
ChainCQG: Flow-Aware Conversational Question Generation (2021.eacl-main)
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| Challenge: | Current datasets for conversational question answering lack realistic, domain-specific training data. |
| Approach: | They propose a model that generates question-answer representations across dialogue turns . they use flow propagation training to improve conversational flow and fluidity . |
| Outcome: | The proposed model outperforms answer-aware and answer-unaware SOTA baselines significantly . it generates different types of questions with improved fluidity and coreference alignment. |
The Interplay of Task Success and Dialogue Quality: An in-depth Evaluation in Task-Oriented Visual Dialogues (2021.eacl-main)
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| Challenge: | chit-chat and task-oriented dialogue models are evaluated on their task success metric, but the best model is usually chosen based on task success. |
| Approach: | They compare models playing different games to find out which one is best . they find that this discrepancy is model- and task-agnostic . |
| Outcome: | The proposed model can generate utterances that are indistinguishable from human dialogues by learning to ground, encode, and decode words that do not occur frequently in the training set. |
“Are you kidding me?”: Detecting Unpalatable Questions on Reddit (2021.eacl-main)
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| Challenge: | Existing methods to detect online abuse focus on the more explicit forms of abuse . existing methods focus on detecting subtler forms of online abuse leaving them unnoticed . |
| Approach: | They propose a task to detect unpalatable questions using reddit data to implement a context-aware dataset and implement 'learning models' they hope future research will address subtle forms of abuse since harm passes unnoticed through existing detection systems. |
| Outcome: | The proposed task is based on a dataset of reddit users and a conversational context. |
Neural-Driven Search-Based Paraphrase Generation (2021.eacl-main)
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| Challenge: | Existing non-supervised paraphrase generation models are biased toward specific problems like question answering or image captioning. |
| Approach: | They propose a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical distance. |
| Outcome: | The proposed algorithms perform well against non-supervised baselines. |
Word Alignment by Fine-tuning Embeddings on Parallel Corpora (2021.eacl-main)
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| Challenge: | Existing work on word alignment has focused on unsupervised learning on parallel text. |
| Approach: | They propose to combine pre-trained contextualized word embeddings with multilingually trained language models to achieve competitive results on word alignment tasks. |
| Outcome: | The proposed model outperforms state-of-the-art models on five language pairs and can train multilingual word aligners that can obtain robust performance on different language pairs. |
Paraphrases do not explain word analogies (2021.eacl-main)
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| Challenge: | Several attempts have been made to explain distributional word embeddings as linguistic regularities as directions. |
| Approach: | They propose to use an analogy to explain why linguistic regularities should hold in distributional word embeddings. |
| Outcome: | The proposed explanation does not hold empirically. |
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games (2021.eacl-main)
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Alessandro Suglia, Yonatan Bisk, Ioannis Konstas, Antonio Vergari, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon
| Challenge: | Using guessing games, an artificial agent can learn to perform on novel downstream tasks such as Visual Question Answering (VQA). |
| Approach: | They propose a supervised learning scenario in which an agent learns to mimic successful guessing games and a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning. |
| Outcome: | The proposed model can be applied to a VQA dataset using a supervised learning scenario and a novel way for an agent to play by itself. |
A Unified Feature Representation for Lexical Connotations (2021.eacl-main)
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| Challenge: | ideological attitudes and stance are often expressed through subtle meanings of words and phrases. |
| Approach: | They propose a method for lexical representations that capture connotations within the embedding space . they define six new fine-grained connotation aspects for nouns and adjectives . |
| Outcome: | The proposed method improves stance detection when data is limited. |
FAST: Financial News and Tweet Based Time Aware Network for Stock Trading (2021.eacl-main)
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| Challenge: | Existing methods for stock movement prediction are limited and do not account for the fine-grain temporal irregularities in the release of large volumes of text. |
| Approach: | They propose a hierarchical, learning to rank approach that uses textual data to make time-aware predictions for ranking stocks based on expected profit. |
| Outcome: | The proposed method outperforms state-of-the-art methods by over 8% in terms of cumulative profit and risk-adjusted returns on two benchmarks: English tweets and Chinese financial news spanning two major stock indexes and four global markets. |
Building Representative Corpora from Illiterate Communities: A Reviewof Challenges and Mitigation Strategies for Developing Countries (2021.eacl-main)
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| Challenge: | Existing methods for collecting data from high-income countries (HICs) make implicit assumptions about literacy and internet access, but in low-income and sub-Saharan Africa (SSA) such assumptions may not hold for LICs where the bulk of the population lives. |
| Approach: | They propose a set of practical mitigation strategies to address the under-representation of illiterate communities in NLP corpora. |
| Outcome: | The proposed methods address the under-representation of illiterate communities in NLP corpora and propose mitigation strategies to help future work. |
Process-Level Representation of Scientific Protocols with Interactive Annotation (2021.eacl-main)
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| Challenge: | Existing efforts to automate wet lab workflows are focusing on graph-prediction models that capture both concrete, exact quantities ("30 minutes") and vague instructions ("swirl") |
| Approach: | They manually annotate PEGs in a corpus of complex lab protocols with a novel interactive textual simulator that keeps track of entity traits and semantic constraints during annotation. |
| Outcome: | The proposed graph-prediction models are good at entity identification and local relation extraction while addressing challenges such as cross-sentence relations and long-range coreference. |
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation (2021.eacl-main)
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| Challenge: | Existing studies have shown that existing models amplify biases observed in training data. |
| Approach: | They propose to use MT and NLP to amplify biases observed in training data to investigate how bias amplification might affect language in a broader sense. |
| Outcome: | The proposed model amplifys biases observed in training data and could lead to an artificially impoverished language, the authors show. |
First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT (2021.eacl-main)
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| Challenge: | Multilingual pretrained language models have demonstrated remarkable zero-shot cross-lingual transfer capabilities. |
| Approach: | They propose to use a layer ablation technique to create a multilingual model that is viewed as a stacking of two sub-networks: a language-agnostic encoder and a task-specific predictor. |
| Outcome: | The proposed model can perform zero-shot cross-lingual transfer for many languages. |
Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models (2021.eacl-main)
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| Challenge: | Existing benchmarks do not probe professional bias as pronoun resolution may be obfuscated by cross-correlations from other manifestations of gender prejudice. |
| Approach: | They propose to use a skew and stereotype metrics to quantify and analyse the gender bias present in contextual language models when tackling the WinoBias pronoun resolution task. |
| Outcome: | The proposed methods reduce skew and stereotype relative to the unaugmented fine-tuned BERT model. |
On the evolution of syntactic information encoded by BERT’s contextualized representations (2021.eacl-main)
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| Challenge: | Existing studies have focused on how linguistic information is encoded in pretrained language models to solve supervised tasks. |
| Approach: | They analyze how the syntax trees are embedded in the geometry of pretrained models for six different tasks, covering all levels of the linguistic structure. |
| Outcome: | The proposed model is able to learn and improve on GLUE and SQUAD, but it lacks the ability to learn the linguistic information required to solve the tasks. |
Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks (2021.eacl-main)
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| Challenge: | Empirically, we investigate KG matches for the SocialIQA, Physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC (SIQA), ConceptNet (Speer et al., 2017), and an automatically constructed instructional KG based on WikiHow (Ostermann e., 2019b). |
| Approach: | They propose a method to assess how well a candidate KG can fill in knowledge gaps for a given task by using commonsense probes. |
| Outcome: | Empirically, we show that the proposed KG-to-task match is a good match for socialIQA, physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC, ConceptNet, and an instructional KG based on WikiHow. |
Calculating the optimal step of arc-eager parsing for non-projective trees (2021.eacl-main)
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| Challenge: | arc-eager parsers are often driven by a classifier that determines the next step given features extracted from the current configuration. |
| Approach: | They propose to calculate the optimal next step of an arc-eager parser relative to a non-projective dependency structure in cubic time. |
| Outcome: | The proposed method can be used to train dependency parsers on configurations that correspond to 'gold' trees from a treebank. |
Subword Pooling Makes a Difference (2021.eacl-main)
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| Challenge: | Contextual word-representations use subword tokenization to handle large vocabularies and unknown words. |
| Approach: | They propose to use the first subword for morphological probing, POS tagging and NER to pool multiple subwords that correspond to a single word in contextual language models. |
| Outcome: | The proposed model outperforms two multilingual models on morphological probing, POS tagging and NER tasks in 9 languages. |
Content-based Models of Quotation (2021.eacl-main)
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| Challenge: | Prior work has focused on manual feature engineering and development of frameworks to test factors that influence quotability. |
