Papers with Information Extraction & Text Mining
Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track) (2026.eacl-industry)
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| Challenge: | EACL 2026 Industry Track attracted 170 submissions, and 167 reviewers contributed to the review process. |
| Approach: | EACL 2026 Industry Track attracted 170 submissions, 167 reviewers contributed to the review process. 71 papers were selected for presentation at the track. |
| Outcome: | EACL 2026 Industry Track attracted 170 submissions, and 167 reviewers contributed to the review process. |
TexSmart: A System for Enhanced Natural Language Understanding (2021.acl-demo)
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Lemao Liu, Haisong Zhang, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng, Botong Zhou, Dick Zhu, Xiao Feng, Tao Chen, Tao Yang, Dong Yu, Feng Zhang, ZhanHui Kang, Shuming Shi
| Challenge: | TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications. |
| Approach: | They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities. |
| Outcome: | The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions. |
Neural Network based Extreme Classification and Similarity Models for Product Matching (N18-3)
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| Challenge: | Matching a seller listed item to an appropriate product has become a fundamental step for e-commerce platforms. |
| Approach: | They propose to use a shallow neural network to match a seller's item to an appropriate product . they also propose a similarity approach based on deep siamese network to train and infer product information. |
| Outcome: | The proposed models outperform the baseline models by more than 5% in terms of accuracy and are capable of efficient training and inference. |
Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts (2021.findings-emnlp)
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| Challenge: | Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. |
| Approach: | They develop a neural topic model which extracts topics from word co-occurrence graphs . Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. |
| Outcome: | Empirical results show that the proposed model can generate more coherent topics than baseline topic models. |
Abstractive Timeline Summarization (D19-54)
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| Challenge: | Prior approaches to TLS focus on extractive methods, which generate extractive timelines . a study with human judges shows that our abstractive system also produces output that is easy to read and understand. |
| Approach: | They propose an abstractive timeline summarization system that is unsupervised . their system outperforms extractive systems in terms of ROUGE scores . |
| Outcome: | The proposed system outperforms extractive systems in terms of ROUGE scores . it produces output that is easy to read and understand, the authors say . |
elfen: A Python Package for Efficient Linguistic Feature Extraction for Natural Language Datasets (2026.eacl-demo)
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| Challenge: | elfen is a Python library for efficient linguistic feature extraction for text datasets. |
| Approach: | They propose a Python library for efficient linguistic feature extraction for text datasets. |
| Outcome: | The proposed library enables linguistic feature extraction on thousands of items even on limited computing resources. |
A Korean Knowledge Extraction System for Enriching a KBox (C18-2)
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| Challenge: | Existing systems for knowledge extraction from natural language sentences are lacking for all languages. |
| Approach: | They propose a Korean knowledge extraction system and web interface for enriching a KBox knowledge base based on the Korean DBpedia. |
| Outcome: | The proposed system can extract factual knowledge from natural language sentences . the endpoint can be used to add knowledge to a KBox knowledge base anytime and anywhere . |
Event Time Extraction and Propagation via Graph Attention Networks (2021.naacl-main)
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| Challenge: | Existing work on grounding events into a precise timeline has been limited due to the inherent ambiguity of language and the requirement for information propagation over inter-related events. |
| Approach: | They propose a 4-tuple temporal representation for entity slot filling to ground events into a timeline using a graph attention network approach. |
| Outcome: | The proposed approach yields 7.0% match rate over contextualized embedding approaches and 16.3% higher match rate compared to sentence-level manual event time argument annotation. |
Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction (D19-51)
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| Challenge: | Existing studies on forex prediction ignore related text completely and focus on forex trade data only, which loses important semantic information. |
| Approach: | They propose a BERT-based Hierarchical Aggregation Model to summarize forex news . they group news from different aspects and extract the most crucial news in each group . |
| Outcome: | The proposed model outperforms baseline methods and grouping methods and summarizes the influence patterns for forex trading. |
Train, Sort, Explain: Learning to Diagnose Translation Models (N19-4)
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| Challenge: | Evaluating translation models is a trade-off between effort and detail. |
| Approach: | They propose to use a neural text classifier to automatically expose systematic differences between human and machine translations to human experts. |
| Outcome: | The proposed method exposes systematic differences between human and machine translations to human experts. |
A reproduction of Apple’s bi-directional LSTM models for language identification in short strings (2021.eacl-srw)
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| Challenge: | Language Identification is the task of identifying a document’s language. |
| Approach: | They propose to use bi-LSTMs to identify language on very short strings such as text message fragments to perform automatic spell check. |
| Outcome: | The proposed model outperforms open-source language identifiers and its language identification mistakes are due to confusion between related languages. |
Neural Models for Reasoning over Multiple Mentions Using Coreference (N18-2)
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| Challenge: | Existing Recurrent Neural Network (RNN) layers are biased towards short-term dependencies and hence not suited to such tasks. |
| Approach: | They propose a recurrent layer which is instead biased towards coreferent dependencies and uses coreference annotations extracted from an external system to connect entity mentions belonging to the same cluster. |
| Outcome: | The proposed layer improves performance on Wikihop, LAMBADA and the bAbi AI datasets with large gains when training data is scarce. |
An Editorial Network for Enhanced Document Summarization (D19-54)
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| Challenge: | Existing extractive and abstractive summarization methods are less fluent, coherent and readable, whereas extractive methods are sensitive to vocabulary size, making them more difficult to train and generalize. |
| Approach: | They propose an approach which uses a combination of extractive and abstractive methods to combine a given sequence of sentences into a short version. |
| Outcome: | The proposed method is compared with state-of-the-art methods using extractive-only or abstractive- only baselines. |
Structural Patent Classification Using Label Hierarchy Optimization (2025.findings-emnlp)
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| Challenge: | Existing methods for patent classification ignore key technical content claims and citation relationships . existing methods treat labels as independent targets, failing to exploit semantic and structural information within the label taxonomy. |
| Approach: | They propose a Claim Structure based Patent Classification model with Label Awareness . structural graph learning is used to mine the internal logic of patent claims . |
| Outcome: | The proposed method is more effective than state-of-the-art classification models. |
Joint Multimedia Event Extraction from Video and Article (2021.findings-emnlp)
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Brian Chen, Xudong Lin, Christopher Thomas, Manling Li, Shoya Yoshida, Lovish Chum, Heng Ji, Shih-Fu Chang
| Challenge: | Existing methods to extract multimedia events from video and text are limited to video and images. |
| Approach: | They propose a task to jointly extract events from video and text documents . they propose 'self-supervised' cross-modal event coreference model and cross-mod transformer architecture . |
| Outcome: | The proposed method achieves 6.0% and 5.8% absolute F-score gain on video-article pairs . the proposed method can resolve coreference and extract multimodal event frames more accurately than existing methods. |
Ultra-Fine Entity Typing (P18-1)
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| Challenge: | Experimental results show that a model that can predict ultra-fine types can be crowd-sourced . head words indicate the type of the noun phrases they appear in, and are important for context-sensitive tasks . |
| Approach: | They propose a task where sentences are given with an entity mention . they introduce a new type of distant supervision: head words, which indicate the type of noun phrases they appear in. |
| Outcome: | The proposed model can predict ultra-fine types at varying granularity and performs well on a fine-grained entity typing benchmark. |
Ask-and-Verify: Span Candidate Generation and Verification for Attribute Value Extraction (2022.emnlp-industry)
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| Challenge: | Existing reading comprehension models can over-generate attribute values which hinders precision. |
| Approach: | They propose a product attribute value extraction task that captures key factual information from product descriptions and a new end-to-end pipeline framework called Ask-and-Verify. |
| Outcome: | The proposed framework outperforms existing models by up to 3.1% F1 absolute improvement points while scaling to thousands of attributes. |
Language over Labels: Contrastive Language Supervision Exceeds Purely Label-Supervised Classification Performance on Chest X-Rays (2022.aacl-srw)
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| Challenge: | Pretrained CLIP models lack domain-specific knowledge of text and images. |
| Approach: | They adapt CLIP-based models to the chest radiography domain using contrastive language supervision and a detailed ablation study of the batch and dataset size. |
| Outcome: | The proposed model outperforms supervised learning on labels on the MIMIC-CXR dataset while generalizing to the CheXpert and RSNA Pneumonia datasets. |
IDP Accelerator: Agentic Document Intelligence from Extraction to Compliance Validation (2026.acl-demo)
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Md Mofijul Islam, Md Sirajus Salekin, Joe King, Priyashree Roy, Vamsi Thilak Gudi, Spencer Romo, Akhil Nooney, Bob Strahan, Boyi Xie, Diego A. Socolinsky
| Challenge: | Large Language Models (LLMs) are inadequate for extracting structured insights from unstructured documents. |
| Approach: | They propose a framework enabling agentic AI for end-to-end document intelligence with four key components: DocSplit, configurable Extraction Module, and Rule Validation Module. |
| Outcome: | The proposed framework achieves 98% classification accuracy, 80% reduced processing latency, and 77% lower operational costs over legacy baselines. |
IEPile: Unearthing Large Scale Schema-Conditioned Information Extraction Corpus (2024.acl-short)
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| Challenge: | Large Language Models exhibit a significant performance gap in Information Extraction (IE) high-quality instruction data is the vital key for enhancing LLMs' specific capabilities . |
| Approach: | They propose a bilingual (English and Chinese) IE instruction corpus that contains 0.32B tokens. |
| Outcome: | The proposed model improves the performance of LLMs for IE with zero-shot generalization. |
A Walk-based Model on Entity Graphs for Relation Extraction (P18-2)
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| Challenge: | Existing models treat each relation in a sentence individually, but a graph-based model needs to consider multiple relations between entities to model the dependencies among them. |
| Approach: | They propose a graph-based neural network model that treats multiple pairs in a sentence simultaneously and considers interactions among them. |
| Outcome: | The proposed model performs comparable to the state-of-the-art systems on the ACE 2005 dataset without external tools. |
Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction (P18-2)
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| Challenge: | et al., 1998: bootstrapping for relation extraction uses minimally supervised methods . etudes show that proposed methods for automatic seed selection and noise reduction are better than baseline systems . |
| Approach: | They propose automatic seed selection and noise reduction for distantly supervised relation extraction tasks. |
| Outcome: | The proposed methods achieve better performance than baseline systems in both tasks. |
LLMs Underperform Graph-Based Parsers on Supervised Relation Extraction for Complex Graphs (2026.acl-short)
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| Challenge: | Relation extraction is a core NLP task which involves extracting [head, relation, dependent] RDF triples from text. |
| Approach: | They evaluate four large language models against a graph-based parser on six relation extraction datasets with sentence graphs of varying sizes and complexities. |
| Outcome: | The graph-based parser outperforms the LLMs on six relation extraction datasets with sentence graphs of varying sizes and complexities. |
Recognizing UMLS Semantic Types with Deep Learning (D19-62)
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Isar Nejadgholi, Kathleen C. Fraser, Berry De Bruijn, Muqun Li, Astha LaPlante, Khaldoun Zine El Abidine
| Challenge: | Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. |
| Approach: | They propose to use general and domain-specific information to combine general and specific information to create a new entity recognition method. |
| Outcome: | The proposed method produces a state-of-the-art result on a newly released dataset, MedMentions. |
A Graphical Interface for Curating Schemas (2021.acl-demo)
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| Challenge: | Existing work on analyzing information extracted from documents has focused on examining the model understanding of complex schemas. |
| Approach: | They propose a curation interface that takes an IE system’s output in a pre-defined format and generates a graphical representation of its elements. |
| Outcome: | The proposed interface can be used to edit and prune schemas for complex events like Improvised Explosive Device (IED) based scenarios. |
Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks (2023.acl-srw)
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| Challenge: | Current approaches to OOD detection in NLP are not yet sufficiently sensitive to capture all samples characterized by various types of distributional shifts. |
| Approach: | They evaluated eight methods that are easily integrable into existing NLP systems and require no additional OOD data or model modifications. |
| Outcome: | The proposed methods are easily integrable into existing NLP systems and require no additional OOD data or model modifications. |
Leveraging Dependency Forest for Neural Medical Relation Extraction (D19-1)
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| Challenge: | Existing methods for medical relation extraction use dependency syntax as a source of features. |
| Approach: | They propose a method to extract relational information from medical literature by using dependency forests. |
| Outcome: | The proposed method outperforms the standard tree-based methods in the medical domain. |
Event Ontology Completion with Hierarchical Structure Evolution Networks (2023.emnlp-main)
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| Challenge: | Existing methods for event detection require predefined schemas, but manual defining is expensive and labor-intensive. |
| Approach: | They propose a task to achieve event clustering, hierarchy expansion and type naming . they propose 'neighbor Contrastive Clustering' module and a Hierarchy-Aware Linking module . |
| Outcome: | The proposed method outperforms baseline methods on three datasets. |
Graphene: a Context-Preserving Open Information Extraction System (C18-2)
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| Challenge: | Graphene is an open IE system that generates accurate, meaningful and complete propositions . current systems tend to extract propositions with long argument phrases that can be further decomposed into meaningful propositions, with each of them representing a separate fact. |
| Approach: | They propose a lightweight Open IE system that generates accurate, meaningful propositions . they identify the rhetorical relations that hold between them to maintain their semantic relationship . |
| Outcome: | The proposed system generates propositions that are accurate, meaningful and complete . it preserves the context of the relational tuples extracted from the source sentence . |
TermoUD - a language-independent terminology extraction tool (2023.eacl-demo)
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| Challenge: | TermoUD is a language-independent terminology extraction tool . it uses languagedependent shallow grammar to select candidate terms . |
| Approach: | They propose a language-independent terminology extraction tool called TermoUD which uses shallow grammar to select candidate terms. |
| Outcome: | The proposed method is suitable for languages with the Universal Dependencies (UD) parser. |
Regularized Graph Convolutional Networks for Short Text Classification (2020.coling-industry)
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| Challenge: | Short text classification is a problem in natural language processing, social network analysis, and e-commerce. |
| Approach: | They propose a short text classification technique that incorporates label dependencies into the output space to overcome the limitations of short text. |
| Outcome: | The proposed model outperforms baseline methods on proprietary and external datasets and is more robust to noise in textual features. |
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. |
A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications (D19-53)
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| Challenge: | Existing methods for relation extraction rely on lexical patterns and dependency templates. |
| Approach: | They propose a rule-based method for extracting entity networks from scientific literature . they use syntactic features of constituent parsing trees to extract and construct graphs . |
| Outcome: | The proposed method outperforms state-of-the-art methods in several cases. |