| Approach: | They propose to use quotability identification as a passage ranking problem to evaluate models' performance . they use five datasets that span multiple languages and genres of literature . |
| Outcome: | The proposed model outperforms the existing model on five datasets that span multiple languages and genres of literature. |
L2C: Describing Visual Differences Needs Semantic Understanding of Individuals (2021.eacl-main)
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| Challenge: | Existing methods for captioning images without understanding individual's semantics are not effective . a new task, visual comparison, has drawn increasing attention in the field of language and vision . |
| Approach: | They propose a learning-to-compare model which learns to understand semantic structures of two images and compares them while learning to describe each one. |
| Outcome: | The proposed model outperforms the baseline and human evaluation on the Birds-to-Words dataset. |
VoiSeR: A New Benchmark for Voice-Based Search Refinement (2021.eacl-main)
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| Challenge: | a new study shows that voice-based search systems are challenging to support in the context of the user intent of voice searches . support for voice-driven search, exploration, and refinement is a fundamental aspect of voice assistants . |
| Approach: | They propose to use crowdsourcing to collect voice-based search refinements . they use 10,000 search refinement utterances to annotate a search intent . |
| Outcome: | The proposed dataset shows that voice-based search refinements can support most common tasks . the study shows that the proposed dataset can support research in conversational query understanding . |
Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings (2021.eacl-main)
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| Challenge: | a novel method for online news stream clustering is proposed . a user can scour the many news sources multiple times a day to find news articles . |
| Approach: | They propose a method for online news stream clustering that is a variant of the streaming K-means algorithm. |
| Outcome: | The proposed model achieves state-of-the-art on a standard stream clustering dataset of English documents. |
Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews (2021.eacl-main)
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| Challenge: | Existing models for sentiment-topic extraction assume topics are grouped under discrete sentiment categories such as ‘positive’, ‘negative’ and ‘neural’. |
| Approach: | They propose a Brand-Topic Model which aims to detect brand-associated polarity-bearing topics from product reviews. |
| Outcome: | The proposed model outperforms existing models on Amazon reviews and shows that it is more coherent and unique than existing models. |
Lexical Normalization for Code-switched Data and its Effect on POS Tagging (2021.eacl-main)
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| Challenge: | Social media data can be used to improve natural language processing performance, but it is often overlooked by lexical normalization systems. |
| Approach: | They propose three lexical normalization models specifically designed to handle code-switched data and evaluate their performance on POS tags. |
| Outcome: | The proposed models outperform monolingual models and lead to 5.4% performance increase for POS tagging compared to unnormalized input. |
Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification (2021.eacl-main)
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| Challenge: | Existing research on fact verification focuses on unstructured textual evidence, but it is still underexplored. |
| Approach: | They propose to use a table-based language model to verify textual statements . they use cell embeddings and numerical information to improve accuracy . |
| Outcome: | The proposed method outperforms the state-of-the-art model on question answering tasks even without modeling numerical information. |
A Study of Automatic Metrics for the Evaluation of Natural Language Explanations (2021.eacl-main)
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| Challenge: | a lack of transparency is a key issue for robotics and AI. |
| Approach: | They propose to map existing automatic evaluation methods for natural language generation onto explanations. |
| Outcome: | The proposed model shows that embedding-based evaluation methods have higher correlations with human ratings than word-overlap metrics. |
Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling (2021.eacl-main)
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| Challenge: | Written language contains stylistic cues that can be exploited to automatically infer a variety of potentially sensitive author information. |
| Approach: | They propose to use a transformer-based extension of a lexical replacement attack to attack written language by rewriting an author's text. |
| Outcome: | The proposed framework achieves high transferability when trained on a weakly labeled corpus—decreasing target model performance below chance. |
Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks (2021.eacl-main)
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| Challenge: | a large amount of work on cross-lingual transfer learning focused on typological and genealogical similarities between languages. |
| Approach: | They propose three features that capture cross-cultural similarities that manifest in linguistic patterns and quantify distinct aspects of language pragmatics. |
| Outcome: | The proposed features capture cross-cultural similarities manifest in linguistic patterns and quantify aspects of language pragmatics. |
PHASE: Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media (2021.eacl-main)
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| Challenge: | Recent studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. |
| Approach: | They propose a time-and-phase-aware framework that adaptively learns features from a user’s historical emotional spectrum to contextualize suicidal intent. |
| Outcome: | The proposed framework outperforms state-of-the-art methods while outperforming existing methods. |
Exploiting Definitions for Frame Identification (2021.eacl-main)
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| Challenge: | a frame-semantic parsing task is to determine which frame best captures the meaning of a word or phrase in a sentence. |
| Approach: | They propose a frame identification model that generates representations for frames and lexical units (senses) they evaluate the model on three data sets and show it consistently achieves better performance than previous systems. |
| Outcome: | The proposed model consistently outperforms previous systems on three data sets. |
ADePT: Auto-encoder based Differentially Private Text Transformation (2021.eacl-main)
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| Challenge: | Differential privacy is an important privacy concern when building statistical models on data containing sensitive information. |
| Approach: | They propose a utility-preserving differentially private text transformation algorithm using auto-encoders that can be used to transform text to offer robustness against attacks and produce transformations with high semantic quality. |
| Outcome: | The proposed model performs better against membership inference attacks while offering lower to no degradation in the utility of the underlying transformation process compared to baselines. |
Conceptual Grounding Constraints for Truly Robust Biomedical Name Representations (2021.eacl-main)
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| Challenge: | Existing approaches to encoding biomedical names require lexical and domain-specific semantics to be robust. |
| Approach: | They propose a method which encodes biomedical names with lexical and domain-specific semantics . they use conceptual grounding constraints to align encoded names to pretrained embeddings of their concept identifiers a technique that is effective even when using a deep averaging network . |
| Outcome: | The proposed representations capture more domain-specific semantics while remaining universally applicable across biomedical corpora and domains. |
Adaptive Mixed Component LDA for Low Resource Topic Modeling (2021.eacl-main)
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| Challenge: | Probabilistic topic models in low resource settings are faced with less reliable estimates due to sparsity of discrete word co-occurrence counts. |
| Approach: | They propose a mixture model which interpolates between discrete and continuous topic-word distributions and utilises pre-trained embeddings to improve topic coherence. |
| Outcome: | The proposed model outperforms fully discrete, fully continuous, and static mixture models on topic coherence in low resource settings. |
Evaluating Neural Model Robustness for Machine Comprehension (2021.eacl-main)
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| Challenge: | evaluating model robustness to adversarial attacks can provide deeper understanding of how deep neural networks work and what kind of linguistic information is actually captured by neural networks. |
| Approach: | They propose a method for strategic sentence-level perturbations to evaluate model robustness to adversarial attacks using character and word perturbations. |
| Outcome: | The proposed model improves model performance during adversarial attacks by using ensembles and predicts errors in adversarials. |
Hidden Biases in Unreliable News Detection Datasets (2021.eacl-main)
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| Challenge: | Recent studies show that automatic unreliable news detection models only use the article itself without resorting to fact-checking mechanisms. |
| Approach: | They propose to use a simple model as a difficulty/bias probe instead of a complex one . they observe a significant drop in accuracy for all models tested in a clean split . |
| Outcome: | The proposed model can achieve good performance by memorizing site-label mapping instead of modeling the real task. |
Annealing Knowledge Distillation (2021.eacl-main)
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| Challenge: | Knowledge distillation (KD) is a powerful model compression technique for deep neural networks. |
| Approach: | They propose a method to feed the rich information provided by teacher’s soft-targets incrementally and more efficiently by annealing the teacher output incrementally. |
| Outcome: | The proposed method can be used on image classification and NLP language inference tasks with BERT-based models on the GLUE benchmark. |
Unsupervised Extractive Summarization using Pointwise Mutual Information (2021.eacl-main)
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| Challenge: | Unsupervised approaches to extractive summarization rely on notion of sentence importance defined by semantic similarity between a sentence and the document. |
| Approach: | They propose a method to measure relevance and redundancy using PMI between sentences. |
| Outcome: | The proposed method outperforms similarity-based methods on news, medical journal articles, and personal anecdotes. |
Context-aware Neural Machine Translation with Mini-batch Embedding (2021.eacl-main)
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| Challenge: | Existing models that translate sentences in sentence-by-sentence manner do not consider the inter-sentent context in a language translation model. |
| Approach: | They propose to use mini-batch embedding to represent the features of sentences in a mini-Batch by choosing sentences from the same document and then embedd it into the model. |
| Outcome: | The proposed method outperforms baseline models and improves writing style or terminology to fit the document’s context. |
Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT (2021.eacl-main)
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| Challenge: | a recent study has shown that multilingual BERT encodes sentences in structurally meaningful ways. |
| Approach: | They analyze how morphosyntactic alignment manifests across embedding spaces of languages . they train classifiers to recover subjecthood of mBERT embedds in transitive sentences . |
| Outcome: | The proposed model encodes a high-order grammatical feature of morphosyntactic alignment across languages . the results show that the classifier distributions reflect the morphological alignment of their training languages based on the results . |
Streaming Models for Joint Speech Recognition and Translation (2021.eacl-main)
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| Challenge: | Using end-to-end models for speech translation has become a focus of the ST community . cascaded models have the advantage of including automatic speech recognition output . |
| Approach: | They propose a model that condenses sound waves into translated text and integrates automatic speech recognition outputs into the models. |
| Outcome: | The proposed model is statistically similar to cascading models, but has half the number of parameters. |
DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections (2021.eacl-main)
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Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, Ilya Eckstein
| Challenge: | Using pre-trained models, we learn to jointly predict words and entities from multiple text sources without any human supervision. |
| Approach: | They propose to learn rich self-supervised entity representations from large amounts of associated text. |
| Outcome: | The proposed models outperform baseline models on downstream tasks in the TV-Movies domain, and scale to very large corpora. |
Scientific Discourse Tagging for Evidence Extraction (2021.eacl-main)
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| Challenge: | Primary experimental articles provide the crucial raw material for all subsequent scientific research, but the growing number of scientific literature makes it difficult for domain experts to efficiently utilize them. |
| Approach: | They propose to automatically extract text fragments from primary research papers that describe the evidence presented in that paper's figures and to use them to build models of scientific argument. |
| Outcome: | The proposed method is able to extract text fragments from primary research papers that describe the evidence presented in that paper's figures, and it is transferable to new datasets. |
Incremental Beam Manipulation for Natural Language Generation (2021.eacl-main)
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| Challenge: | a larger beam size can lead to deteriorating performance of natural language generation systems due to model errors . performance of NLG systems can plateau or even decrease when beam sizes larger than 10 are used . |
| Approach: | They propose to rerank the output of beam search to produce a good set of hypotheses . they propose incremental beam manipulation to discarded hypothese . |
| Outcome: | The proposed method outperforms a strong reranker on the E2E and WebNLG datasets while being on par with the existing method. |
StructSum: Summarization via Structured Representations (2021.eacl-main)
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Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov
| Challenge: | Abstractive summarization models overfit to training corpora, lack of transparency and layout bias . authors propose incorporating latent and explicit dependencies across sentences in source document . |
| Approach: | They propose a framework based on document-level structure induction to address layout bias and lack of transparency in abstractive summarization models. |
| Outcome: | The proposed framework improves coverage of content in the source documents and generates more abstractive summaries by generating more novel n-grams. |
Project-then-Transfer: Effective Two-stage Cross-lingual Transfer for Semantic Dependency Parsing (2021.eacl-main)
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| Challenge: | Several remarkable contributions have been made in syntactic dependency parsing, especially on universal dependencies. |
| Approach: | They propose to capture cross-linguality by combing annotation projection and model transfer of pre-trained language models. |
| Outcome: | The proposed model parser almost achieved the approximated upper bound. |
LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction (2021.eacl-main)
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| Challenge: | Open Information Extraction (OIE) systems extract factual propositions into n-ary tuples . current datasets are limited in size and diversity . |
| Approach: | They propose to convert QA-SRL 2.0 dataset to large-scale OIE dataset LSOIE. |
| Outcome: | The proposed dataset is 20 times larger than the next largest human-annotated OIE dataset. |
Changing the Mind of Transformers for Topically-Controllable Language Generation (2021.eacl-main)
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| Challenge: | Existing interactive writing assistants do not allow authors to guide text generation in desired topical directions. |
| Approach: | They propose a framework that displays multiple candidate upcoming topics and generates a text generation model that adheres to the chosen topics. |
| Outcome: | The proposed model generates fluent sentences related to the selected topics, as judged by automated metrics and crowdsourced workers. |
Unsupervised Abstractive Summarization of Bengali Text Documents (2021.eacl-main)
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Radia Rayan Chowdhury, Mir Tafseer Nayeem, Tahsin Tasnim Mim, Md. Saifur Rahman Chowdhury, Taufiqul Jannat
| Challenge: | Abstractive summarization systems are difficult to perform due to the unavailability of the parallel data for low-resource languages like Bengali. |
| Approach: | They propose a graph-based unsupervised abstractive summarization system in Bengali text documents that requires only a Part-Of-Speech (POS) tagger and a pre-trained language model trained on Bengali texts. |
| Outcome: | The proposed system outperforms baselines without human-annotated reference summaries on a human-random dataset with Bengali text. |
From Toxicity in Online Comments to Incivility in American News: Proceed with Caution (2021.eacl-main)
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| Challenge: | Existing tools for quantifying incivility online, in news and in congressional debates are inadequate for the analysis of incivility in news. |
| Approach: | They develop a Jigsaw Perspective API to quantify incivility in news . they show that toxicity models are inadequate for the analysis of incivility in news. |
| Outcome: | The Jigsaw Perspective API detects incivility on a corpus of American news articles. |
On the Computational Modelling of Michif Verbal Morphology (2021.eacl-main)
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| Challenge: | Existing computational models of the verbal morphology of the Métis language are insufficient to model the language's unique phonological interactions. |
| Approach: | They propose a finite-state computational model of the verbal morphology of Michif . they use composed finite state transducers to model concatenative morphologies . |
| Outcome: | The proposed model is based on a series of finite-state transducers. |
A Few Topical Tweets are Enough for Effective User Stance Detection (2021.eacl-main)
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| Challenge: | Recent work that employs unsupervised classification has shown that user stance detection is highly accurate on vocal Twitter users, but fails for less vocal users, who may have only authored a few tweets about a target. |
| Approach: | They propose to perform user stance detection on vocal Twitter users using contextualized embeddings to represent tweets and then perform unsupervised classification of the user. |
| Outcome: | The proposed method outperforms two baseline methods and achieves 89.6% accuracy and 91.3% macro F-measure on eight controversial topics. |
Do Syntax Trees Help Pre-trained Transformers Extract Information? (2021.eacl-main)
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| Challenge: | Recent work suggests that incorporating syntax information from dependency trees can improve task-specific transformer models. |
| Approach: | They propose to incorporate dependency tree information into pre-trained transformers for three tasks . they propose a late fusion approach and a joint fusion technique to infuses syntax structure into attention layers. |
| Outcome: | The proposed models obtain state-of-the-art results on SRL and relation extraction tasks. |
Informative and Controllable Opinion Summarization (2021.eacl-main)
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| Challenge: | Existing methods for opinion summarization use a two-stage extractive and abstractive approach to generate summaries for reviews of a specific target. |
| Approach: | They propose a framework for opinion summarization that condenses all input reviews into multiple dense vectors which serve as input to an abstractive model. |
| Outcome: | The proposed framework produces more informative summaries and allows to take user preferences into account using a zero-shot customization technique. |
Coloring the Black Box: What Synesthesia Tells Us about Character Embeddings (2021.eacl-main)
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| Challenge: | Neural network models are difficult to understand and are considered "black boxes". |
| Approach: | They use grapheme–color synesthesia to study character embeddings in English . they compare graphemes to phonemes to find the most human-like character embeds . |
| Outcome: | The results show that grapheme-to-phoneme conversion results in the most human-like character embeddings. |
How Good (really) are Grammatical Error Correction Systems? (2021.eacl-main)
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| Challenge: | Standard evaluations of Grammatical Error Correction systems use a fixed reference text generated relative to the original text. |
| Approach: | They propose to use a gold reference text to evaluate Grammatical Error Correction systems that is generated relative to the original text and is independent of the system output. |
| Outcome: | The proposed evaluations show that the system performs 20-40 points better than standard evaluations. |
BERTective: Language Models and Contextual Information for Deception Detection (2021.eacl-main)
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| Challenge: | Existing methods to classify texts as truthful or deceptive are limited by the context of the text being analyzed. |
| Approach: | They propose to use a corpus of Italian dialogues to classify texts as truthful or deceptive. |
| Outcome: | The proposed models show that not all contexts are equally useful to the task. |
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning (2021.eacl-main)
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| Challenge: | Existing approaches to learn simultaneous translation model with coupled programmer-interpreter policies are suboptimal as they fix the agent's policy to focus learning the NMT model or learn adaptive agent policies while the NRT model is fixed. |