CRAFT Shared Tasks 2019 Overview — Integrated Structure, Semantics, and Coreference (D19-57)
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William Baumgartner, Michael Bada, Sampo Pyysalo, Manuel R. Ciosici, Negacy Hailu, Harrison Pielke-Lombardo, Michael Regan, Lawrence Hunter
| Challenge: | CRAFT corpus provides a unique foundation for integrating natural language processing (NLP) tasks involving structure, semantics, and coreference. |
| Approach: | They propose to use the CRAFT corpus to evaluate three fundamental language processing tasks over full-text biomedical articles. |
| Outcome: | The CRAFT corpus provides a unique foundation for integrating natural language processing tasks involving structure, semantics, and coreference. |
Corpus Creation and Analysis for Named Entity Recognition in Telugu-English Code-Mixed Social Media Data (P19-2)
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| Challenge: | Named Entity Recognition (NER) is a subtask of Information Extraction in NLP. |
| Approach: | They present a Telugu-English code-mixed corpus with the corresponding named entity tags. |
| Outcome: | The proposed model scored 0.96, 0.94 and 0.95 on a Telugu-English code-mixed corpus. |
CoRefi: A Crowd Sourcing Suite for Coreference Annotation (2020.emnlp-demos)
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| Challenge: | Using a web-based coreference annotation suite, we demonstrate that non-expert annotators can be trained to perform and review coreference resolution tasks. |
| Approach: | They propose a web-based coreference annotation suite oriented for crowdsourcing that provides guided onboarding and a novel algorithm for a reviewing phase. |
| Outcome: | The proposed tool provides guided onboarding and a novel algorithm for a review phase. |
Hierarchy Builder: Organizing Textual Spans into a Hierarchy to Facilitate Navigation (2023.acl-demo)
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| Challenge: | Information extraction systems produce hundreds to thousands of strings on a specific topic. |
| Approach: | They propose a method that allows users to consume a large collection of related textual strings in an exploratory mode. |
| Outcome: | The proposed method allows users to consume a large collection of related textual strings in an exploratory mode. |
STREAM-ZH: Simplified Topic Retrieval Exploration and Analysis Module for Chinese Language (2026.eacl-short)
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| Challenge: | Simplified Topic Retrieval Exploration and Analysis Module for Chinese language is the first topic modeling package to fully support the Chinese language. |
| Approach: | They propose a topic modeling package that fully supports the Chinese language . they use preprocessed textual datasets to assess topic models . |
| Outcome: | The proposed framework outperforms existing topic models using English-translated textual input. |
Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions (2024.acl-demos)
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| Challenge: | Fundus is a news scraper that extracts news articles from the web with just a few lines of code. |
| Approach: | They introduce Fundus, a news scraper that enables users to obtain news articles with just a few lines of code. |
| Outcome: | The proposed news scraper optimizes for quality and provides a unified interface for newspapers. |
Contextualized Weak Supervision for Text Classification (2020.acl-main)
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| Challenge: | Existing methods for weakly supervised text classification generate pseudo-labels in a context-free manner, thus, the ambiguous, context-dependent nature of human language has been long overlooked. |
| Approach: | They propose a framework that provides contextualized weak supervision for text classification . they leverage contextualized representations of word occurrences and seed word information . |
| Outcome: | The proposed framework provides contextualized weak supervision for text classification . it leverages representations of word occurrences and seed word information to differentiate interpretations . the proposed framework also disambiguates initial seed words, making it fully contextualized . |
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories (L18-1)
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| Challenge: | a new dataset is used to classify text into positive, negative, and neutral classes . a large amount of work on automatic detecting emotions from text has focused on classifying text into basic emotion categories . |
| Approach: | They use Twitter as the source of the textual data they annotate to find out which emotions often present together in tweets . |
| Outcome: | The proposed dataset is useful for training and testing supervised machine learning algorithms . it is based on the results of the SemEval-2018 task 1: Affect in Tweets . |
SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction (2025.coling-main)
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| Challenge: | Joint relation extraction models face high computational complexity, complex network architectures, difficult parameter tuning and limited interpretability. |
| Approach: | They develop a candidate label marker mechanism that prioritizes strategic label selection over simple label generation. |
| Outcome: | The proposed candidate label marks improve the SOTA methods by 2.5%, 1.9%, 1.2% . the proposed candidate labels improve the performance of the proposed methods . |
LTV: Labeled Topic Vector (C18-2)
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| Challenge: | Using nnDDC, we generate labeled topic classifications based on the Dewey Decimal Classification (DDC) Unlike related approaches, we use classifiers to define the dimensions of CISS, which are directly labeles by the underlying target class. |
| Approach: | They propose a website and API that generates labeled topic classifications based on the Dewey Decimal Classification (DDC) they propose nnDDC, a largely language-independent natural network-based classifier for DDC, which is language-dependent . |
| Outcome: | The proposed model is language-independent and performs well in 40 languages. |
Simple Large-scale Relation Extraction from Unstructured Text (L18-1)
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| Challenge: | Knowledge-based question answering relies on the availability of facts, most of which cannot be found in structured sources. |
| Approach: | They propose a method for creating distant (weak) supervision labels for training a large-scale RE system by decoupling the model architecture from the feature design of a state-of-the-art neural network system. |
| Outcome: | The proposed method performs on par with the state-of-the-art model with similar features at 75x reduction in training time. |
CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset (2021.emnlp-demo)
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Baoli Zhang, Zhucong Li, Zhen Gan, Yubo Chen, Jing Wan, Kang Liu, Jun Zhao, Shengping Liu, Yafei Shi
| Challenge: | Existing crowd annotation tools for named entity recognition (NER) focus on efficiency and don't consider consistency of datasets. |
| Approach: | They propose a crowd annotation platform for Chinese named entity recognition (NER) CroAno provides a systematic solution for improving label consistency of Chinese NER datasets. |
| Outcome: | The proposed platform improves label consistency of Chinese NER datasets. |
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction (D19-1)
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| Challenge: | Existing event extraction methods are limited to extract event arguments within the sentence scope. |
| Approach: | They propose a model which generates an entity-based directed acyclic graph to fulfill document-level EE effectively. |
| Outcome: | The proposed model can generate entity-based directed acyclic graph to fulfill document-level EE effectively. |
STRASS: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings (P19-2)
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| Challenge: | Summarization is a costly and timedemanding task. |
| Approach: | They propose an extractive text summarization method which leverages the semantic information in existing sentence embedding spaces. |
| Outcome: | The proposed method performs similarly to state-of-the-art extractive methods with effective training and inference time. |
Chop and Change: Anaphora Resolution in Instructional Cooking Videos (2022.findings-aacl)
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| Challenge: | temporally evolving entities present challenges for anaphora resolution tasks . recipes provide rich source for referring expressions of transformed entities . |
| Approach: | They propose to use annotations to annotate recipes for anaphora resolution task . they propose to employ temporal features to improve anamorphic resolution . |
| Outcome: | The proposed annotation scheme improves the performance of the anaphora resolution task. |
Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation (2024.findings-eacl)
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Shohei Higashiyama, Hiroki Ouchi, Hiroki Teranishi, Hiroyuki Otomo, Yusuke Ide, Aitaro Yamamoto, Hiroyuki Shindo, Yuki Matsuda, Shoko Wakamiya, Naoya Inoue, Ikuya Yamada, Taro Watanabe
| Challenge: | et al., 2006) considers geographic relatedness among geo-entity mentions in document-level geoparsing. |
| Approach: | They present a Japanese travelogue dataset that considers geographic relatedness among geo-entity mentions. |
| Outcome: | The proposed dataset includes 200 travelogue documents with rich geo-entity information . it shows that human activities, mobility, and events are often described with natural language expressions of locations or geographic entities (geo-entities) |
GAINER: Graph Machine Learning with Node-specific Radius for Classification of Short Texts and Documents (2024.eacl-long)
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| Challenge: | Recent advances in Graph Machine Learning (GML) have led to the development of numerous models tailored for processing text for various natural language applications. |
| Approach: | They propose a framework called Graph mAchine learnIng with Node-spEcific Radius that is aimed at graph-based NLP. |
| Outcome: | The proposed framework is non-neural and novel for graph-based NLP. |
Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup (2020.acl-srw)
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| Challenge: | Towards this goal, many studies have focused on disaster-related tweet classification. |
| Approach: | They compile a multilingual dataset for multi-label classification of disaster-related tweets . they show that their model generalizes to unseen disasters in the test set . |
| Outcome: | The proposed model generalizes to unseen disasters and improves with Manifold Mixup. |
LiDARR: Linking Document AMRs with Referents Resolvers (2025.acl-demo)
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Jon Cai, Kristin Wright-Bettner, Zekun Zhao, Shafiuddin Rehan Ahmed, Abijith Trichur Ramachandran, Jeffrey Flanigan, Martha Palmer, James Martin
| Challenge: | Abstract Meaning Representation (AMR) is a formalism for semantic representation of natural language text. |
| Approach: | They propose a web tool for semantic annotation at the document level using Abstract Meaning Representation (AMR) it integrates an AMR-to-surface alignment model and a coreference resolution model into the tool . |
| Outcome: | The proposed tool simplifies the creation of knowledge graphs from natural language documents . it integrates an AMR-to-surface alignment model and coreference resolution model . |
Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis (N18-2)
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Devamanyu Hazarika, Soujanya Poria, Prateek Vij, Gangeshwar Krishnamurthy, Erik Cambria, Roger Zimmermann
| Challenge: | Present neural-based models exploit aspect and its contextual information in the sentence but ignore inter-aspect dependencies. |
| Approach: | They propose to combine aspect-based sentiment analysis with temporal dependency processing to incorporate this pattern into a sentence. |
| Outcome: | The proposed approach is based on the SemEval 2014 dataset and shows that it is effective for predicting sentiments of aspects in sentences with multiple aspects. |
KazNERD: Kazakh Named Entity Recognition Dataset (2022.lrec-1)
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| Challenge: | Named entity recognition (NER) is a subtask of information extraction aimed at identifying named entities (NEs) in semi-or unstructured text and classifying them into pre-specified types. |
| Approach: | They present a dataset for Kazakh named entity recognition using an annotation scheme and guidelines for annotation. |
| Outcome: | The dataset contains 112,702 sentences and 136,333 annotations for 25 entity classes. |
Tethering Broken Themes: Aligning Neural Topic Models with Labels and Authors (2025.findings-naacl)
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| Challenge: | Recent studies suggest that topic models do not align well with human intentions. |
| Approach: | They propose a method to align neural topic models with both labels and authorship information. |
| Outcome: | The proposed method improves existing models in terms of topic quality and alignment. |
Hierarchical Attention Prototypical Networks for Few-Shot Text Classification (D19-1)
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| Challenge: | Existing methods for text classification are based on large-scale labeled data, but few data are available. |
| Approach: | They propose a hierarchical attention prototypical networks for few-shot text classification . they use attention mechanism to highlight or weaken the importance of features, words, and instances . |
| Outcome: | The proposed model can capture more important features, words, and instances . it can also increase support set augmentability and accelerate convergence speed in training stage . |
OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding (2023.emnlp-demo)
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| Challenge: | Event understanding is fundamental for humans to understand the world. |
| Approach: | They propose an event understanding toolkit called OmniEvent that is comprehensive and fair . it supports mainstream modeling paradigms and the processing of 15 widely-used datasets . |
| Outcome: | The toolkit supports mainstream modeling paradigms and the processing of 15 widely-used English and Chinese datasets. |
SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction (2021.findings-acl)
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| Challenge: | Document-level relation extraction (doc-level RE) is a classification problem that predicts relations for all entity pairs in a document. |
| Approach: | They propose a document-level relation extraction architecture to represent intra- and inter-sentential relations in different ways. |
| Outcome: | The proposed architecture outperforms the state-of-the-art methods on the public datasets. |
PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space (D19-1)
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| Challenge: | Existing approaches to reviewer-paper matching are less effective to deal with the vocabulary mismatch and partial topic overlap between the submission and reviewer. |
| Approach: | They propose to combine the common topic model and abstract topic vectors to model the topics common to the submission and the reviewer's profile while relying on abstract topic vectors. |
| Outcome: | The proposed model improves on the existing model on two datasets. |
Systems’ Agreements and Disagreements in Temporal Processing: An Extensive Error Analysis of the TempEval-3 Task (L18-1)
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| Challenge: | Temporal Processing systems are crucial for timelines and storylines . TempEval-3 is the latest evaluation campaign on open-domain TP in english . |
| Approach: | They present a Temporal Processing system that incorporates high level lexical semantic features and uses them to evaluate temporal relation classification. |
| Outcome: | The proposed system achieves the best scores for event detection and temporal relation classification from raw text, but the errors are not as robust as previous systems. |
Toward Automatic Delegation Extraction in Japanese Law (2026.eacl-industry)
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| Challenge: | a higher-level law authorizes a lower-level to implement detailed provisions, which is called delegation. |
| Approach: | They propose a two-stage pipeline system for automatic delegation annotation in Japanese law . they extract keywords that indicate delegation using a named entity recognition approach . |
| Outcome: | The proposed system shows sufficient performance to assist manual annotation in practice. |
Classification and Clustering of Arguments with Contextualized Word Embeddings (P19-1)
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| Challenge: | Existing methods for argument mining focus on analyzing local argumentation structures, but information-seeking approaches need to be able to deal with heterogeneous sources and topics. |
| Approach: | They propose to use contextualized word embeddings to classify and cluster topic-dependent arguments using a UKP Sentential Argument Mining Corpus and IBM Debater - Evidence Sentences datasets. |
| Outcome: | The proposed method improves state-of-the-art on argument classification and clustering tasks and across multiple datasets. |
Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis (P18-1)
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| Challenge: | Existing methods for predicate argument structure analysis are difficult and difficult . a Japanese model can detect a zero pronoun and identify a referent of the zero pronominator . |
| Approach: | They propose a model that performs coreference resolution and predicate argument structure analysis simultaneously. |
| Outcome: | The proposed model can improve the performance of the inter-sentential zero anaphora resolution drastically. |
Named Entity Recognition for Chinese biomedical patents (2020.coling-main)
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| Challenge: | Existing attempts to address NER for Chinese biomedical texts have been limited due to the amount of Chinese biomedicine discoveries being patented. |
| Approach: | They train and evaluate Chinese biomedical patents NER models based on BERT . their model is optimized for Chinese bio-patent data and scored an F1 . |
| Outcome: | The proposed model achieves an F1 score of 0.540.15 for Chinese biomedical patent data. |
CitationIE: Leveraging the Citation Graph for Scientific Information Extraction (2021.acl-long)
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| Challenge: | Existing work on scientific information extraction (SciIE) considers extraction solely based on the content of an individual paper, without considering the paper’s place in the broader literature. |
| Approach: | They propose to automate the extraction of key information from scientific documents by leveraging a complementary source: the citation graph of referential links between citing and cited papers. |