| Approach: | They propose an algorithmic oracle to produce oracular READ/WRITE actions for training bilingual sentence-pairs using the notion of word alignments. |
| Outcome: | The proposed method outperforms baselines in terms of translation quality quality while keeping the delay low. |
Complementary Evidence Identification in Open-Domain Question Answering (2021.eacl-main)
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| Challenge: | Existing approaches to QA that only measure the relevance between the question and each paragraph are not effective. |
| Approach: | They propose a method that learns vector representations of passages and models the sufficiency and diversity within the selected set, in addition to the relevance between the question and passages. |
| Outcome: | The proposed method significantly improves the accuracy of complementary evidence selection in open-domain question answering domain. |
Entity-level Factual Consistency of Abstractive Text Summarization (2021.eacl-main)
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Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, Bing Xiang
| Challenge: | Existing models exhibit entity hallucination, generating names of entities that are not present in the source document. |
| Approach: | They propose to use entity-level factual consistency to improve model quality . they propose to filter the training data to reduce entity hallucination problem . |
| Outcome: | The proposed model can reduce the entity hallucination problem by filtering the training data. |
On Hallucination and Predictive Uncertainty in Conditional Language Generation (2021.eacl-main)
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| Challenge: | Modern deep neural network models have brought drastic improvements in generation quality measured by standard metrics on different natural language generation tasks. |
| Approach: | They propose a beam search extension to reduce hallucination in conditional language generation by adding a prediction extension to beam search. |
| Outcome: | The proposed extension improves trading performance on standard metric for less hallucination with the proposed beam search variant. |
Fine-Grained Event Trigger Detection (2021.eacl-main)
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| Challenge: | Existing methods for Event Detection (ED) focus on a limited set of event types . existing datasets for ED focus on only 33 event types while the number of events in the TAC KBP dataset is 38. |
| Approach: | They propose to transform Word Sense Disambiguation's Semcor dataset into a large and high-quality dataset for ED. |
| Outcome: | The proposed method transforms the Semcor dataset for Word Sense Disambiguation into a large and high-quality dataset for FED. |
Extremely Small BERT Models from Mixed-Vocabulary Training (2021.eacl-main)
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| Challenge: | Existing knowledge distillation methods cannot be directly applied to train student models with reduced vocabulary and embedding dimensions. |
| Approach: | They propose a method to align teacher and student embeddings via mixed-vocabulary training. |
| Outcome: | The proposed method compresses BERT-LARGE to a task-agnostic model with smaller vocabulary and hidden dimensions, which is an order of magnitude smaller than other distilled models. |
Diverse Adversaries for Mitigating Bias in Training (2021.eacl-main)
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| Challenge: | Existing adversarial methods only partially mitigate the problem of model bias, added to which their training procedures are unstable. |
| Approach: | They propose a method where discriminators are encouraged to learn orthogonal hidden representations from one another to reduce model bias. |
| Outcome: | The proposed method significantly reduces bias and stability of training over standard methods. |
‘Just because you are right, doesn’t mean I am wrong’: Overcoming a bottleneck in development and evaluation of Open-Ended VQA tasks (2021.eacl-main)
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Man Luo, Shailaja Keyur Sampat, Riley Tallman, Yankai Zeng, Manuha Vancha, Akarshan Sajja, Chitta Baral
| Challenge: | Existing visual question answering datasets assume only one ground truth answer for each question. |
| Approach: | They propose alternative answer sets (AAS) of ground-truth answers to address this limitation . they modify top VQA solvers to support multiple plausible answers for a question . |
| Outcome: | The proposed approach improves on the GQA dataset and shows that it is more efficient than previous approaches. |
Better Neural Machine Translation by Extracting Linguistic Information from BERT (2021.eacl-main)
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| Challenge: | Experimental results show that incorporating linguistic information into neural machine translation models is no more difficult to train than conventional Transformer-based NMT. |
| Approach: | They propose to extract linguistic information from contextual word embeddings instead of point estimates to augment NMT models. |
| Outcome: | The proposed method generalizes better in a variety of training contexts and is no more difficult to train than conventional Transformer-based NMT. |
CLiMP: A Benchmark for Chinese Language Model Evaluation (2021.eacl-main)
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| Challenge: | Linguistically informed analyses of language models (LMs) contribute to understanding and improvement of such models. |
| Approach: | They introduce a corpus of Chinese linguistic minimal pairs (CLiMP) to investigate what knowledge Chinese LMs acquire. |
| Outcome: | The proposed corpus of Chinese linguistic minimal pairs (CLiMP) covers 9 major Chinese linguist phenomena. |
Measuring and Improving Faithfulness of Attention in Neural Machine Translation (2021.eacl-main)
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| Challenge: | Existing evidence for faithfulness of neural machine translation models is lacking. |
| Approach: | They propose a novel objective that rewards faithful behaviour by the model through probability divergence and a differentiable objective that can increase faithfulness without reducing the translation quality. |
| Outcome: | The proposed objective increases faithfulness without reducing translation quality and can even improve translation quality in some cases. |
Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering (2021.eacl-main)
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| Challenge: | Existing open-domain question answering systems are insufficient to capture deep semantic matching that goes beyond lexical overlaps. |
| Approach: | They propose a sample-efficient method to pretrain the paragraph encoder using an existing pretraining model instead of heuristically created pseudo question-paragraph pairs. |
| Outcome: | The proposed method outperforms a strong dense retrieval baseline that uses 6 times more computation for training. |
Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs (2021.eacl-main)
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| Challenge: | Existing methods for few-shot link prediction are limited by having only a few examples of a relation . low-frequency relations are abundant in knowledge graphs, but link prediction for these relations is important . |
| Approach: | They perform few-shot link prediction for a set of new relations unseen during training, given only a few examples of each relation at test time. |
| Outcome: | The proposed model is based on a simple, zero-shot baseline that ignores relation-specific information and achieves surprisingly strong performance. |
ProFormer: Towards On-Device LSH Projection Based Transformers (2021.eacl-main)
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| Challenge: | ProFormer is a projection based transformer architecture that is faster and lighter making it suitable to deploy to memory constraint devices such as mobile phones, watches and IoT. |
| Approach: | They propose a projection based transformer architecture that generates word representations on-the-fly without embedding lookup tables and a local projection attention layer that transforms the input sequence of N LSH word projections into a sequence of K representations. |
| Outcome: | The proposed architecture reduces memory footprint from 92.16 MB to 1.7 KB and requires 16x less computation overhead making it suitable to deploy to memory constraint devices and preserve user privacy. |
Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification (2021.eacl-main)
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| Challenge: | Existing methods for hierarchical multi-label classification do not assume label hierarchy exists. |
| Approach: | They propose to jointly learn the classifier parameters as well as the label embeddings . they propose to use hyperbolic embeddables to gain better generalisation over the labels . |
| Outcome: | The proposed method achieves state-of-the-art generalization on benchmarks and is more accurate than existing methods. |
Segmenting Subtitles for Correcting ASR Segmentation Errors (2021.eacl-main)
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David Wan, Chris Kedzie, Faisal Ladhak, Elsbeth Turcan, Petra Galuscakova, Elena Zotkina, Zhengping Jiang, Peter Bell, Kathleen McKeown
| Challenge: | Typical ASR systems segment input audio into utterances using purely acoustic information, which may not resemble sentence-like units expected by conventional machine translation systems for spoken language translation (SLT). |
| Approach: | They propose a model for correcting ASR acoustic segmentation using subtitles as a proxy dataset for creating synthetic aural utterances by modeling common error modes. |
| Outcome: | The proposed model improves performance on MT and audio-document cross-language information retrieval (CLIR) it uses subtitles as a proxy dataset to correct ASR acoustic segmentation . |
Crisscrossed Captions: Extended Intramodal and Intermodal Semantic Similarity Judgments for MS-COCO (2021.eacl-main)
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| Challenge: | Existing image captioning datasets have limited cross-modal associations, preventing researchers from examining how inter-modal learning impacts intra-modal tasks. |
| Approach: | They propose to use image captioning data to support multi-modal retrieval training and evaluation to assess the impact of inter-modality learning. |
| Outcome: | The proposed model is able to measure the influence of intra- and inter-modality learning. |
On-Device Text Representations Robust To Misspellings via Projections (2021.eacl-main)
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| Challenge: | Recent advances in Locality-Sensitive Hashing (LSH)-based projection networks have demonstrated state-of-the-art performance in various classification tasks without explicit word embedding lookup tables by computing on-the fly text representations. |
| Approach: | They propose to use locality-sensitive hashing to compute on-the-fly text representations without explicit word embedding tables. |
| Outcome: | The proposed classifiers are more robust to common misspellings and perturbations of the input text compared to biLSTMs and fine-tuned BERT based methods. |
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)
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| Challenge: | Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain. |
| Approach: | They propose a pre-training method to improve the joint extraction performance with just extra entity annotations. |
| Outcome: | The proposed method outperforms existing methods on ACE05, SciERC, and NYT and outperformed BERT on other tasks. |
Text Augmentation in a Multi-Task View (2021.eacl-main)
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| Challenge: | a multi-task view of data augmentation allows for a more robust performance than traditional augmentation. |
| Approach: | They propose a multi-task view of data augmentation where original and augmented samples are weighted substantively during training. |
| Outcome: | The proposed model improves on three benchmark text classification datasets. |
Representations for Question Answering from Documents with Tables and Text (2021.eacl-main)
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| Challenge: | a study aims to improve question answering on tables by refining table representations based on textual context. |
| Approach: | They aim to improve question answering from tables by refining table representations based on textual context. |
| Outcome: | The proposed method improves on the Natural Questions dataset using text and table representations. |
PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation (2021.eacl-main)
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| Challenge: | Existing methods for cross-lingual transfer use implicit supervision to parse low-resource languages without explicit supervision. |
| Approach: | They propose a method for unsupervised cross-lingual transfer that uses their output as implicit supervision as part of self-training on unlabelled text in the target language. |
| Outcome: | The proposed method improves over state-of-the-art models on both distant and nearby languages, despite being conceptually simpler. |
Modelling Context Emotions using Multi-task Learning for Emotion Controlled Dialog Generation (2021.eacl-main)
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| Challenge: | Recent research has tackled this task using neural generative methods by augmenting emotion classes with the input sequences. |
| Approach: | They propose to use a self-attention based encoder and a decoder with dot product attention mechanism to generate a viable response with a specified emotion. |
| Outcome: | The proposed model outperforms baselines on automatic evaluation measures such as F1 and BLEU scores, thus resulting in more fluent and adequate responses. |
Gender and Racial Fairness in Depression Research using Social Media (2021.eacl-main)
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| Challenge: | Existing studies show that social media behavior can indicate mental health of an individual . previous studies have raised concerns about possible biases in models produced from such data, but no study has investigated how these biase recur with demographic groups. |
| Approach: | They analyze the fairness of depression classifiers trained on Twitter data with respect to gender and racial/ethnic demographic groups. |
| Outcome: | The proposed model performs better for gender and racial/ethnic groups than other models and provides recommendations on how to avoid biases in future research. |
MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark (2021.eacl-main)
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| Challenge: | Existing datasets for task-oriented dialog systems are limited and expensive . current models are based on the simple intent and slot detection paradigm for non-compositional queries. |
| Approach: | They propose to use a multilingual dataset to scale semantic parsing models to new languages . they demonstrate an average improvement of +6.3 points on Slot F1 for existing datasets . |
| Outcome: | The proposed model achieves an average improvement of +6.3 points on Slot F1 over existing models. |
Adapting Event Extractors to Medical Data: Bridging the Covariate Shift (2021.eacl-main)
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| Challenge: | a new study examines the performance of event extractors to new domains without labeled data . event extraction is a key sub-task of interest for text understanding pipelines in multiple domains . |
| Approach: | They propose to align marginal distributions of source and target domains to adapt event extractors to new domains . they use clinical notes and doctor-patient conversations as a testbed . |
| Outcome: | The proposed models reach F1 scores of 70.0 and 72.9 on notes and conversations respectively. |
NoiseQA: Challenge Set Evaluation for User-Centric Question Answering (2021.eacl-main)
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| Challenge: | Question-Answering (QA) systems are deployed in the real world . a lack of research attention has been devoted to studying the issues that arise when people use QA systems. |
| Approach: | They show that component components that precede an answering engine can introduce varied and considerable sources of error. |
| Outcome: | The proposed evaluations highlight the need for QA evaluation to expand to consider real-world use. |
Co-evolution of language and agents in referential games (2021.eacl-main)
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| Challenge: | Referential games allow neural agents to learn language, but they do not take into account the learning biases of the learners. |
| Approach: | They propose to model cultural and architectural evolution in a population of agents to take into account learning biases of the language learners and let them co-evolve. |
| Outcome: | The proposed model outperforms cultural transmission in a population of agents and takes into account learning biases of the learners. |
Modeling Context in Answer Sentence Selection Systems on a Latency Budget (2021.eacl-main)
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| Challenge: | Current AS2 models score question-answer pairs individually, ignoring any information from the document each potential answer was extracted from. |
| Approach: | They propose an approach to efficiently incorporate contextual information into AS2 models . they use unsupervised similarity techniques to extract relevant sentences from source document . |
| Outcome: | The proposed approach improves 6% to 11% over state-of-the-art in AS2 with minimal latency. |
Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees (2021.eacl-main)
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| Challenge: | Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information. |
| Approach: | They propose a plug-and-play framework that incorporates syntax trees into pre-trained Transformers. |
| Outcome: | The proposed framework improves on pre-trained models on natural language understanding datasets and shows that it can be used to train pre-structured neural networks. |
DISK-CSV: Distilling Interpretable Semantic Knowledge with a Class Semantic Vector (2021.eacl-main)
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| Challenge: | Neural networks (NNs) are becoming deeper and more complex, making them difficult to understand and interpret. |
| Approach: | They propose a method to distill knowledge concurrently from any neural network architecture for text classification. |
| Outcome: | The proposed method achieves better performance than the target black-box and provides better explanations than existing techniques. |
Attention Can Reflect Syntactic Structure (If You Let It) (2021.eacl-main)
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| Challenge: | a recent study has attempted to decode linguistic structure from the Transformer . but, much of the work focused on English, a language with rigid word order and a lack of inflectional morphology. |
| Approach: | They propose to fine-tune a feature encoder for BERT to learn linguistic structure from its multi-head attention mechanism. |
| Outcome: | The proposed model can decode full trees above baseline accuracy from single attention heads across languages. |
Extractive Summarization Considering Discourse and Coreference Relations based on Heterogeneous Graph (2021.eacl-main)
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| Challenge: | Abstractive summarization aims to select salient text spans (mostly sentences) from the input document. |
| Approach: | They propose a heterogeneous graph based model that incorporates both discourse and coreference relations between text spans of different granularity. |
| Outcome: | The proposed model is efficient and factually reliable on a benchmark summarization dataset. |
CDA: a Cost Efficient Content-based Multilingual Web Document Aligner (2021.eacl-main)
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| Challenge: | a Content-based document alignment approach is an efficient way to align multilingual web documents based on content. |
| Approach: | They propose a Content-based document alignment approach to align multilingual web documents based on content in parallel training data for machine translation systems. |
| Outcome: | The proposed method achieves comparable performance with state-of-the-art systems in the WMT-16 Bilingual Document Alignment Shared Task benchmark while operating in multilingual space. |
Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers (2021.eacl-main)
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| Challenge: | Numerical tables are used to present experimental results in scientific papers. |
| Approach: | They propose a task to extract metric-types from multi-level header numerical tables . they propose two joint-learning neural classification and generation schemes . |
| Outcome: | The proposed models handle in-header and out-of-headers metric-type identification problems. |
EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction (2021.eacl-main)
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| Challenge: | EmpathBERT is a demographic-aware framework for empathy prediction based on BERT. |
| Approach: | They propose a demographic-aware framework for empathy prediction based on BERT and utilize user demographics to analyze user responses to stimulative news articles. |
| Outcome: | The proposed framework surpasses machine learning and deep learning models and highlights the importance of demographic information in the responses. |
Are Neural Networks Extracting Linguistic Properties or Memorizing Training Data? An Observation with a Multilingual Probe for Predicting Tense (2021.eacl-main)
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| Challenge: | a recent study has shown that neural networks can learn from linguistic representations without supervision . many studies have tried to identify which linguistic properties are encoded in the embeddings . |
| Approach: | They evaluate the ability of Bert embeddings to represent tense information . they use a multilingual linguistic probe to predict the morphology of a word . |
| Outcome: | The proposed model can predict tenses in French and Chinese, but the results drop sharply for Chinese. |
Is Supervised Syntactic Parsing Beneficial for Language Understanding Tasks? An Empirical Investigation (2021.eacl-main)
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| Challenge: | Traditional NLP has long held (supervised) syntactic parsing necessary for successful higher-level semantic language understanding (LU). |
| Approach: | They empirically examine the usefulness of supervised parsing for semantic LU in LM-pretrained transformer networks. |
| Outcome: | The proposed model is based on LM-pretrained transformer networks with a biaffine parsing head and fine-tuned for LU tasks. |