| Outcome: | The proposed model improves on a set of English-language scientific documents. |
A Practical Incremental Learning Framework For Sparse Entity Extraction (C18-1)
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| Challenge: | Existing approaches to extract entities from textual data are expensive and unattractive due to the high cost of training. |
| Approach: | They propose a framework that integrates Entity Set Expansion and Active Learning to reduce the cost of data annotation. |
| Outcome: | The proposed framework reduces the cost of sparse entity annotation by 85% and 45% while maintaining high accuracy. |
An Empirical Study on Fine-Grained Named Entity Recognition (C18-1)
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Khai Mai, Thai-Hoang Pham, Minh Trung Nguyen, Tuan Duc Nguyen, Danushka Bollegala, Ryohei Sasano, Satoshi Sekine
| Challenge: | Named entity recognition (NER) is a well studied topic in natural language processing. |
| Approach: | They propose to remove the CNN layer and use dictionary and category embeddings to improve Japanese FG-NER performance. |
| Outcome: | The proposed method improves Japanese FG-NER F-score from 66.76% to 75.18%. |
A Hybrid Supervised-LLM Pipeline for Actionable Suggestion Mining in Unstructured Customer Reviews (2026.eacl-industry)
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| Challenge: | Existing approaches to extract actionable suggestions from customer reviews are often mixed-intent, unstructured text. |
| Approach: | They propose a hybrid pipeline that uses a RoBERTa classifier and a precision–recall surrogate to extract actionable suggestions from customer reviews. |
| Outcome: | The proposed pipeline outperforms prompt-only, rule-based, and classifier-only baselines in extraction accuracy and cluster coherence. |
A Framework for Flexible Extraction of Clinical Event Contextual Properties from Electronic Health Records (2025.acl-industry)
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| Challenge: | EHRs contain vast amounts of valuable clinical data, stored as unstructured text. |
| Approach: | They propose a method that uses existing NER+L methods to classify medical entities at scale using a named entity recognition and linking task. |
| Outcome: | The proposed model outperforms Bi-LSTM in minority class tasks with up to 28% of the time and 32% faster training time. |
Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters (N19-1)
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| Challenge: | Existing literature analysis does not focus on roles of characters or on relationships between them. |
| Approach: | They propose to combine emotion and character identification into a unified framework for character network extraction from fictional texts. |
| Outcome: | The proposed task is based on fan-fiction short stories and is able to predict emotion relations in the extracted network graph. |
The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing (2021.acl-long)
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| Challenge: | Existing studies restrict modal expressions to a closed syntactic class . modal sense labels are vastly different across different studies, lacking an accepted standard . |
| Approach: | They propose a task where modal expressions can be words of any syntactic class and sense labels are drawn from a comprehensive taxonomy which harmonizes the modal concepts contributed by the different studies. |
| Outcome: | The proposed task is based on the Georgetown Gradable Modal Expressions corpus . it detects and classifies fine-grained modal concepts and associates them with modified events . |
Dynamic and Static Topic Model for Analyzing Time-Series Document Collections (P18-2)
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| Challenge: | a collection of documents often has dynamic structures, i.e., topics evolve along time depending on multiple topics in the past. |
| Approach: | They propose a dynamic and static topic model that considers dynamic and dynamic structures of topic evolution and static structures of the topic hierarchy at each time. |
| Outcome: | The proposed model outperforms conventional models on scientific papers . it shows that extracted topic structures are useful for analyzing research activities . |
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. |
The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing (2021.acl-long)
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| Challenge: | Pre-trained language models have improved the state-of-the-art results on many NLP applications. |
| Approach: | They propose a simple error regularization trick that improves confidence estimation without substantially increasing the computation budget. |
| Outcome: | The proposed regularization improves confidence estimation without increasing computation budget. |
A Compressive Memory-based Retrieval Approach for Event Argument Extraction (2025.coling-main)
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| Challenge: | Existing retrieval-based EAE methods have input length constraints and the gap between the retriever and the inference model. |
| Approach: | They propose a retrieval-based retrieval mechanism that overcomes input length constraints . they use compressive memory to cache retrieved information and support continuous updates . |
| Outcome: | The proposed method outperforms retrieval-based methods on three public datasets. |
Unsupervised Extractive Opinion Summarization Using Sparse Coding (2022.acl-long)
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| Challenge: | Existing methods for opinion summarization rely on human annotations, which may not be feasible. |
| Approach: | They propose to perform opinion summarization in an unsupervised manner by using a dictionary learning algorithm that implicitly captures semantic information from the review text. |
| Outcome: | The proposed algorithm performs well on SPACE and AMAZON datasets and performs controllable summarization to generate aspect-specific summaries using only a few samples. |
MMAR: Multilingual and Multimodal Anaphora Resolution in Instructional Videos (2024.findings-emnlp)
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| Challenge: | Existing approaches to multilingual anaphora resolution include images and video inputs. |
| Approach: | They propose to include multimodal information in the form of images in anaphora resolution tasks. |
| Outcome: | The proposed approach improves resolution by 10% for unseen languages. |
Spot the BlindSpots: Systematic Identification and Quantification of Fine-Grained LLM Biases in Contact Center Call Summarization (2025.emnlp-industry)
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| Challenge: | Abstractive summarization is a core application in contact centers, where Large Language Models generate millions of summaries of call transcripts daily. |
| Approach: | They propose a framework that uses an LLM as a zero-shot classifier to derive categorical distributions for each bias dimension in a pair of transcripts and its summary. |
| Outcome: | The proposed framework identifies and quantifies 15 operational bias dimensions and measures them using two metrics: Fidelity Gap and Coverage. |
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. |
Noisy Multi-Label Text Classification via Instance-Label Pair Correction (2024.findings-naacl)
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| Challenge: | Noise is a significant challenge for machine learning models, especially deep learning models. |
| Approach: | They propose a holistic selection metric that identifies noisy pairs while considering global loss information and instance-specific ranking information. |
| Outcome: | The proposed approach significantly improves performance in noisy multi-label text classification tasks. |
Event Semantic Classification in Context (2024.findings-eacl)
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| Challenge: | In this work, we focus on the semantic classification of events in context to help machines gain a deeper understanding of events. |
| Approach: | They propose to integrate event semantics into downstream tasks to help machines understand events better. |
| Outcome: | The proposed model improves the understanding of events in context. |
DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction (2022.acl-short)
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| Challenge: | Existing benchmarking datasets for multilingual relation extraction have been lacking . |
| Approach: | They propose to use a new benchmark dataset to study multilingual relation extraction task by distant supervision. |
| Outcome: | The proposed task is performed on a multilingual relation extraction dataset using an mBERT encoder. |
Cross-domain NER with Generated Task-Oriented Knowledge: An Empirical Study from Information Density Perspective (2024.emnlp-main)
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| Challenge: | Cross-domain Named Entity Recognition (CDNER) is crucial for Knowledge Graph (KG) construction and natural language processing (NLP) |
| Approach: | They propose to automatically generate task-oriented knowledge using large language models (LLMs) and then employ task-orientated pre-training (TOPT) to facilitate domain adaptation. |
| Outcome: | The proposed model can learn to distinguish between different entities and improve its domain adaptation. |
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts (P19-1)
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| Challenge: | Emotion cause extraction (ECE) aims at extracting potential causes behind certain emotions in text. |
| Approach: | They propose a 2-step task to extract potential pairs of emotions and corresponding causes in a document. |
| Outcome: | The proposed task is based on a benchmark emotion cause corpus. |
A Lexicon-Based Graph Neural Network for Chinese NER (D19-1)
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| Challenge: | Chinese named entity recognition models are vulnerable to word ambiguities due to the lack of global semantics and chain structure. |
| Approach: | They propose a lexicon-based graph neural network with global semantics to solve word ambiguities in Chinese named entity recognition (NER) Lexicons are used to construct the graph and provide word-level features. |
| Outcome: | The proposed model improves on four NER datasets on Chinese characters, potential words, and the whole-sentence semantics. |
A Mixed Hierarchical Attention Based Encoder-Decoder Approach for Standard Table Summarization (N18-2)
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| Challenge: | Structured data summarization involves generation of summaries from structured input data. |
| Approach: | They propose a hierarchical attention-based encoder-decoder model which leverages the structure in addition to the content of the tables. |
| Outcome: | The proposed model improves on the weathergov dataset by 30% over the current state-of-the-art. |
Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time (N18-1)
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| Challenge: | a novel topic model is proposed to allow topical trends to be captured in temporal collections of documents. |
| Approach: | They propose a novel unsupervised neural dynamic topic model where topics are influenced by topic discovery over time. |
| Outcome: | The proposed model shows better generalization, topic interpretation, evolution and trends compared to state-of-the-art models . |
Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization (2022.acl-long)
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| Challenge: | Abstractive summarization systems still suffer from faithfulness errors, authors say . prior work has proposed models that improve faithfulness, but it is unclear whether this improvement comes from an increased level of extractiveness of the outputs. |
| Approach: | They propose a faithfulness-abstractiveness trade-off curve that serves as a control . they also learn a selector to identify the most faithful and abstractive summary for a given document . |
| Outcome: | The proposed model achieves higher faithfulness scores while being abstractive than the baseline system on two datasets. |
Conundrums in Event Coreference Resolution: Making Sense of the State of the Art (2021.emnlp-main)
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| Challenge: | Recent years have seen the successful application of span-based neural models to entity-based information extraction tasks such as entity coreference resolution (CR) Existing event coreference resolvers focused on feature engineering are few and far between, let alone event corefers. |
| Approach: | They propose to adapt existing span-based event reference systems to event coreference by adapting the models originally developed for entity coreference to event CR. |
| Outcome: | The proposed model improves the representations of entity mentions in entity-based IE tasks compared to non-span models . |
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition (2023.findings-acl)
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Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, Bingzhe Wu
| Challenge: | Named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty. |
| Approach: | They propose to introduce two uncertainty-guided loss terms to the conventional EDL and a series of uncertainty-guiding training strategies to solve these challenges. |
| Outcome: | The proposed method achieves better OOV/OOD detection performance and generalization ability on OOV entities compared to state-of-the-art methods. |
Embodied Executable Policy Learning with Language-based Scene Summarization (2024.naacl-long)
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| Challenge: | Existing Large Language models with text inputs lack the capability to evolve with non-expert interactions with environments. |
| Approach: | They propose a novel learning paradigm that generates robots’ executable actions in the form of text, derived solely from visual observations. |
| Outcome: | The proposed learning paradigm surpasses baselines and can adapt to the target tasks effectively. |
Multinomial Adversarial Networks for Multi-Domain Text Classification (N18-1)
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| Challenge: | Existing methods for text classification are domain-dependent, but there is no annotated data for some domains. |
| Approach: | They propose a multinomial adversarial network to tackle multi-domain text classification . they show that MANs significantly outperform prior art on the MDTC task . |
| Outcome: | The proposed model outperforms the prior art on the multi-domain text classification task. |
D2S: Document-to-Slide Generation Via Query-Based Text Summarization (2021.naacl-main)
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| Challenge: | Existing research efforts to automate the document-to-slide generation process face a critical challenge: no publicly available dataset for training and benchmarking. |
| Approach: | They propose a dataset SciDuet that gathers papers and their corresponding slides from recent years’ NLP and ML conferences. |
| Outcome: | The proposed system outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation. |
IPED: An Implicit Perspective for Relational Triple Extraction based on Diffusion Model (2024.naacl-long)
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| Challenge: | Existing approaches to extract relational triples have inherent shortcomings such as redundant information and incomplete triple recognition. |
| Approach: | They propose an Implicit Perspective for relational triple Extraction based on Diffusion model that uses block coverage to complete tables. |
| Outcome: | The proposed method achieves state-of-the-art performance while gaining low computational complexity. |
Seeded Hierarchical Clustering for Expert-Crafted Taxonomies (2022.findings-emnlp)
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| Challenge: | Practitioners from many disciplines use expert-crafted taxonomies to make sense of large, unlabeled corpora. |
| Approach: | They propose a weakly supervised algorithm for seeded hierarchical clustering that fits unlabeled data to taxonomies using a small set of labeled examples. |
| Outcome: | The proposed algorithm outperforms baselines on three real-world datasets. |
EmoGist: Efficient In-Context Learning for Visual Emotion Understanding (2025.findings-emnlp)
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| Challenge: | EmoGist is a training-free, in-context learning method for visual emotion classification . context-dependent definitions of emotion labels could allow more accurate predictions of emotions . |
| Approach: | They introduce EmoGist, a training-free, in-context learning method for performing visual emotion classification with LVLMs. |
| Outcome: | The proposed method improves micro F1 scores and macro F1 with LVLMs. |
Thesis Proposal: A Normalization-First Framework for Sound, Complete, and Utility-Ready Open Information Extraction (2026.acl-srw)
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| Challenge: | Existing approaches to extract relational tuples from text are incomplete and ambiguous . Existing methods rely on predefined schemas to produce t-uples . |
| Approach: | They propose a normalization-first framework that reframes OIE as a structured semantic transformation pipeline . they formalize soundness, completeness, and usefulness as approximate yet verifiable guarantees over extraction quality . |
| Outcome: | The proposed framework aims to make OIE usable for downstream reasoning and machine interpretability. |
ZSEE: A Dataset based on Zeolite Synthesis Event Extraction for Automated Synthesis Platform (2024.findings-naacl)
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| Challenge: | Automated synthesis of zeolite holds great significance for attaining economic and environmental benefits. |
| Approach: | They propose an event extraction task to mine structural synthesis actions from experimental narratives for modular automated synthesis. |
| Outcome: | The proposed method can significantly expedite automated synthesis of zeolites owing to its machine readability. |
Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction (2022.findings-naacl)
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Xiang Chen, Ningyu Zhang, Lei Li, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen
| Challenge: | Existing approaches for named entity recognition and relation extraction suffer from error sensitivity when irrelevant object images are incorporated in texts. |
| Approach: | They propose a hierarchical visual prefix fusion NeTwork for visual-enhanced entity and relation extraction using pluggable visual prefixed visual features. |
| Outcome: | The proposed method achieves state-of-the-art on three benchmark datasets. |
Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines (D19-1)
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| Challenge: | Using dialog agents, we can collect family history data from in-person consultations and crowdsource it to a genetic counselor. |
| Approach: | They propose to use natural language interactions annotated with medical family histories to collect information from a genetic counselor and crowdsourcing. |