Facilitating Terminology Translation with Target Lemma Annotations (2021.eacl-main)
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| Challenge: | Recent work on terminology integration assumes that the correct morphological forms are apriori known. |
| Approach: | They propose to train machine translation systems using a source-side data augmentation method that annotates randomly selected source language words with their target language lemmas. |
| Outcome: | The proposed method improves terminology translation accuracy in Latvian and Baltic languages. |
Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources (2021.eacl-main)
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| Challenge: | a hybrid approach to lemmatization enhances the seq2seq neural model with additional lemmas extracted from an external lexicon or a rule-based system. |
| Approach: | They propose a hybrid approach that enhances a seq2seq neural model with additional lemmas extracted from an external lexicon or a rule-based system. |
| Outcome: | The proposed model achieves statistically significant improvements on 23 UD languages, compared to baseline models not utilizing additional lemma information. |
Summarising Historical Text in Modern Languages (2021.eacl-main)
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| Challenge: | Historical text summarisation is a routine for historians and digital humanities researchers but has never been automated. |
| Approach: | They propose a model that can be trained even with no cross-lingual data and further benchmark it against state-of-the-art algorithms. |
| Outcome: | The proposed model outperforms standard cross-lingual benchmarks on historical text summarisation task and identifies distinctness and value of the dataset. |
Challenges in Automated Debiasing for Toxic Language Detection (2021.eacl-main)
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| Challenge: | Existing methods for debiasing toxic language data are limited in their ability to prevent biased behavior in toxic language detection systems. |
| Approach: | They propose to debiase toxic language detection models using lexical and dialectal markers using synthetic labels instead of traditional methods. |
| Outcome: | The proposed method reduces dialectal associations with toxicity despite the use of synthetic labels . |
Adaptive Fusion Techniques for Multimodal Data (2021.eacl-main)
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| Challenge: | Effective fusion of data from multiple modalities is challenging due to the heterogeneous nature of multimodal data. |
| Approach: | They propose two adaptive fusion techniques that aim to combine multimodal data effectively. |
| Outcome: | The proposed networks can model context from other modalities better than existing methods. |
Detecting Scenes in Fiction: A new Segmentation Task (2021.eacl-main)
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Albin Zehe, Leonard Konle, Lea Katharina Dümpelmann, Evelyn Gius, Andreas Hotho, Fotis Jannidis, Lucas Kaufmann, Markus Krug, Frank Puppe, Nils Reiter, Annekea Schreiber, Nathalie Wiedmer
| Challenge: | Text segmentation is a long standing issue in the area of natural language processing . even modern methods struggle with processing text longer than a couple of sentences or paragraphs . |
| Approach: | They introduce the task of scene segmentation on narrative texts and provide an annotated corpus . they discuss linguistic and narrative properties of the task and provide baseline experiments . |
| Outcome: | The proposed task is very challenging and the results are impressive. |
LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content (2021.eacl-main)
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| Challenge: | Existing work on claim detection is built on the basis of a 'segregation' of claims across different domains. |
| Approach: | They propose a generalized generalized model that captures syntactic features through part-of-speech and dependency embeddings, as well as contextual features through a fine-tuned language model. |
| Outcome: | The proposed model outperforms baselines on six claim datasets by an average of 3 claim-F1 points and 2 claim-f1 points on the general-domain experiments. |
Interpretability for Morphological Inflection: from Character-level Predictions to Subword-level Rules (2021.eacl-main)
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| Challenge: | Neural models for morphological inflection have recently attained very high results, but their interpretation remains challenging. |
| Approach: | They propose a linguistically-motivated variant to the encoder-decoder model with attention that incorporates a character-level cross-attention mechanism and a self-attention module over substrings of the input. |
| Outcome: | The proposed model performs well on three typologically-different languages and is highly interpretable. |
Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates (2021.eacl-main)
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| Challenge: | Existing models for question generation suffer from lack of diversity and bad sentence structures. |
| Approach: | They propose a framework that integrates flexible templates with a neural-based model to generate diverse expressions of questions with sentence structure guidance. |
| Outcome: | The proposed framework generates diverse expressions of questions with sentence structure guidance while maintaining high quality and consistency under automatic evaluation and human evaluation. |
Handling Out-Of-Vocabulary Problem in Hangeul Word Embeddings (2021.eacl-main)
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| Challenge: | Word embedding is considered an essential factor in improving the performance of various Natural Language Processing (NLP) models. |
| Approach: | They propose a Hangeul word embedding model that infers original word embeds from typos while maintaining high performance. |
| Outcome: | The proposed model performs well against typos while maintaining high performance. |
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation (2021.eacl-main)
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| Challenge: | Existing studies on multimodality in simultaneous machine translation have highlighted the challenges for the agent to maintain good translation quality while learning an optimal translation path. |
| Approach: | They propose a multimodal approach to simultaneous machine translation using reinforcement learning with strategies to integrate visual and textual information in both the agent and the environment. |
| Outcome: | The proposed multimodal approach improves translation quality while keeping latency low while providing visual cues. |
STAR: Cross-modal [STA]tement [R]epresentation for selecting relevant mathematical premises (2021.eacl-main)
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| Challenge: | Existing representations of mathematical statements in natural language are ineffective . STAR model uses cross-modal attention to represent mathematical text . |
| Approach: | They propose a model that uses cross-modal attention to represent mathematical text . it uses conjectures written in both natural and mathematical language to recommend premises . |
| Outcome: | The proposed model outperforms baseline models that do not distinguish between natural and mathematical elements and achieves better performance than state-of-the-art models. |
Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions? (2021.eacl-main)
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| Challenge: | Existing models fail to answer a large portion of sub-questions . Existing systems have achieved super-human performance . |
| Approach: | They propose to use a neural decomposition model to generate sub-questions for a multi-hop question and extract the corresponding sub-answers. |
| Outcome: | The proposed model is based on a hotpotQA dataset with a multi-hop question and sub-answers. |
Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models (2021.eacl-main)
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| Challenge: | Recent work has shown that monolingual English language models can be used as knowledge bases. |
| Approach: | They use mBERT to query monolingual English language models with masked sentences to test their hypothesis. |
| Outcome: | The proposed model can be used to answer fill-in-the-blank questions in 53 languages. |
Variational Weakly Supervised Sentiment Analysis with Posterior Regularization (2021.eacl-main)
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| Challenge: | Existing methods for sentiment analysis require human annotations, but they are scarce. |
| Approach: | They propose a posterior regularization framework to control the posterior distribution of label assignment. |
| Outcome: | The proposed framework improves the variational approach to the weakly supervised sentiment analysis and the performance is more stable with smaller prediction variance. |
Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration (2021.eacl-main)
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| Challenge: | Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem, but gold data suggests that maximizing the probability of a singular sense is not the most suitable training objective for WSD. |
| Approach: | They propose to use Word Sense Disambiguation (WSD) as a multi-label classification problem in which multiple senses can be assigned to each target word. |
| Outcome: | The proposed method bears closer resemblance to how human annotators disambiguate text and can be extended to exploit structured knowledge from semantic networks. |
Graph-based Fake News Detection using a Summarization Technique (2021.eacl-main)
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| Challenge: | Existing methods to detect fake news using external information and internal information are difficult to identify external information in all documents. |
| Approach: | They propose a graph-based fake news detection method that uses only the document internal information to represent the relationship between all sentences using a diagram and the reflection rate of contextual information among sentences is computed by using an attention mechanism. |
| Outcome: | The proposed method achieves high accuracy, 91.04%, that is 8.85%p better than the previous method. |
Cognition-aware Cognate Detection (2021.eacl-main)
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Diptesh Kanojia, Prashant Sharma, Sayali Ghodekar, Pushpak Bhattacharyya, Gholamreza Haffari, Malhar Kulkarni
| Challenge: | Existing approaches to cognate detection use orthographic, phonetic and semantic similarity based features sets. |
| Approach: | They propose a method for enriching feature sets with cognitive features extracted from gaze behaviour data from human readers’ gaze behaviour. |
| Outcome: | The proposed method improves cognate detection performance by 10% and 12% over existing methods. |
A Simple Three-Step Approach for the Automatic Detection of Exaggerated Statements in Health Science News (2021.eacl-main)
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| Challenge: | Exaggerations in health news can have tremendous adverse effects on the lifestyle of the common masses who feed themselves mostly on such news instead of the source scientific publication. |
| Approach: | They propose a three-step approach that extracts relation phrases from a scientific paper and then classifies the strength of the relationship phrase extracted. |