| Outcome: | The proposed system averages 0.87 on complex sentences on the targeted relations. |
PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages (D18-1)
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| Challenge: | Using a hierarchical model, we aim to extract all the publication strings from a researcher's homepage. |
| Approach: | They propose an end-to-end hierarchical model named PubSE based on Bi-LSTM-CRF and an alternating training method for training the model. |
| Outcome: | The proposed model outperforms the state-of-the-art models by 11.8% in F1-score on real data. |
Improving Model Generalization: A Chinese Named Entity Recognition Case Study (2021.acl-short)
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| Challenge: | Named Entity Recognition (NER) is a fundamental building block for various downstream natural language processing tasks due to the ambiguous word boundaries and complex composition. |
| Approach: | They propose to resample entities within the same category to encourage a model to leverage both name and context knowledge in the training process. |
| Outcome: | The proposed method significantly improves a model’s ability to detect unseen entities, especially for company, organization and position categories. |
Incorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network (2021.naacl-main)
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| Challenge: | Existing neural coreference resolution models lack syntactic and semantic information . however, such information has been shown to benefit other tasks. |
| Approach: | They propose a graph-based model that incorporates syntactic and semantic structures of sentences. |
| Outcome: | The proposed model incorporates syntactic and semantic structures of sentences. |
Knowledge Augmentation Enhances Token Classification for Recipe Understanding (2026.eacl-long)
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| Challenge: | Using entity type-specific and knowledge-augmented token classification, we achieve state-of-the-art (SOTA) results on 5 out of 7 benchmark recipe datasets, significantly outperforming traditional token classification methods. |
| Approach: | They propose an entity type-specific and knowledge-augmented token classification framework to improve encoder models’ performance on recipe texts. |
| Outcome: | The proposed model outperforms traditional token classification methods on 5 out of 7 recipe datasets and is the largest annotated food-related dataset to date. |
Double Graph Based Reasoning for Document-level Relation Extraction (2020.emnlp-main)
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| Challenge: | Existing methods for document-level relation extraction fail to recognize relations between entities across sentences. |
| Approach: | They propose a method to recognize relations for long paragraphs by a Graph Aggregation-and-Inference Network (GAIN) they propose to use a heterogeneous mention-level graph and an entity-level EG graph to analyze the relationships. |
| Outcome: | The proposed method achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art. |
GraphCache: Message Passing as Caching for Sentence-Level Relation Extraction (2022.findings-naacl)
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| Challenge: | Existing work only encodes entity types and textual context within individual instances, which limits the performance of sentence-level relation extraction (RE). |
| Approach: | They propose a module that aggregates the features from sentences to learn global representations of properties and augments local features within individual sentences. |
| Outcome: | The proposed module can learn global representations of properties from sentences and augment local features within individual sentences. |
MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)
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Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou
| Challenge: | Existing datasets exhibit data scarcity and limited coverage of general-domain events. |
| Approach: | They present a MAssive eVENt detection dataset which contains 4,480 Wikipedia documents and 168 event types. |
| Outcome: | The proposed dataset shows that existing methods cannot achieve promising results on the small datasets. |
Large language models are few-shot clinical information extractors (2022.emnlp-main)
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| Challenge: | a long-running goal of clinical NLP is the extraction of important variables trapped in clinical notes. |
| Approach: | They propose to use large language models to tackle diverse clinical extraction tasks . they propose to reannote existing CASI datasets to compare their models with clinical text. |
| Outcome: | The proposed models outperform existing models on few-shot clinical information extraction tasks. |
In Layman’s Terms: Semi-Open Relation Extraction from Scientific Texts (2020.acl-main)
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| Challenge: | Information Extraction (IE) systems extract only a fraction of the information captured, and Open IE systems do not perform well on the long and complex sentences encountered in scientific texts. |
| Approach: | They propose to use Focused Open Biological Information Extraction (FOBIE) to train a narrow scientific IE system to extract trade-off relations and arguments that are central to biology texts. |
| Outcome: | The proposed system extracts trade-off relations and arguments that are central to biology texts. |
Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction? (2023.acl-long)
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| Challenge: | Existing approaches to biomedical relation extraction (RE) are limited due to the scarcity of annotations and the prevalence of instances without explicitly pre-defined labels. |
| Approach: | They propose a method which converts biomedical relation extraction (RE) as natural language inference formulation through indirect supervision. |
| Outcome: | Extensive experiments on three widely-used biomedical RE benchmarks show that indirect supervision improves biomedically relation extraction even when a domain gap exists. |
Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER (D19-1)
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| Challenge: | Contextual word embeddings have demonstrated state-of-the-art performance on various NLP tasks. |
| Approach: | They propose to use adversarial learning to improve upon multilingual BERT's zero-resource cross-lingual performance by aligning embeddings of English documents and their translations. |
| Outcome: | The multilingual version of BERT performs surprisingly well in cross-lingual settings, even when only labeled English data is used to finetune the model. |
TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task (2020.acl-main)
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| Challenge: | Existing methods for Relation Extraction (RE) still show a high error rate . label errors account for 8% absolute F1 test error, and more than 50% of examples need to be relabeled. |
| Approach: | They validate the most challenging 5K examples using trained annotators and analyze misclassifications on the challenging instances. |
| Outcome: | The proposed methods perform well on the most challenging datasets and improve on the relabeled test set. |
Diversity-Aware Coherence Loss for Improving Neural Topic Models (2023.acl-short)
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| Challenge: | Experimental results show that our method significantly improves the performance of neural topic models without requiring any pretraining or additional parameters. |
| Approach: | They propose a variational autoencoder framework that minimizes the posterior and prior divergence and a diversity-aware coherence loss that encourages the model to learn corpus-level coherency scores while maintaining high diversity between topics. |
| Outcome: | The proposed approach significantly improves the performance of neural topic models without pretraining or additional parameters. |
Span-based Named Entity Recognition by Generating and Compressing Information (2023.eacl-main)
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| Challenge: | Existing work on Named Entity Recognition (NER) only used generative or information compression models to improve performance. |
| Approach: | They propose to combine two types of IB models into one system to enhance Named Entity Recognition (NER) they incorporate unsupervised generative components span reconstruction and synonym generation into a span-based NER system. |
| Outcome: | The proposed model focuses on learning span representation, which is applicable not only to span-based NER but also to other span-related tasks such as event coreference resolution and question answering. |
Unsupervised Relation Extraction: A Variational Autoencoder Approach (2021.emnlp-main)
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| Challenge: | Existing methods for relation extraction use latent variables and supervised training which requires large datasets. |
| Approach: | They propose a VAE-based unsupervised relation extraction technique that uses latent variables as an intermediate variable instead of a latent variable. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the NYT dataset and outperformed existing methods. |
Sentence-Level Resampling for Named Entity Recognition (2022.naacl-main)
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| Challenge: | named entity recognition (NER) tasks are often dominated by the majority of non-entity tokens in text . a data imbalance problem is causing the NER models to ignore named entities . |
| Approach: | They propose a set of sentence-level resampling methods to reduce data imbalance . they use a training sentence to compute the importance of each training sentence based on its tokens and entities . |
| Outcome: | The proposed methods outperform sub-sentence-level resampling, data augmentation, and loss functions on multiple corpora. |
An Investigation of Evaluation Methods in Automatic Medical Note Generation (2023.findings-acl)
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| Challenge: | Recent studies show that doctors can save significant amounts of time when using automatic note generation. |
| Approach: | They propose task-specific metrics for automatic note generation from medical conversation summarization and generation, including knowledge-graph embedding-based metrics, customized model-based measures with domain-specific weights, and ensemble metrics. |
| Outcome: | The proposed evaluation metrics are compared to existing models and can have different behaviors on different types of clinical notes datasets. |
Hypergraph based Understanding for Document Semantic Entity Recognition (2024.acl-long)
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| Challenge: | Existing document understanding models focus on entity categories while ignoring the extraction of entity boundaries. |
| Approach: | They propose a hypergraph attention document semantic entity recognition framework which uses hypergraph focus to focus on entity boundaries and entity categories at the same time. |
| Outcome: | The proposed framework can improve the performance of existing models on FUNSD, CORD, XFUND and SROIE. |
Training Dynamics for Text Summarization Models (2022.findings-acl)
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| Challenge: | Pre-trained language models have shown impressive results when fine-tuned on large summarization datasets. |
| Approach: | They analyze the training dynamics for generation models, focusing on summarization . they find that a propensity to copy the input is learned early in the training process . |
| Outcome: | The proposed model learns at different stages of fine-tuning, the authors show . they show that factual errors are learnt in later stages, but not at high-loss tokens . |
Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models (2024.acl-long)
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| Challenge: | Existing approaches to cross-document event coreference resolution are prone to learning simple co-occurrences due to the complexity of contexts. |
| Approach: | They propose a collaborative approach to cross-document event coreference resolution that leverages both a universally capable LLM and a task-specific SLM. |
| Outcome: | The proposed approach surpasses the performance of both large and small language models individually, underscoring its effectiveness in diverse scenarios. |
Genre Identification and the Compositional Effect of Genre in Literature (C18-1)
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| Challenge: | Literature is artistic and conveys complex themes over the course of very long narratives. |
| Approach: | They propose a method which can work with large literary corpus of texts . they propose 'gutenberg' dataset to perform Genre Identification . |
| Outcome: | The proposed methods improve results in a literature-based task with 200,000 words of literature . the Gutenberg dataset is used to model literary classifications with a high level of fidelity . |
Text Annotation Graphs: Annotating Complex Natural Language Phenomena (L18-1)
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| Challenge: | Text Annotation Graphs is a web-based tool for annotating text . it provides functionality for representing complex relationships between words and word phrases . |
| Approach: | They introduce a web-based tool for annotating text, Text Annotation Graphs, or TAG . it provides functionality for representing complex relationships between words and word phrases . |
| Outcome: | The proposed software can represent complex relationships between words and words . it can also be used to find similar structures within the current document or external annotated documents. |
GLEN: General-Purpose Event Detection for Thousands of Types (2023.emnlp-main)
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| Challenge: | ACE 2005 2 is the first large-scale event extraction dataset with 205K event mentions and 3,465 different types. |
| Approach: | They propose to use the DWD Overlay to map PropBank rolesets to a large distantlysupervised training dataset with partial labels to make event extraction more accessible. |
| Outcome: | The proposed model performs better than baselines including InstructGPT and ACE 2005 2 despite being 18 years old . key limitations of ACE include its small event ontology of 33 types, small dataset size of around 600 documents and restricted domain (with a significant portion concentrated on military conflicts). |
Combining Counting Processes and Classification Improves a Stopping Rule for Technology Assisted Review (2023.findings-emnlp)
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| Challenge: | Experiments on multiple data sets show that the proposed approach consistently improves performance and outperforms several alternative methods. |
| Approach: | They propose to integrate a text classifier into an existing TAR stopping rule to train it without the need for additional annotations. |
| Outcome: | Experiments on multiple data sets show the proposed approach outperforms other methods and achieves the desired level of recall with a lower cost than the existing method based on counting processes alone. |
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. |
On Faithfulness and Factuality in Abstractive Summarization (2020.acl-main)
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| Challenge: | Existing conditional text generation models produce unfaithful and unfaithed summaries . current models accomplish a high level of fluency and coherence . |
| Approach: | They propose to use pretrained models for document summarization to better understand hallucinations . they find that textual entailment measures better correlate with faithfulness . |
| Outcome: | The proposed models generate faithful and factual summaries as evaluated by humans. |
FloDusTA: Saudi Tweets Dataset for Flood, Dust Storm, and Traffic Accident Events (2020.lrec-1)
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| Challenge: | Detecting events from tweets can help to predict real-world events precisely. |
| Approach: | They propose to use tweets written in Arabic and Saudi dialects to identify events from tweets. |
| Outcome: | The proposed system can detect flood, dust storm, traffic accident, and non-event. |
Glocal: Incorporating Global Information in Local Convolution for Keyphrase Extraction (N19-1)
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| Challenge: | Graph Convolutional Networks (GCNs) model nodes’ local pairwise importance but lack the capability to model global relative importance in tasks where global ranking is a key component for the task. |
| Approach: | They propose to incorporate global relative importance information into the GCN family of models by using scaled node weights. |
| Outcome: | The proposed method improves keyphrase extraction by 2% and improves the baseline by 5%. |
Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning (C18-1)
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| Challenge: | Existing approaches to named entity recognition (NER) in Chinese are limited by the lack of annotated data. |
| Approach: | They propose a method which can automatically populate annotated training data without humancost by using distant supervision. |
| Outcome: | The proposed method performs better than comparison systems on two datasets. |
Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading (2021.findings-acl)
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| Challenge: | Existing models that use textual features and sentiments to make stock predictions are poor explainability and low signal-to-noise ratio. |
| Approach: | They propose a bi-level event detection model that detects corporate events from news articles and an elaborately-annotated dataset EDT for corporate event detection and news-based stock prediction benchmark. |
| Outcome: | The proposed strategy outperforms baselines in winning rate, excess returns over the market, and the average return on each transaction. |
QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization (2022.naacl-main)
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| Challenge: | Existing studies on text summarization factual consistency are divided into two categories . entailment-based and question answering-based metrics are the most efficient . |
| Approach: | They propose an optimized QA-based metric that improves factual consistency by 14% . they compare entailment-based and QA metrics to find the best fit . |
| Outcome: | The proposed metric outperforms the best performing entailment-based metric on the SummaC factual consistency benchmark. |
Recurrent Attention Networks for Long-text Modeling (2023.findings-acl)
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| Challenge: | Existing approaches to encoding long documents using self-attention have been limited by quadratic computational complexities and limited application in long text processing. |
| Approach: | They propose a long-document encoding model that allows the recurrent operation of self-attention. |
| Outcome: | The proposed model extracts global semantics in token-level and document-level representations, making it inherently compatible with both sequential and sequential tasks. |