| Outcome: | The proposed approach outperforms baseline models that compare state-of-the-art embedding of the statement pairs through a binary classifier or recast the problem as a textual entailment task. |
Modeling Coreference Relations in Visual Dialog (2021.eacl-main)
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| Challenge: | Visual dialog is a vision-language task where an agent needs to answer a series of questions grounded in an image based on the understanding of the dialog history and the image. |
| Approach: | They propose two soft constraints that can improve the model’s ability of resolving coreferences in dialog in an unsupervised way based on linguistic knowledge and discourse features of human dialog. |
| Outcome: | The proposed model achieves state-of-the-art performance on the VisDial v1.0 dataset without pretraining on other vision language datasets. |
Increasing Robustness to Spurious Correlations using Forgettable Examples (2021.eacl-main)
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| Challenge: | Neural NLP models often exploit spurious correlations to perform tasks. minority examples have been shown to increase the out-of-distribution generalization of pre-trained language models. |
| Approach: | They propose to use example forgetting to find minority examples without prior knowledge of spurious correlations in the dataset. |
| Outcome: | The proposed approach improves out-of-distribution generalization on minorities . it shows that minority examples are more robust on challenging datasets . |
On Robustness of Neural Semantic Parsers (2021.eacl-main)
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| Challenge: | Semantic parsing maps natural language (NL) utterances into logical forms (LFs) adversarial examples are created by adding tiny perturbations to inputs but can severely deteriorate model performance. |
| Approach: | They propose to construct robustness test sets based on existing benchmark corpora and to evaluate the effect of data augmentation. |
| Outcome: | The proposed method measures the performance of the proposed parsers on robustness test sets and evaluates the effect of data augmentation. |
Benchmarking a transformer-FREE model for ad-hoc retrieval (2021.eacl-main)
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| Challenge: | a recent study compares transformer-based models with a greener and more sustainable alternative. |
| Approach: | They compare transformer-based models with a "greener and more sustainable" alternative . they show that transformer-like models can be used in real-world retrieval applications . |
| Outcome: | The lighter model achieves a speedup of 20 times in training and 7 to 47 times in inference while maintaining a comparable retrieval performance. |
Reanalyzing the Most Probable Sentence Problem: A Case Study in Explicating the Role of Entropy in Algorithmic Complexity (2021.eacl-main)
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| Challenge: | Existing descriptive complexity measures are ineffective at describing algorithms' behaviour, and can make an apparently tractable problem seem NP-complete. |
| Approach: | They propose to use statistical measures to give an updated analysis of the complexity of the NP-complete most probable sentence problem for pCFGs. |
| Outcome: | The proposed method can be applied to word sense disambiguation and inference tasks. |
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? (2021.eacl-main)
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| Challenge: | Neural models have established state-of-the-art performance on several NLP benchmarks, but little is understood about the mechanisms by which they operate. |
| Approach: | They examine the probing paradigm through a set of controlled synthetic tasks and show that pretrained word embeddings play a considerable role in encoding these properties rather than the training task itself. |
| Outcome: | The proposed model can encode linguistic properties above chance-level even when distributed in the data as random noise, reversing the interpretation of absolute claims on probing tasks. |
One-class Text Classification with Multi-modal Deep Support Vector Data Description (2021.eacl-main)
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| Challenge: | Using multi-modal deep SVDD, we can build a much better description for target one-class data. |
| Approach: | They propose to extend uni-modal SVDD to multiple modal mSVDD and introduce a mechanism for incorporating negative supervision in the absence of real negative data. |
| Outcome: | The proposed model outperforms uni-modal SVDD and can get further improvements when negative supervision is incorporated. |
Unsupervised Word Polysemy Quantification with Multiresolution Grids of Contextual Embeddings (2021.eacl-main)
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| Challenge: | a new method to quantify polysemy is based on basic geometry in the contextual embedding space . word sense annotation has always been one of the tasks with the lowest interannotator agreement . |
| Approach: | They propose a method to estimate polysemy based on simple geometry in contextual embedding space. |
| Outcome: | The proposed method is fully unsupervised and data-driven . it can be used to sample sentences with different senses at no extra cost . |
Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19 (2021.eacl-main)
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Muhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi, Dinesh Pabbi, Kunal Verma, Rannie Lin
| Challenge: | a global pandemic of coronavirus disease 2019 has impacted millions of people . a human annotation study reveals the utility of our models on a subset of Mega-COV . |
| Approach: | They develop powerful models to analyze tweets related to the pandemic . they use a multilingual Twitter dataset with geo-location information . |
| Outcome: | The proposed model can identify whether a tweet is related to the pandemic and detect misinformation about it. |
Disfluency Correction using Unsupervised and Semi-supervised Learning (2021.eacl-main)
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Nikhil Saini, Drumil Trivedi, Shreya Khare, Tejas Dhamecha, Preethi Jyothi, Samarth Bharadwaj, Pushpak Bhattacharyya
| Challenge: | Disfluencies in conversational speech can affect performance of downstream NLP tasks. |
| Approach: | They propose a disfluency correction model that converts disfluent to fluent text . they use unsupervised encoder-decoder models to generate semi-supervised models . |
| Outcome: | The proposed model achieves a BLEU score of 79.39 on the Switchboard corpus test set and 85.28 with semi-supervision. |
Complex Question Answering on knowledge graphs using machine translation and multi-task learning (2021.eacl-main)
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Saurabh Srivastava, Mayur Patidar, Sudip Chowdhury, Puneet Agarwal, Indrajit Bhattacharya, Gautam Shroff
| Challenge: | Existing approaches to question answering on knowledge graphs are based on a modularized sequential approach where errors in one module lead to the accumulation of errors in downstream modules. |
| Approach: | They propose a multi-task BERT based Neural Machine Translation model to address these challenges. |
| Outcome: | The proposed model can answer questions over a knowledge graph on one publicly available and one proprietary dataset. |
Recipes for Adapting Pre-trained Monolingual and Multilingual Models to Machine Translation (2021.eacl-main)
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| Challenge: | Recent advances in machine translation (MT) have improved performance on low-resource language pairs. |
| Approach: | They propose to freeze most BART parameters and add new ones to fine-tune a model trained on MT. |
| Outcome: | The proposed model outperforms naive fine-tuning on Vietnamese to English on a training set for Vietnamese to Vietnamese . the proposed model is able to fine- tune on smaller datasets while still maintaining the same model performance. |
From characters to words: the turning point of BPE merges (2021.eacl-main)
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| Challenge: | morphological complexity is still a major challenge for NLP and the study of language. |
| Approach: | They perform a cross-linguistic comparison following incremental merges of BPE for 47 diverse languages. |
| Outcome: | The results show that language distributions are similar under specific levels of tokenization. |
A Large-scale Evaluation of Neural Machine Transliteration for Indic Languages (2021.eacl-main)
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| Challenge: | We analyze multilingual transliteration for Indic languages using scripts derived from the ancient Brahmi script. |
| Approach: | They propose a multilingual training recipe for Indic languages that utilizes orthographic similarity between English and Indic. |
| Outcome: | The proposed training recipe improves multilingual transliteration for Indic languages. |
Communicative-Function-Based Sentence Classification for Construction of an Academic Formulaic Expression Database (2021.eacl-main)
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| Challenge: | Formulaic expressions (FEs) are used in scientific papers. |
| Approach: | They propose to use top–down approach to assign CF labels to sentences and then extract FEs from them using a CF-labelled sentence dataset. |
| Outcome: | The proposed method can be used to build FE databases of disciplines that are different from the training data. |
Regulatory Compliance through Doc2Doc Information Retrieval: A case study in EU/UK legislation where text similarity has limitations (2021.eacl-main)
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| Challenge: | Major scandals in corporate history have urged the need for regulatory compliance, where organizations need to ensure that their controls (processes) comply with relevant laws, regulations, and policies. |
| Approach: | They introduce regulatory information retrieval (REG-IR) an application of document-to-document information retrievals where the query is an entire document making the task more challenging than traditional IR where the queries are short. |
| Outcome: | The proposed approach is more challenging than traditional IR where the query is an entire document making the task more challenging. |
The Chinese Remainder Theorem for Compact, Task-Precise, Efficient and Secure Word Embeddings (2021.eacl-main)
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| Challenge: | a new method for compressing word vector embeddings into integers is being developed . a high precision approach to compressing words into integer results in negligible performance gains . |
| Approach: | They propose a method for compressing word vector embeddings into integers using the Chinese Reminder Theorem. |
| Outcome: | The proposed method speeds up addition by 48.27% and compresses GloVe word embedding libraries by 25.86%. |
Don’t Change Me! User-Controllable Selective Paraphrase Generation (2021.eacl-main)
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| Challenge: | a new technique allows paraphrase generation to be user-controlled . a user looking for cheap hotels in New York would not find the other answer helpful . |
| Approach: | They propose a method that provides a user with explicit tags that can be placed around any arbitrary segment of text to mean "don't change me!" they propose allowing user-controllable paraphrase generation by fine-tuning model that exhibits this behavior . |
| Outcome: | The proposed technique is language agnostic and tested in English and Chinese. |