Extracting Chemical-Protein Interactions via Calibrated Deep Neural Network and Self-training (2020.findings-emnlp)
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| Challenge: | Several natural language processing methods have been used to extract interactions between chemicals and proteins from biomedical text data. |
| Approach: | They propose a method to extract chemical–protein interactions from biomedical text data . they use a pre-trained language-understanding model and calibration techniques to estimate uncertainty . |
| Outcome: | The proposed approach achieves state-of-the-art performance on the Biocreative VI ChemProt task while preserving higher calibration abilities. |
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER (2022.acl-long)
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Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren
| Challenge: | Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. |
| Approach: | They propose a demonstration-based learning method which lets the input be prefaced by task demonstrations for in-context learning. |
| Outcome: | The proposed method improves on in-domain learning and domain adaptation in low-resource settings. |
Open Information Extraction from Conjunctive Sentences (C18-1)
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| Challenge: | Recent work has highlighted the lack of proper conjunction processing as the most significant source of missed yield in Open IE. |
| Approach: | They develop a coordination analyzer that searches over hierarchical conjunct boundaries and uses a language model to score conjunctions. |
| Outcome: | The proposed system performs extraction over the simple sentences identified by CALM to obtain up to 1.8x yield with a moderate increase in precision compared to extractions from original sentences. |
WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia (2021.naacl-main)
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| Challenge: | Existing datasets for cross-document event coreference resolution are limited and small . authors present a method for identifying clusters of text mentions that refer to the same event . |
| Approach: | They propose a method for generating a large-scale Wikipedia event coreference dataset . they use a generic approach that adapts state-of-the-art models to the cross-document setting . |
| Outcome: | The proposed method outperforms existing models and can be applied to other languages. |
Arabic Data Science Toolkit: An API for Arabic Language Feature Extraction (L18-1)
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| Challenge: | Data scientists unfamiliar with Arabic or natural language processing prefer statistical methods because they are language-independent. |
| Approach: | They propose a framework for Arabic feature extraction that leverages Arabic-specific linguistic and stylistic features to enhance their systems. |
| Outcome: | The Arabic Data Science Toolkit (ADST) is a framework for Arabic language feature extraction. |
Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition (2022.naacl-main)
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| Challenge: | Named entity recognition (NER) models trained on CoNLL do not transfer well to other domains, even within the same language. |
| Approach: | They propose a token-level gating layer to augment pre-trained multilingual transformers with gazetteers containing named entities (NE) from a target language or domain. |
| Outcome: | The proposed model improves on cross-lingual transfer with an F1 score of 92.92 for English and an average of 89.43 across all languages in CoNLL. |
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (D18-1)
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| Challenge: | Existing approaches to summarize documents are not extractive and require an abstractive approach. |
| Approach: | They propose a novel abstractive model which is conditioned on the article’s topics and based entirely on convolutional neural networks. |
| Outcome: | The proposed model outperforms an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans. |
Treasures Outside Contexts: Improving Event Detection via Global Statistics (2021.emnlp-main)
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| Challenge: | Existing neural-based ED models are confused by changeable contexts during testing . we propose a system that extracts statistical event features from word-event cooccurrence frequencies . |
| Approach: | They propose to integrate a set of statistical event features from word-event co-occurrence frequencies into the training set to cooperate with contextual features. |
| Outcome: | The proposed model outperforms ten strong baselines on ACE2005 and KBP2015 datasets. |
DocOIE: A Document-level Context-Aware Dataset for OpenIE (2021.findings-acl)
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| Challenge: | Existing solutions focus on extracting tuples at sentence level, but sentences exist as part of a document rather than standalone. |
| Approach: | They propose to annotate 800 sentences from 80 documents to form a DocOIE dataset . they propose to use document-level context to improve OpenIE performance . |
| Outcome: | The proposed OpenIE model improves performance by incorporating documentlevel context into the dataset. |
COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from sentences, but when faced with multiple aspect terms, the MRC-based methods could fail due to the interference from other aspect terms. |
| Approach: | They propose a COntext-Masked MRC framework for Aspect Sentiment Triplet Extraction (ASTE) which aims to extract sentiment triplets from sentences . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on benchmark datasets and shows that it can extract sentiment triplets from multiple aspect terms. |
AD3: Attentive Deep Document Dater (D18-1)
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| Challenge: | Existing methods to predict creation time of documents are based on time-stamp metadata, but none are available. |
| Approach: | They propose an attention-based neural document dating system which utilizes both context and temporal information in documents in a flexible and principled manner. |
| Outcome: | The proposed system outperforms neural and non-neural baselines on multiple real-world datasets. |
Self-Supervised Learning for Contextualized Extractive Summarization (P19-1)
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| Challenge: | Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss . previous work builds an end-to-end system to learn to choose sentences without explicitly modeling document context . |
| Approach: | They propose three auxiliary pre-training tasks that learn to capture the document context in a self-supervised fashion. |
| Outcome: | The proposed models outperform existing models on a CNN/DM dataset. |
BKEE: Pioneering Event Extraction in the Vietnamese Language (2024.lrec-main)
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| Challenge: | Event Extraction (EE) is a fundamental task in information extraction. |
| Approach: | They propose a Vietnamese event extraction dataset that includes 33 different event types and 28 different event argument roles. |
| Outcome: | The proposed dataset provides a labeled dataset for entity mentions, event mentions and event arguments on 1066 documents. |
Relation Extraction with Type-aware Map Memories of Word Dependencies (2021.findings-acl)
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| Challenge: | Existing studies focus on the dependency connections between words with limited attention paid to exploiting dependency types. |
| Approach: | They propose a neural approach for relation extraction with type-aware map memories . they map all associated words along with dependencies among them to memory slots . |
| Outcome: | The proposed approach achieves state-of-the-art on two English benchmark datasets. |
Adapting Coreference Resolution to Twitter Conversations (2020.findings-emnlp)
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| Challenge: | Existing studies on coreference resolution for Twitter texts show that performance is low. |
| Approach: | They propose to use Twitter conversations to train a system that is originally trained on OntoNotes to improve coreference resolution. |
| Outcome: | The proposed system outperforms existing systems on Twitter by 21.6%. |
Weakly-supervised Text Classification Based on Keyword Graph (2021.emnlp-main)
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| Challenge: | Existing methods for text classification ignore keyword correlation, thus ignoring it . existing methods treat keywords independently, thus not exploiting correlation between them . |
| Approach: | They propose a framework to explore keyword-keyword correlation on keyword graph by GNN . they use a self-supervised task to pretrain annotators and fine-tune them . |
| Outcome: | The proposed method outperforms existing methods on long- and short-text datasets. |
Document-Level Event Argument Extraction by Leveraging Redundant Information and Closed Boundary Loss (2022.naacl-main)
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| Challenge: | Document-level event argument extraction is a crucial subtask of event extraction. |
| Approach: | They propose to use redundant event information to extract multiple arguments from a document . they propose a loss function to classify Universum class by their open decision boundary . |
| Outcome: | The proposed model outperforms the previous state-of-the-art models by 3.35% in F1-score. |
Regular Expression Guided Entity Mention Mining from Noisy Web Data (D18-1)
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| Challenge: | Named Entity Recognition (NER) is a subtask of the broader problem of Information Extraction (IE) from text. |
| Approach: | They propose a framework that uses Regular Expressions to identify entities from web data . they combine expressive power of REs with ability of deep learning to learn from large data a human expert is asked to label a small set of documents . |
| Outcome: | The proposed framework achieves impressive accuracy while requiring modest human effort. |
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. |
MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)
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Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, Juanzi Li
| Challenge: | Existing datasets for event understanding have limited coverage due to complexity of tasks. |
| Approach: | They propose a dataset that augments MAVEN datasets with event argument annotations . they propose 98,591 events and 290,613 arguments obtained with laborious human annotation . |
| Outcome: | The proposed dataset is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction. |
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing methods for few-shot Named Entity Recognition ignore entity boundaries and are time-consuming . a seminal span-based prototypical network solves the problem using two stages: span extraction and mention classification. |
| Approach: | They propose a seminal span-based prototypical network that tackles few-shot NER . they transform sequential tags into a global boundary matrix and use prototypical learning . |
| Outcome: | The proposed model outperforms strong baselines over multiple benchmarks. |
Cross-media Structured Common Space for Multimedia Event Extraction (2020.acl-main)
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| Challenge: | We propose a new task to extract events and their arguments from multimedia documents . traditional methods target text, images or videos, but multimedia content is distributed via multimedia . |
| Approach: | They propose a method that encodes structured representations of semantic information from textual and visual data into a common embedding space. |
| Outcome: | The proposed method achieves 4.0% and 9.8% absolute gains on text event argument role labeling and visual event extraction. |
Text is All You Need: LLM-enhanced Incremental Social Event Detection (2025.acl-long)
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| Challenge: | Existing state-of-the-art (SOTA) SED models rely on graph neural networks (GNNs) Existing SED frameworks rely heavily on GNNs, which require complex graph construction and time-consuming training processes. |
| Approach: | They propose a framework that leverages the rich background knowledge of large language models to formalize and disambiguate short texts by completing abbreviations and summarizing informal expressions. |
| Outcome: | The proposed framework outperforms existing models on two challenging real-world datasets. |
Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations (2020.lrec-1)
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| Challenge: | a study of the performance of NLP in relation extraction focuses on a business sector . a morphological dictionary can be used to extract named-entity pairs . |
| Approach: | They propose to use annotated textual corpora to perform Brand-Product relation extraction . they propose to propose query expansion by morpho-syntactically related words . |
| Outcome: | The proposed method improves the performance of the Brand-Product relation extraction task. |
Text-to-Text Extraction and Verbalization of Biomedical Event Graphs (2022.coling-1)
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| Challenge: | Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature. |
| Approach: | They propose a framework to solve event extraction and event verbalization with a unified text-to-text approach. |
| Outcome: | The proposed framework achieves greater state-of-the-art performance than single-task competitors and can generate coherent natural language utterances from structured data. |
HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications (2021.findings-acl)
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Qiao Cheng, Juntao Liu, Xiaoye Qu, Jin Zhao, Jiaqing Liang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Yanghua Xiao
| Challenge: | Relation extraction (RE) is an essential topic in natural language processing and has attracted extensive attention. |
| Approach: | They propose a case-oriented construction framework to build a hard case relation extraction dataset with 65,225 relational facts annotated from 9,231 documents. |
| Outcome: | The proposed model achieves a high 96% F1 score on data quality and is far lower than humans. |
Recognition of They/Them as Singular Personal Pronouns in Coreference Resolution (2022.naacl-main)
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| Challenge: | a new benchmark evaluates coreference resolution systems' ability to recognize singular personal "they" we find that current systems overwhelmingly choose to resolve "they's" correctly to a singular entity or to 'a group' |
| Approach: | They propose to evaluate coreference resolution systems for singular personal "they" they use WinoNB schemas to evaluate whether they can correctly resolve singular "they". |
| Outcome: | The proposed benchmark evaluates coreference resolution systems for singular personal "they" they show that they are biased toward resolving "they", not "them" |
MOKA: Moral Knowledge Augmentation for Moral Event Extraction (2024.naacl-long)
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| Challenge: | Existing methods for discerning moral values are limited due to lack of context, lack of moral reasoning capabilities and complexity of moral stances. |
| Approach: | They propose a framework for moral event extraction using moral words and moral scenarios. |
| Outcome: | The proposed framework outperforms baselines across three moral event understanding tasks. |
DORE: Document Ordered Relation Extraction based on Generative Framework (2022.findings-emnlp)
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| Challenge: | Existing generative methods do not fit document-level relation extraction tasks where there are multiple entities and relational facts. |
| Approach: | They propose to generate a symbolic and ordered sequence from relation matrix which is easier to learn and introduce several negative sampling strategies to improve the performance with balanced signals. |
| Outcome: | The proposed method can improve the performance of the generative DocRE models on four datasets. |
Towards Summarizing Healthcare Questions in Low-Resource Setting (2022.coling-1)
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| Challenge: | Existing methods to generate large-scale datasets are difficult in closed domains where human annotation requires domain expertise. |
| Approach: | They propose a method to generate diverse and semantic questions in a low-resource setting with the aim of summarizing healthcare questions. |
| Outcome: | The proposed method generates diverse, fluent, and informative summarized questions on healthcare question summarization datasets. |
Graph-tree Fusion Model with Bidirectional Information Propagation for Long Document Classification (2024.findings-emnlp)
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| Challenge: | Existing methods for document classification struggle with token limits and fail to adequately model hierarchical relationships within documents. |
| Approach: | They propose a novel model leveraging a graph-tree structure to capture local and global dependencies. |
| Outcome: | The proposed model captures syntactic relationships and broader document contexts without token limits and can handle arbitrarily long contexts. |
An Ordinal Latent Variable Model of Conflict Intensity (2023.acl-long)
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| Challenge: | Advances in automated event extraction yield massive data sets of “who did what to whom” micro-records that enable data-driven approaches to monitoring conflict. |
| Approach: | They propose a probabilistic generative model that assumes each observed event is associated with a latent intensity class. |
| Outcome: | The proposed model obtains comparatively good held-out predictive performance on a conflictual to cooperative scale. |
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues (2021.naacl-main)
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Bo-Hsiang Tseng, Shruti Bhargava, Jiarui Lu, Joel Ruben Antony Moniz, Dhivya Piraviperumal, Lin Li, Hong Yu
| Challenge: | Traditionally, anaphora resolution and ellipses resolution are limited in dialogues . despite rapid progress in dialogue systems, several difficulties remain . |
| Approach: | They propose a joint learning framework for modeling coreference resolution and query rewriting for complex, multi-turn dialogues. |
| Outcome: | The proposed model outperforms the state-of-the-art model on a rewritten dialogue dataset. |
Exploiting Global and Local Hierarchies for Hierarchical Text Classification (2022.emnlp-main)
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| Challenge: | Existing methods encode label hierarchy in a global view, which makes them hard to exploit hierarchical information. |
| Approach: | They propose to leverage label hierarchy in multi-label text classification by encoding label hierarchy as a static hierarchical structure containing all labels. |
| Outcome: | The proposed method achieves significant improvement on three benchmark datasets compared with the state-of-the-art method HGCLR. |
Query-focused Scenario Construction (D19-1)
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| Challenge: | Stronger neural network models and harder synthetic training settings are important to achieve high performance. |
| Approach: | They propose a query-based system that extracts compatible sets of events from news data . stronger neural network models and harder synthetic training settings are important to achieve high performance . |
| Outcome: | The proposed system outperforms baselines on a human-curated dataset of scenarios about real-world news topics. |
Generation-Augmented and Embedding Fusion in Document-Level Event Argument Extraction (2025.coling-main)
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| Challenge: | Document-level event argument extraction is a crucial task that aims to extract arguments from the entire document, beyond sentence-level analysis. |
| Approach: | They propose a novel approach to document-level event argument extraction that integrates predefined templates and generative language models into a foundational embedding derived from a classification model. |
| Outcome: | The proposed approach is more effective than baseline models and data-efficient in low-resource scenarios. |
Targeted Augmentation for Low-Resource Event Extraction (2024.findings-naacl)
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| Challenge: | Existing methods for low-resource information extraction struggle to strike a balance between weak augmentation and drastic augmentation. |
| Approach: | They propose a data augmentation paradigm that uses back validation and targeted augmentation to produce augmented examples with enhanced diversity, polarity, accuracy, and coherence. |
| Outcome: | The proposed paradigm produces augmented examples with enhanced diversity, polarity, accuracy, and coherence. |
What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text (2022.findings-acl)
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| Challenge: | Current research on anaphora resolution is mostly based on declarative text, such as chemical patents or instruction manuals. |
| Approach: | They propose a framework for anaphora annotation for the chemical domain for modeling anamorphic phenomena in recipes and chemical patents. |
| Outcome: | The proposed framework improves resolution of anaphora in recipes, suggesting transferability of general procedural knowledge. |
Open Domain Event Extraction Using Neural Latent Variable Models (P19-1)
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| Challenge: | Existing work on extracting events from news documents focuses on a set of pre-specified event types. |
| Approach: | They propose a latent variable neural model which is scalable to large corpus. |
| Outcome: | The proposed model performs better than the state-of-the-art method for event schema induction. |
Task-oriented Domain-specific Meta-Embedding for Text Classification (2020.emnlp-main)
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| Challenge: | Existing methods neglect domain-specific knowledge and use the same word embedding for each word in all domain-specified datasets. |
| Approach: | They propose a method to incorporate domain-specific and task-oriented information into meta-embeddings by combining pre-trained word embeddings. |
| Outcome: | The proposed method performs well on four text classification datasets and shows that it is compatible with existing methods. |
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition (2022.findings-acl)
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| Challenge: | Dialog sentiment classification (DSC) and dialog act recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog. |
| Approach: | They propose a framework which integrates prediction-level interactions other than semantics-level ones into dialog understanding and dual-task reasoning by integrating temporal relations into the model. |
| Outcome: | The proposed model outperforms existing models by large margins while costing less training time and requiring less computation resource. |
Word-Level Loss Extensions for Neural Temporal Relation Classification (C18-1)
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| Challenge: | Unsupervised pre-trained word embeddings are used for many tasks in natural language processing to leverage unlabeled textual data. |
| Approach: | They extend the model's task loss with an unsupervised auxiliary loss on the word-embedding level of the model to ensure that the learned word representations contain both task-specific features and more general features. |
| Outcome: | The proposed model improves on the task of extracting narrative containment relations from clinical records using a general-domain part-of-speech tagger as linguistic resource. |
Fusion meets Function: The Adaptive Selection-Generation Approach in Event Argument Extraction (2025.coling-main)
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| Challenge: | Event Argument Extraction is a critical subtask of Event Extraction, focused on identifying event arguments within text. |
| Approach: | They propose a Fusion Selection-Generation-Based Approach that merges selective and generative methods to enhance argument extraction accuracy. |
| Outcome: | The proposed method improves on the RAMS and WikiEvents, while preserving the unique characteristics of both methods. |
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. |
Pre-training for Abstractive Document Summarization by Reinstating Source Text (2020.emnlp-main)
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| Challenge: | Abstractive document summarization models are often trained on limited supervised data . authors present three objectives for pretraining abstractive summarizing models . |
| Approach: | They propose to pre-train a SEQ2SEQ based abstractive summarization model on unlabeled text. |
| Outcome: | The proposed method improves on two benchmark summarization datasets with 19GB of text . the goal is sentence reordering, next sentence generation and masked document generation . |
SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction (2020.emnlp-main)
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| Challenge: | Existing methods for relation extraction use heuristics or distant-supervised annotations, but distant supervised methods make strong assumptions on entity cooccurrence without sufficient contexts. |
| Approach: | They propose a framework that exploits weak, self-supervised signals by leveraging large pretrained language models for adaptive clustering on contextualized relational features. |
| Outcome: | The proposed framework exploits weak, self-supervised signals on open-domain Relation Extraction . it bootstraps the self-supervised signals by improving contextualized features in relation classification . |
Effective Data Augmentation for Sentence Classification Using One VAE per Class (2022.coling-1)
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| Challenge: | Variational auto-encoders and its conditional variant the Conditional-VAE (CVAE) are often used to generate new textual data, but they require more complex manipulations to ensure that the generated examples are useful. |
| Approach: | They propose a simple way to use Variational Auto-Encoders (VAE) for data augmentation by training one VAE per class. |
| Outcome: | The proposed method outperforms generative models on binary classification tasks and several dataset sizes on four different tasks. |
Learning Geometry-Aware Representations for New Intent Discovery (2024.acl-long)
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| Challenge: | Existing methods for intent classification fail to distinguish new intents due to intertwined centers . a novel framework that learns geometry-aware representations to maximally separate all intents is proposed . |
| Approach: | They propose a new intent discovery framework that learns geometry-aware representations to maximally separate all intents. |
| Outcome: | The proposed framework achieves a new state-of-the-art performance on three benchmarking datasets. |
Deep Exhaustive Model for Nested Named Entity Recognition (D18-1)
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| Challenge: | Named entity recognition (NER) is a task of finding entities with specific semantic types such as Protein, Cell, and RNA in text. |
| Approach: | They propose a deep neural model for nested named entity recognition . they enumerate all possible regions or spans as potential entity mentions . |
| Outcome: | The proposed model outperforms state-of-the-art models on nested and flat NER . it achieves 77.1% and 78.4% respectively in terms of F-score, without external knowledge resources. |
CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction (2024.lrec-main)
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| Challenge: | Existing IE tools lack multi-task support and automatic updates for KG and EKG construction. |
| Approach: | They propose a human-machine-cooperative IE toolkit for KG and EKG construction that unifies different IE subtasks and integrates LLMs as the assistant machine. |
| Outcome: | The proposed tool improves annotation quality, efficiency, and stability simultaneously. |
Generating Uncontextualized and Contextualized Questions for Document-Level Event Argument Extraction (2024.naacl-long)
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| Challenge: | Existing methods for document-level argument extraction do not require human involvement and combine uncontextualized and contextualized questions. |
| Approach: | They propose multiple question generation strategies for document-level event argument extraction that do not require human involvement and combine uncontextualized and contextualized questions. |
| Outcome: | The proposed questions do not require human involvement and are suitable for document-level argument extraction. |
Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy (2025.coling-main)
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| Challenge: | Existing methods for text classification based on large language models are difficult to apply directly to solve. |
| Approach: | They propose a data quality enhancement method to improve LLMs' performance in classification tasks by using a greedy algorithm to select data and then performing fine-tuning. |
| Outcome: | The proposed method improves the performance of large language models in text classification tasks and significantly improves training efficiency, saving nearly half of the training time. |
Mitigating Uncertainty in Document Classification (N19-1)
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| Challenge: | Existing models for uncertainty measurement are time-consuming and unable to handle large-scale data sets. |
| Approach: | They propose a new dropout-entropy method for uncertainty measurement and a metric learning method on feature representations to boost the performance of dropout based uncertainty methods. |
| Outcome: | The proposed method improves accuracy from 0.78 to 0.92 when 30% of the most uncertain predictions were handed over to human experts in “20NewsGroup” data. |
Towards Modern Topic Models: A Survey of Taxonomies and Paradigm Shifts from Algorithm-Centric to LLM-Centered Topic Analysis (2026.findings-acl)
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| Challenge: | Topic modeling (TM) is a classic unsupervised learning task in the field of natural language processing. |
| Approach: | They propose a new taxonomy that emphasizes the role of LLMs and the design of end-to-end workflows. |
| Outcome: | The proposed taxonomy emphasizes the role of LLMs and the design of end-to-end workflows. |
Bridging Resolution: A Survey of the State of the Art (2020.coling-main)
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| Challenge: | bridging resolution is an anaphora resolution task that is less studied than entity coreference resolution. |
| Approach: | This paper presents a survey of the current state of research on bridging resolution . it identifies and resolves bridling/associative anaphors, which are anamorphic references to non-identical associated antecedents. |
| Outcome: | The proposed task is more difficult than entity coreference resolution because of the lack of annotated corpora and lack of standardized evaluation protocols. |
Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction (2022.findings-emnlp)
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| Challenge: | Existing data augmentation methods for event extraction are costly and time-consuming. |
| Approach: | They propose a data augmentation framework that randomly masks out an adjunct sentence fragment and infills a variable-length text span with a fine-tuned infilling model. |
| Outcome: | The proposed framework can generate more diverse data while keeping the original structure unchanged . it can replace a fragment of arbitrary length in the text with another fragment of variable length . |
Inductive Topic Variational Graph Auto-Encoder for Text Classification (2021.naacl-main)
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| Challenge: | Existing methods for text classification do not assume explicit latent semantic structure of documents, making them less effective and difficult to interpret. |
| Approach: | They propose a model that integrates a topic model into variational graph-auto-encoder to capture hidden semantic information between documents and words. |
| Outcome: | The proposed model outperforms existing models on supervised and semi-supervised text classification and unsupervised representation learning. |
Packed Levitated Marker for Entity and Relation Extraction (2022.acl-long)
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| Challenge: | Existing work on entity and relation extraction ignores the interrelation between spans . a novel approach to extract better span representations from pre-trained languages is needed . |
| Approach: | They propose a span representation approach that packs Levitated Markers to consider interrelation between spans. |
| Outcome: | The proposed model improves on baselines on six NER benchmarks and achieves a 4.1%-4.3% strict relation F1 improvement with higher speed over previous state-of-the-art models. |
CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition (N19-1)
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| Challenge: | Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. |
| Approach: | They propose to use a Chinese Named Entity Recognition (NER) model that uses a character-based convolutional neural network and a gated recurrent unit to capture the information from adjacent characters and sentence contexts. |
| Outcome: | The proposed model outperforms existing models on Weibo, MSRA and Chinese Resume datasets. |
Towards Effective Extraction and Evaluation of Factual Claims (2025.acl-long)
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| Challenge: | Lack of a standardized evaluation framework impedes assessment and comparison of claim extraction methods. |
| Approach: | They propose a framework for evaluating claim extraction in the context of fact-checking . they also introduce Claimify, an LLM-based claim extraction method . |
| Outcome: | The proposed evaluation framework outperforms existing methods in the evaluation of claim extraction methods. |
ECG-QALM: Entity-Controlled Synthetic Text Generation using Contextual Q&A for NER (2023.findings-acl)
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| Challenge: | Named Entity Recognition (NER) requires high-quality labeled datasets. |
| Approach: | They propose a method that uses pre-trained language models to generate entity-controlled text to augment small labeled datasets for downstream NER tasks. |
| Outcome: | The proposed method produces full text samples with desired entities appearing in a controllable way while retaining sentence coherence closest to the real world data. |
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification (2022.naacl-main)
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| Challenge: | Existing methods for data augmentation do not fully exploit the potential of DA in NLP. |
| Approach: | They propose an easy and plug-in framework for data augmentation to support effective text classification. |
| Outcome: | The proposed framework outperforms existing methods in most cases, but not using agent networks or pre-trained generation networks. |
Rethinking Document-Level Relation Extraction: A Reality Check (2023.findings-acl)
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| Challenge: | Recent efforts push up performance boundaries of document-level relation extraction (DocRE) but these efforts are not promising. |
| Approach: | They construct four types of entity mention attacks to examine model robustness . they also have a close check on model usability in a more realistic setting . |
| Outcome: | The proposed model is based on a strong or untenable assumption in common . the model is robust under four types of mention attacks and usable in a realistic setting . |
CoRelation: Boosting Automatic ICD Coding through Contextualized Code Relation Learning (2024.lrec-main)
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| Challenge: | Existing methods for boosting ICD coding performance lack a model for complex code relations . current methods overlook the importance of context in clinical notes . |
| Approach: | They propose a contextualized and flexible framework to enhance learning of ICD code relations . they use clinical notes to model all possible code relations using a dependent learning paradigm . |
| Outcome: | The proposed approach improves on six public ICD coding datasets compared to state-of-the-art models. |
CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction (2021.acl-long)
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| Challenge: | Existing methods to integrate extracted knowledge from the Web to knowledge graphs (KGs) however, the predictions are made independently, which can be mutually inconsistent. |
| Approach: | They propose a relation integration model that aligns free-text relations to relations in a target KG . they propose combining two stages to make independent predictions and a collective model that accesses all candidate predictions. |
| Outcome: | The proposed model outperforms baseline models on two datasets and improves AUC from .677 to .748 and from 1.716 to 1.780. |
On the Rigour of Scientific Writing: Criteria, Analysis, and Insights (2024.findings-emnlp)
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| Challenge: | despite its importance, little work exists on modelling rigour in scientific writing . despite widespread use of term, scientific literature lacks definition of rigor . |
| Approach: | They propose a framework to automatically identify and define rigour criteria and assess their relevance in scientific writing. |
| Outcome: | The proposed framework can be tailored to the evaluation of scientific rigour for different areas. |
Sequential Cross-Document Coreference Resolution (2021.emnlp-main)
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| Challenge: | Existing models for cross-document coreference resolution have been used for within-document entity coreference but have been relatively limited. |
| Approach: | They propose a model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference. |