Rethinking Coherence Modeling: Synthetic vs. Downstream Tasks (2021.eacl-main)
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| Challenge: | Coherence models are typically evaluated only on synthetic tasks, which may not be representative of their performance in downstream applications. |
| Approach: | They compare models' performance on synthetic sentences with those on retrieval-based dialog. |
| Outcome: | The proposed models perform poorly on synthetic sentences and retrieval-based dialog tasks. |
From the Stage to the Audience: Propaganda on Reddit (2021.eacl-main)
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| Challenge: | a recent opinion piece in the Washington Post highlights a difference between the political discourse in the two countries. |
| Approach: | They analyze political forums on Reddit that target a diverse audience in two countries . they focus on three research questions: who is posting propaganda? and how is propaganda received? |
| Outcome: | The authors analyze political forums on reddit in the US and the UK for one year . they find that propaganda is misleading and is received by different audiences . |
Probing for idiomaticity in vector space models (2021.eacl-main)
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| Challenge: | Contextualised word representation models are used to represent idiomaticity in language. |
| Approach: | They propose probing measures to assess if some of the expected linguistic properties of noun compounds are readily available in some standard and widely used representations. |
| Outcome: | The proposed models show that idiomaticity is not yet accurately represented by contextualised models. |
Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension? (2021.eacl-main)
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| Challenge: | Existing benchmarks for machine reading comprehension (MRC) are insufficient to assess models for their capabilities to read and comprehend . |
| Approach: | They propose an ablation-based method to assess the extent to which MRC datasets evaluate the understanding of explicit discourse relations. |
| Outcome: | The proposed method shows that the model's performance drops on three large-scale datasets . the results suggest that most of the answers do not require understanding the discourse structure of the text. |
Why Is MBTI Personality Detection from Texts a Difficult Task? (2021.eacl-main)
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| Challenge: | Automatic detection of the four MBTI personality dimensions from texts has attracted noticeable attention from the natural language processing and computational linguistic communities. |
| Approach: | They propose to use a questionnaire-based personality assessment to provide more objective assessment of one's personality than traditional questionnaires. |
| Outcome: | The proposed systems rarely outperform the majority-class baseline despite large datasets and high levels of noise in training datasets. |
Enconter: Entity Constrained Progressive Sequence Generation via Insertion-based Transformer (2021.eacl-main)
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| Challenge: | Autoregressive language models do not perform well under hard lexical constraints as they lack fine control of content generation process. |
| Approach: | They propose a new insertion transformer that considers hard lexical constraints and imposes rules over objects in the generated text. |
| Outcome: | The proposed model outperforms baseline models in several performance metrics rendering it more suitable in practical applications. |
Meta-Learning for Effective Multi-task and Multilingual Modelling (2021.eacl-main)
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| Challenge: | Existing studies on multitask and multilingual learning have shown that learning cross-lingual embeddings can benefit multiple tasks and languages. |
| Approach: | They propose a meta-learning approach to learn interactions between tasks and languages . they also investigate the role of different sampling strategies used during meta-learned model . |
| Outcome: | The proposed model improves on five different tasks and six different languages from the XTREME multilingual benchmark dataset. |
“Killing Me” Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism (2021.eacl-main)
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| Challenge: | Several attention-based spoiler detection models are insufficient for utilizing dependency relations between context words. |
| Approach: | They propose a new spoiler detection model called SDGNN that uses syntax-aware graph neural networks to detect dependency relations between context words. |
| Outcome: | The proposed model outperforms existing models on two real-world benchmark datasets. |
BERTese: Learning to Speak to BERT (2021.eacl-main)
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| Challenge: | Recent work shows that pre-trained language models encode large amounts of world knowledge in their parameters. |
| Approach: | They propose a method for automatically rewriting queries into a paraphrase query called "BERTese" they add auxiliary loss functions that encourage the query to correspond to actual language tokens . |
| Outcome: | The proposed method outperforms baselines and provides some insight into the type of language that helps language models perform knowledge extraction. |
Lifelong Knowledge-Enriched Social Event Representation Learning (2021.eacl-main)
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| Challenge: | Existing approaches to represent social events and situations fail to consider pragmatic aspects . a conceptual framework for lifelong language learning integrates commonsense knowledge with lifelong learning. |
| Approach: | They propose a representation learning framework that integrates pragmatic aspects with lifelong language learning. |
| Outcome: | The proposed model integrates commonsense knowledge with lifelong language learning to produce richer representations of social events. |
GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition (2021.eacl-main)
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| Challenge: | Named entity recognition (NER) models often need to be trained with many manual labels to perform well. |
| Approach: | They propose to train named entity recognition systems using heuristic labeling rules . they create a graph with nodes representing candidate rules extracted from unlabeled data . |
| Outcome: | The proposed method achieves an average improvement of +20% over the baseline on three NER datasets. |
An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning (2021.eacl-main)
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| Challenge: | Using a multi-task approach, we extract facts from documents at entity level. |
| Approach: | They propose a multi-task approach that builds upon coreference resolution and gathers relevant signals via multi-instance learning with multi-level representations combining global entity and local mention information. |
| Outcome: | The proposed model is on par with task-specific learning, though more efficient due to shared parameters and training steps. |
WER-BERT: Automatic WER Estimation with BERT in a Balanced Ordinal Classification Paradigm (2021.eacl-main)
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| Challenge: | Automatic Speech Recognition (ASR) systems are evaluated using Word Error Rate (WER) a higher WER means a lower percentage of errors between the ground truth and the transcription of the system. |
| Approach: | They propose a new balanced paradigm for automatic Word Error Rate estimation using a Librispeech dataset and a Google Cloud's Speech-to-Text API. |
| Outcome: | The proposed approach is more effective than regression in a classification setting, but suffers from heavy class imbalance. |
Two Training Strategies for Improving Relation Extraction over Universal Graph (2021.eacl-main)
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| Challenge: | Existing methods for Distantly Supervised Relation Extraction (DS-RE) with a UG may lead to degradation in performance. |
| Approach: | They propose to use a Universal Graph (UG) to train a distantly supervised relation extraction model. |
| Outcome: | The proposed training strategies on biomedical and NYT10 datasets prove the robustness of the proposed methods and achieve state-of-the-art results. |
Adaptation of Back-translation to Automatic Post-Editing for Synthetic Data Generation (2021.eacl-main)
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| Challenge: | Automated Post-Editing (APE) aims to correct errors in the output of a given machine translation system. |
| Approach: | They propose two new methods of synthesizing additional MT outputs by adapting back-translation to the APE task, obtaining robust enlargements of existing synthetic APE training dataset. |
| Outcome: | The proposed methods improve translation quality on the English-German APE task by enlarging the existing training dataset. |
Removing Word-Level Spurious Alignment between Images and Pseudo-Captions in Unsupervised Image Captioning (2021.eacl-main)
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| Challenge: | Unsupervised image captioning is a challenging task that requires manual annotation. |
| Approach: | They propose a simple gating mechanism that is trained to align image features with the most reliable words in pseudo-captions. |
| Outcome: | The proposed method outperforms the previous methods without complex learning objectives. |
Towards More Fine-grained and Reliable NLP Performance Prediction (2021.eacl-main)
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| Challenge: | Performance prediction is a task of estimating a system’s performance without performing experiments. |
| Approach: | They propose to understand reliability of performance prediction models from two angles: confidence intervals and calibration. |
| Outcome: | The proposed methods demonstrate the feasibility of fine-grained performance prediction and the necessity to perform reliability analysis for performance prediction methods in the future. |
Metrical Tagging in the Wild: Building and Annotating Poetry Corpora with Rhythmic Features (2021.eacl-main)
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| Challenge: | a prerequisite for the computational study of literature is the availability of properly digitized texts with reliable meta-data and ground-truth annotation. |
| Approach: | They propose to annotate prosodic features in large poetry corpora for English and German and train corpus driven neural models that enable large scale analysis. |
| Outcome: | The proposed models outperform baseline and BERT-based approaches in English and german and show that they learn foot boundaries better when jointly predicting syllable stress, aesthetic emotions and verse measures benefit from each other. |
Enhancing Aspect-level Sentiment Analysis with Word Dependencies (2021.eacl-main)
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| Challenge: | Existing approaches to enhance aspect-level sentiment analysis have omitted syntactic information . experimental results show that our approach outperforms baseline models on all datasets . |
| Approach: | They propose to leverage word dependencies to enhance aspect-level sentiment analysis . they propose to use key-value memory networks to leverage different dependency results . |
| Outcome: | The proposed approach outperforms baseline models on all datasets and achieves state-of-the-art performance on three of them. |