| Outcome: | The proposed model achieves competitive results for entity and event coreference while minimizing error propagation in complex reasoning tasks. |
MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective (2022.acl-long)
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Xiao Wang, Shihan Dou, Limao Xiong, Yicheng Zou, Qi Zhang, Tao Gui, Liang Qiao, Zhanzhan Cheng, Xuanjing Huang
| Challenge: | Named Entity Recognition models are feature-engineering and machine learning based. |
| Approach: | They propose a new NER learning framework that uses entity mentions to improve model performance. |
| Outcome: | The proposed model achieves better performance on OOV entities on various settings and datasets. |
Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques (2021.acl-long)
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| Challenge: | Creating digital SOAP notes is burdensome and contributes to physician burnout . authors propose a pipeline to generate these notes based on transcripts of clinical conversations . |
| Approach: | They propose a pipeline to leverage deep summarization models based on conversations between physicians and patients . they propose an algorithm that extracts important utterances relevant to each section and generates one summary sentence per cluster . |
| Outcome: | The proposed algorithm outperforms its abstract counterpart by 8 ROUGE-1 points and produces more factual sentences as assessed by human evaluators. |
Tchebycheff Procedure for Multi-task Text Classification (2020.acl-main)
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| Challenge: | Existing methods for text classification assume that multitask text classification problems are convex multiobjective optimization problems. |
| Approach: | They propose a Tchebycheff procedure to optimize multi-task classification problems without convex assumption. |
| Outcome: | The proposed method is able to find an arbitrary Pareto optimal solution in the PareTO set if the problem is convex, but excludes many Paret optimal solutions from its search scope. |
From Moments to Milestones: Incremental Timeline Summarization Leveraging Large Language Models (2024.acl-long)
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| Challenge: | Prior work on timeline summarization has neglected the potential synergy between the two forms of timelines. |
| Approach: | They propose a timeline summarization approach that leverages large language models to generate both event and topic timelines. |
| Outcome: | The proposed approach outperforms the best existing approaches in four TLS benchmarks. |
ForumSum: A Multi-Speaker Conversation Summarization Dataset (2021.findings-emnlp)
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| Challenge: | Abstractive summarization quality has been improved but there is a lack of data for conversation summarizing applications. |
| Approach: | They propose to build a conversation summarization dataset with human written summaries from internet forums. |
| Outcome: | The proposed dataset can be easily expanded to improve conversation summarization applications. |
What’s under the hood: Investigating Automatic Metrics on Meeting Summarization (2024.findings-emnlp)
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| Challenge: | Existing evaluation metrics do not capture meeting-specific errors, leading to ineffective assessment. |
| Approach: | They examine the relationship between established metrics and human evaluations to determine what challenges and errors are captured by correlating metric scores with human evaluation. |
| Outcome: | The proposed measures show weak correlations with human evaluations and a third of the correlations show error masking. |
Unified Structure Generation for Universal Information Extraction (2022.acl-long)
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| Challenge: | Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. |
| Approach: | They propose a unified text-to-structure generation framework, namely UIE, which can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. |
| Outcome: | The proposed framework can model different IE tasks, generate targeted structures, and learn general IE abilities from different knowledge sources. |
Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach (D19-1)
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| Challenge: | 0Shot-TC is a challenging NLU problem to which little attention has been paid by the research community. |
| Approach: | They propose to use a standardized evaluation system to classify text snippets without seeing task specific training data. |
| Outcome: | The proposed model is based on a set of standardized evaluations and state-of-the-art baselines. |
Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution (P19-1)
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| Challenge: | Recognizing that various textual spans across multiple texts refer to the same entity or event is an important NLP task. |
| Approach: | They propose a neural architecture for cross-document coreference resolution by representing an event mention using its lexical span, surrounding context, and relation to other mentions via predicate-arguments structures. |
| Outcome: | The proposed model outperforms the state-of-the-art event coreference model on ECB+ while providing the first entity coreference results on this corpus. |
The Dots Have Their Values: Exploiting the Node-Edge Connections in Graph-based Neural Models for Document-level Relation Extraction (2020.findings-emnlp)
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| Challenge: | Existing methods for document-level relation extraction do not capture the representations of the nodes in the graphs. |
| Approach: | They propose to explicitly compute the representations for the nodes in the graph-based edge-oriented model for Document-level Relation Extraction (DRE) . they propose to introduce two novel representation regularization mechanisms to improve the representation vectors for DRE. |
| Outcome: | The proposed model achieves state-of-the-art performance on two benchmark datasets. |
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control (D19-1)
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| Challenge: | Selective rationalization is a common mechanism to ensure that predictive models reveal how they use any available features. |
| Approach: | They propose a co-operative method which uses introspection to explicitly predict and incorporate the outcome into the selection process. |
| Outcome: | The proposed model maintains high predictive accuracy and leads to comprehensive rationales. |
Named Entity Recognition via Noise Aware Training Mechanism with Data Filter (2021.findings-acl)
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| Challenge: | Existing methods for named entity recognition (NER) do not distinguish noisy from hard samples. |
| Approach: | They propose a noise-aware-with-filter method to help model identify noisy samples . they propose 'incomplete trust' loss function which boosts L CRF with a robust term . |
| Outcome: | The proposed method outperforms the existing methods on six real-world Chinese and English NER datasets. |
Document-level Entity-based Extraction as Template Generation (2021.emnlp-main)
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| Challenge: | Document-level entity-based extraction (EE) tasks extract entity-centric information from unstructured text across multiple sentences. |
| Approach: | They propose a generative framework for two document-level EE tasks: role-filler entity extraction (RE) and relation extraction ( RE). |
| Outcome: | The proposed framework captures cross-entity dependencies and avoids exponential computation complexity of identifying N-ary relations. |
OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework (2022.acl-long)
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| Challenge: | Different Open Information Extraction (OIE) tasks require different types of information. |
| Approach: | They propose to adapt an OIE Graph to different OIE tasks with simple rules . they implement an end-to-end OIA generator and make it open-accessible . |
| Outcome: | The proposed system achieves new SOTA performance on three popular OIE tasks. |
Iterative Document Representation Learning Towards Summarization with Polishing (D18-1)
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| Challenge: | Existing summarization methods read through document only once to generate a document representation, resulting in a sub-optimal representation. |
| Approach: | They propose an iterative model for supervised extractive text summarization which polishes the document representation on many passes through the document. |
| Outcome: | The proposed model outperforms state-of-the-art extractive systems on CNN/DailyMail and DUC2002 datasets. |
PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text (2022.findings-emnlp)
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| Challenge: | 305 open access scientific articles are used for synthesis action graphs . lack of annotated data has hindered progress in this field . |
| Approach: | They propose to annotate Polycrystalline Materials Synthesis Procedures PcMSP from 305 open access scientific articles for the construction of synthesis action graphs. |
| Outcome: | The proposed dataset contains the synthesis sentences, entity mentions and intra-sentence relations extracted from the experimental paragraphs. |
A Unified Generative Framework for Various NER Subtasks (2021.acl-long)
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| Challenge: | Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences. |
| Approach: | They propose to formulate NER subtasks as entity span sequence generation task . framework can be used to solve all three kinds of NER tasks without tagging schema . |
| Outcome: | The proposed framework achieves state-of-the-art (SoTA) or near SoTA performance on eight English NER datasets. |
Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction (2020.acl-main)
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| Challenge: | Sentence summarization systems that use latent space to reconstruct the source sentence are unwillingly exploited. |
| Approach: | They propose a method that uses language modeling and semantic similarity metrics to find a high-scoring summary. |
| Outcome: | The proposed method achieves state-of-the-art for unsupervised sentence summarization according to ROUGE scores. |
Explore Unsupervised Structures in Pretrained Models for Relation Extraction (2022.findings-emnlp)
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| Challenge: | Syntactic trees are widely used in relation extraction (RE) but they are not stable on different text domains and a pre-defined grammar may not fit the target relation schema. |
| Approach: | They propose to use unsupervised structures to extract relation extraction models . they also conduct detailed analyses on their abilities of adapting new RE domains . |
| Outcome: | The proposed models obtain competitive (even the best) performance scores on benchmark RE datasets. |
Distinguishing Between Foreground and Background Events in News (2020.coling-main)
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| Challenge: | a new task is needed to distinguish between foreground and background events in news articles . |
| Approach: | They propose a task of distinguishing between foreground and background events in news articles . they also identify the general temporal position of background events relative to the foregoing period . |
| Outcome: | The proposed model achieves good performance on a dataset of news articles . |
Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition (2020.coling-main)
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| Challenge: | Identifying motion entities in text is not only challenging but beneficial for a better natural language understanding. |
| Approach: | They propose a Motion Entity Tagging model to identify entities in motion in a text using the Literal-Motion-in-Text dataset for training and evaluating the model. |
| Outcome: | The proposed method improves the Named-Entity Recognition task by splitting clauses and phrases from complex and long motion sentences. |
Learning Adverbs with Spectral Mixture Kernels (2024.findings-acl)
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| Challenge: | In order for robots to collaborate with humans, it is important to share and understand their experiences through language. |
| Approach: | They propose a hierarchical Dirichlet Process-Spectral Mixture Latent Dirichlets Allocation model which learns the relationship between human motions and adverbs by capturing frequency kernels that represent motion characteristics and shared topics of a given aadverts. |
| Outcome: | The proposed model outperforms representative neural network models in terms of perplexity score and predicts more appropriate adverbs. |
HYDRA: A Multi-Head Encoder-only Architecture for Hierarchical Text Classification (2025.emnlp-main)
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| Challenge: | State-of-the-art approaches rely on complex components like graph encoders, label semantics, and autoregressive decoders. |
| Approach: | They propose a multi-head encoder-only architecture for hierarchical text classification that treats each level as a separate classification task with its own label space. |
| Outcome: | The proposed architecture matches or exceeds state-of-the-art methods on four benchmarks. |
KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi (2020.coling-main)
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| Challenge: | low-resource African languages are traditionally left behind because of the lack of well-annotated data and effective preprocessing. |
| Approach: | They propose two news datasets for multi-class classification of news articles in two low-resource African languages. |
| Outcome: | The proposed datasets show that training embeddings on the higher-resourced Kinyarwanda yields successful cross-lingual transfer to Kirundi. |
CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction (2021.acl-long)
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| Challenge: | Existing methods to reduce noise from DS generated training data are not effective for distantly supervised relation extraction (DSRE) |
| Approach: | They propose a multi-instance learning framework to reduce DS noise by dividing training instances into several bags and using them as new data units. |
| Outcome: | The proposed framework improves on NYT10, GDS and KBP with significant improvements over existing methods. |
Towards Better Hierarchical Text Classification with Data Generation (2023.findings-acl)
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| Challenge: | Existing methods to improve hierarchical text classification are expensive and lack high-quality labeled data. |
| Approach: | They propose a hierarchical text classification framework that can achieve both label controllability and text diversity by extracting high-quality hierarchic label information. |
| Outcome: | The proposed method can achieve label controllability and text diversity by extracting high-quality hierarchical label information. |
Rethinking Negative Sampling for Handling Missing Entity Annotations (2022.acl-long)
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| Challenge: | Empirical studies show low missampling rate and high uncertainty are both essential for achieving promising performances with negative sampling. |
| Approach: | They propose an adaptive and weighted sampling distribution that further improves negative sampling by introducing missampling and uncertainty concepts. |
| Outcome: | The proposed approach improves on synthetic and well-annotated datasets in terms of F1 score and loss convergence. |
SumTitles: a Summarization Dataset with Low Extractiveness (2020.coling-main)
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| Challenge: | Existing methods for extractive summarization of dialogue data are limited by the grammar and structure of the utterances used. |
| Approach: | They propose a low-extractive corpus of movie dialogues for abstractive text summarization . they use an alignment algorithm to construct the corpus and a baseline evaluation . |
| Outcome: | The proposed method is low-extractive and shows high performance in dialogue datasets. |
Compressive Summarization with Plausibility and Salience Modeling (2020.emnlp-main)
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| Challenge: | a new method to learn which compressions to apply is based on syntactic rules for deleting spans . plausibility and salience are the two main criteria for determining which compression to apply . a recent study shows that the plausability model generally selects for grammatical and factual deletions compared to extractive methods . |
| Approach: | They propose to leave the decision about what to delete to two data-driven criteria . they show that plausibility and salience are the most important criteria if a span is deleted . |
| Outcome: | The proposed method achieves strong in-domain results on benchmark datasets and human evaluation shows that plausibility model generally selects for grammatical and factual deletions. |
Grounded Multimodal Named Entity Recognition on Social Media (2023.acl-long)
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| Challenge: | Existing studies on Multimodal Named Entity Recognition only extract entity-type pairs in text, which is useless for multimodal knowledge graph construction. |
| Approach: | They propose a task to identify named entities in text and their bounding box groundings in image . they extend four well-known MNER methods to establish a number of baseline systems . |
| Outcome: | The proposed framework outperforms baseline systems on the GMNER task. |
Annotation and Analysis of Extractive Summaries for the Kyutech Corpus (L18-1)
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| Challenge: | Summarization of multi-party conversation requires corpora to analyze characteristics of conversations and construct a method for summary generation. |
| Approach: | They propose to annotate a Japanese conversation corpus for a decision-making task . they compare extractive summarization methods with the annotated extractive summary . |
| Outcome: | The proposed corpus is the first annotated for conversation summarization tasks and freely available to anyone. |
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification (2020.acl-main)
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| Challenge: | Existing approaches to comparative preference classification do not learn entity-aware representations well or use sequential modeling approaches that do not generalize well. |
| Approach: | They propose a deep-level deep-graph attention network that leverages word embeddings and syntactic information to solve a comparative preference classification problem. |
| Outcome: | The proposed model achieves state-of-the-art performance in comparative preference classification. |
Building Dataset for Grounding of Formulae — Annotating Coreference Relations Among Math Identifiers (2022.lrec-1)
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| Challenge: | Generally speaking, the meanings of math symbols are not necessarily constant, and the same symbol is used in multiple meanings. |
| Approach: | They annotated 15 papers with the meanings of math symbols and found they can be grounding . they developed a special annotation tool to help them identify the meaning of each symbol . |
| Outcome: | The constructed dataset shows that the meanings of symbols can be ground with a high agreement . the authors developed a special annotation tool to analyze the data . |
Improving Event Detection via Open-domain Trigger Knowledge (2020.acl-main)
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| Challenge: | Existing methods for event detecting are prone to overfitting densely labeled trigger words due to the small scale of training data. |
| Approach: | They propose a novel Enrichment Knowledge Distillation model to leverage external open-domain trigger knowledge to reduce in-built biases to frequent trigger words in annotations. |
| Outcome: | The proposed model outperforms nine strong baselines and is especially effective for unseen/sparsely labeled trigger words. |
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling (2020.acl-main)
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| Challenge: | Existing models for named entity recognition (NER) use sentence-level labels, which are expensive to obtain, to improve NER. |
| Approach: | They propose a sentence-level named entity recognition model that uses sentence-based labels that are easy to obtain. |
| Outcome: | The proposed model produces 3.78%, 4.20%, 2.08% improvements in F1 over the baseline on e-commerce product titles in Vietnamese, Thai, and Indonesian, respectively. |
SPECTRA: Sparse Structured Text Rationalization (2021.emnlp-main)
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| Challenge: | Sparse attention mechanisms are a deterministic alternative, but they lack a way to regularize rationale extraction. |
| Approach: | They propose a framework for deterministic extraction of structured explanations via constrained inference on a factor graph, forming a differentiable layer. |
| Outcome: | The proposed framework outperforms previous studies on performance and plausibility of extracted rationales. |
Cluster & Tune: Boost Cold Start Performance in Text Classification (2022.acl-long)
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| Challenge: | Existing methods to fine-tune pre-trained models for text classification are poor in practice. |
| Approach: | They propose to add an intermediate unsupervised classification task between pre-training and fine-tuning phases to boost performance of pre-trained models. |
| Outcome: | The proposed method improves performance on topical classification tasks when labeled data is scarce. |
A Prism Module for Semantic Disentanglement in Name Entity Recognition (P19-1)
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| Challenge: | Xu et al., 2015) proposed a noise reduction mechanism to disentangle semantics of words . hard and soft attention mechanisms are used to reduce noise in NLP tasks . |
| Approach: | They propose a prism module to disentangle semantic aspects of words and reduce noise . they propose combining prism modules with downstream models to improve model performance . |
| Outcome: | The proposed method significantly improves the performance of baselines on named entity recognition (NER) tasks. |
Multi-Document Scientific Summarization from a Knowledge Graph-Centric View (2022.coling-1)
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| Challenge: | Multi-Document Scientific Summarization (MDSS) aims to produce concise and concise summaries for clusters of topic-relevant scientific papers. |
| Approach: | They propose a model that incorporates knowledge graphs into paper encoding and decoding processes and propose 'decoder' for generating knowledge graph information of summary in the form of descriptive sentences. |
| Outcome: | The proposed architecture improves on baselines on the Multi-Xscience dataset. |
A Dataset of German Legal Documents for Named Entity Recognition (2020.lrec-1)
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| Challenge: | a dataset developed for Named Entity Recognition in German federal court decisions is available under a CC-BY 4.0 license. |
| Approach: | They describe a dataset developed for Named Entity Recognition in German federal court decisions. |
| Outcome: | The proposed dataset was developed for training an NER service for German legal documents in the EU project Lynx. |
EROS:Entity-Driven Controlled Policy Document Summarization (2024.lrec-main)
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| Challenge: | a privacy policy is a crucial component of any organization that allows it to legally collect, process, store, and/or distribute personal data. |
| Approach: | They propose to use a policy-document summarization dataset to enforce the summaries to include critical privacy-related entities and organization’s rationale in collecting those entities. |
| Outcome: | The proposed model improves over baselines and qualitatively evaluates the proposed model on human and qualitative data. |
Building Named Entity Recognition Taggers via Parallel Corpora (L18-1)
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| Challenge: | Existing methods to generate semantic processors for languages lacking hand curated data are inefficiently slow and unaffordable in terms of human resources and economic costs. |
| Approach: | They propose to use statistical word alignments to project annotations from multiple sources to a target language. |
| Outcome: | The proposed method is effective to transport NER annotations across languages . it can generate a good statistical model for a new target language . |
The Automatic Extraction of Linguistic Biomarkers as a Viable Solution for the Early Diagnosis of Mental Disorders (2022.lrec-1)
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| Challenge: | Digital Linguistic Biomarkers extracted from spontaneous language productions proved to be very useful for the early detection of various mental disorders. |
| Approach: | They propose a computational pipeline for the automatic extraction of DLBs from speech samples and written texts. |
| Outcome: | The proposed pipeline is designed to extract DLBs from speech samples and written texts. |
Incorporating Global Contexts into Sentence Embedding for Relational Extraction at the Paragraph Level with Distant Supervision (L18-1)
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| Challenge: | Existing approaches to relation extraction (RE) only extract relations from sentences that contain two target entities. |
| Approach: | They propose to incorporate global contexts from paragraph-into-sentence embedding into RE . they propose to use a knowledge base to extract relations between pairs of entities . |
| Outcome: | The proposed approach can learn an exact RE from sentences without syntactic parsing. |
WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks (2026.findings-acl)
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| Challenge: | Large-language-model (LLM) agents are competent at straightforward web tasks, but struggle with complex tasks. |
| Approach: | They propose a general framework that decomposes web tasks into three subtasks . they show that WebDART lifts end-to-end success rates by 13.7 percentage points . |
| Outcome: | Evaluated on WebChoreArena, WebDART lifts success rates by 13.7 percentage points over previous state-of-the-art agents. |
Extracting, Detecting, and Generating Research Questions for Scientific Articles (2025.coling-main)
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Sina Taslimi, Artemis Capari, Hosein Azarbonyad, Zi Long Zhu, Zubair Afzal, Evangelos Kanoulas, George Tsatsaronis
| Challenge: | Existing tools to generate and extract RQs from scientific articles lack a definition of RQ in articles. |
| Approach: | They propose to use a set of regular expressions to identify articles with well-defined RQs and a detection component to identify more complex RQ's in articles. |
| Outcome: | The proposed pipeline can detect and generate RQs from scientific articles and generate high-quality ones. |
Annotated Corpus of Scientific Conference’s Homepages for Information Extraction (L18-1)
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| Challenge: | a corpus of scientific conferences contains homepages with annotations of important information . name of conference, abbreviation, place, submission, notification, camera ready dates are included . |
| Approach: | They propose a corpus that contains 943 homepages of scientific conferences with annotations of interesting information. |
| Outcome: | The proposed corpus contains 943 homepages of scientific conferences, 14794 including subpages . the results show that it can be used as a reference data set for this type of task. |
Event Causality Recognition Exploiting Multiple Annotators’ Judgments and Background Knowledge (D19-1)
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| Challenge: | Existing methods for recognizing event causality written in web texts ignore each annotator's independent judgments, but we exploit each anorator''s judgments to predict the majority vote labels. |
| Approach: | They propose to grasp each annotator's policy by training multiple classifiers that predict the labels given by a single annotators and combine the outputs to predict the final labels determined by majority vote. |
| Outcome: | The proposed methods grasp each annotator's policy and combine the outputs to predict the final labels determined by majority vote. |
Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction (2023.findings-acl)
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| Challenge: | Existing methods for semantic frame induction are labor intensive . a method that uses contextualized embeddings can be used to acquire frame element knowledge. |
| Approach: | They propose a method that applies deep metric learning to semantic frame induction tasks . they use a pre-trained language model to fine-tune frame-annotated models to perform argument clustering . |
| Outcome: | The proposed method achieves substantially better performance than existing methods on FrameNet. |
Annotating Perspectives on Vaccination (2020.lrec-1)
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| Challenge: | Vaccination corpus is a corpus of texts related to the online vaccination debate . it contains documents from the Internet which reflect different views on vaccinations . |
| Approach: | They present a corpus of texts related to the online vaccination debate annotated with perspectives about attribution, claims and opinions. |
| Outcome: | The Vaccination Corpus contains 294 documents from the Internet which reflect different views on vaccinations. |
OD-RTE: A One-Stage Object Detection Framework for Relational Triple Extraction (2023.acl-long)
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| Challenge: | Existing pipelines for relational triple extraction are underutilizing regional information of triple. |
| Approach: | They propose a one-stage Object Detection framework for Relational Triple Extraction . framework uses vertices-based bounding box detection and global relational triple region detection . |
| Outcome: | The proposed framework could extract all types of triples on two widely used datasets. |
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing (2023.findings-acl)
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| Challenge: | Recent work on distantly supervised (DS) ultra-fine entity typing has received significant attention . however, DS data is noisy and often suffers from missing or wrong labeling issues resulting in low precision and low recall. |
| Approach: | They propose a noise model to estimate unknown labeling noise distribution over input contexts and noisy type labels and a model to train on denoised data. |
| Outcome: | The proposed model outperforms baseline methods on the Ultra-Fine entity typing dataset and OntoNotes dataset. |
Federated Document-Level Biomedical Relation Extraction with Localized Context Contrast (2024.lrec-main)
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| Challenge: | Existing studies on relation extraction focus on document-level training without sharing raw medical texts. |
| Approach: | They propose a federated framework for relation extraction that enables collaborative training without sharing raw medical texts. |
| Outcome: | The proposed framework extends document-level relation extraction to a federated environment. |
Enhancing Continual Relation Extraction via Classifier Decomposition (2023.findings-acl)
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| Challenge: | Existing studies only adopt a vanilla strategy when learning representations of new relations . experimental results show that the importance of the first training stage to CRE models may be underestimated. |
| Approach: | They propose a framework that splits the last FFN layer into separated previous and current classifiers to maintain previous knowledge and encourage model to learn more robust representations at this training stage. |
| Outcome: | The proposed framework outperforms the state-of-the-art models on two benchmarks. |
Deep Attention Diffusion Graph Neural Networks for Text Classification (2021.emnlp-main)
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| Challenge: | Existing methods for text classification based on graph neural networks (GNNs) consider only one-hop neighborhoods and low-frequency information within texts, which suffer from over-smoothing issues if many graph layers are stacked. |
| Approach: | They propose a deep attention diffusion Graph Neural Network model to learn text representations by bridging the chasm of interaction difficulties between a word and its distant neighbors. |
| Outcome: | The proposed model outperforms existing methods on standard benchmark datasets on a set of textual features. |
CaRB: A Crowdsourced Benchmark for Open IE (D19-1)
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| Challenge: | Open Information Extraction (Open IE) systems have been evaluated traditionally via manual annotation. |
| Approach: | They propose to use a dataset to score Open IE systems by matching system predictions with benchmark datasets. |
| Outcome: | The proposed framework matches predictions with the benchmark dataset and is noisy and inconsistent. |
Context-based Virtual Adversarial Training for Text Classification with Noisy Labels (2022.lrec-1)
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| Challenge: | Recent studies show that deep neural networks can memorize noisy labels with limited training time. |
| Approach: | They propose a virtual adversarial training method to prevent a classifier from overfitting to noisy labels. |
| Outcome: | The proposed method performs the adversarial training in the context rather than the inputs. |
German Also Hallucinates! Inconsistency Detection in News Summaries with the Absinth Dataset (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have made significant progress on a wide range of natural language processing tasks, but they still suffer from hallucinating information in their output. |
| Approach: | They propose to use an annotated dataset to detect hallucinations in german news summarization and open-source it to foster further research on hallucinosity detection in german. |
| Outcome: | The proposed model can detect hallucinations in the output and evaluate the faithfulness of the summaries. |
Unsupervised Non-transferable Text Classification (2022.emnlp-main)
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| Challenge: | Existing methods to train a good deep learning model require labeled data for the target domain which can be difficult to obtain. |
| Approach: | They propose an unsupervised non-transferable learning method that does not require annotated target domain data and introduce a secret key component for recovering the model’s access to the target domain. |
| Outcome: | The proposed method reduces model generalization ability in specific target domains while still recovering access to the target domain. |
BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition (2025.coling-main)
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| Challenge: | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that aims to detect the entity spans of text and classify them into pre-defined set of entity types. |
| Approach: | They propose a boundary-aware contrastive learning strategy to enhance the LLM’s ability to perceive entity boundaries for generalized entity spans. |
| Outcome: | The proposed framework outperforms prior methods and validates its effectiveness across a range of LLM architectures. |
NCRE: A Benchmark for Document-level Nominal Compound Relation Extraction (2025.coling-main)
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| Challenge: | Existing work focuses on detecting specific relations between entities, often constrained to specific fields and lacking general applicability. |
| Approach: | They propose a novel task that concentrates on abstract relation extraction between noun phrases . they annotate a Chinese dataset and develop a model incorporating a rotary position-enhanced word pair detection schema. |
| Outcome: | The proposed task is more efficient than previous methods. |
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification (2021.emnlp-main)
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| Challenge: | Data augmentation aims to alleviate the overfitting issue in low-resource or class-imbalanced situations. |
| Approach: | They propose a framework called Text AutoAugment to enhance training samples . they use a Bayesian optimization algorithm to search for the best policy . |
| Outcome: | The proposed framework outperforms baseline methods on six benchmark datasets. |
HiCOT: Improving Neural Topic Models via Optimal Transport and Contrastive Learning (2025.findings-acl)
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| Challenge: | Recent advances in neural topic models (NTMs) have improved topic quality but still face challenges: weak document-topic alignment, high inference costs due to large pretrained language models, and limited modeling of hierarchical topic structures. |
| Approach: | They propose a framework that integrates hierarchical clustering and contrastive learning to refine document-topic relationships using compact PLM-based embeddings. |
| Outcome: | The proposed framework improves topic coherence, topic performance, representation quality and computational efficiency over existing NTMs. |
Extraction of Hyponymic Relations in French with Knowledge-Pattern-Based Word Sketches (2020.lrec-1)
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| Challenge: | Hyponymy is the cornerstone of taxonomies and concept hierarchies. |
| Approach: | They propose a French hyponymic sketch grammar for Sketch Engine based on knowledge patterns. |
| Outcome: | The proposed grammar extracts hyponymic pairs from any user-owned corpus. |
Distantly Supervised Course Concept Extraction in MOOCs with Academic Discipline (2023.acl-long)
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| Challenge: | Existing methods to extract knowledge concepts from MOOCs are noisy and incomplete because of the limited dictionary and diverse MOOC. |
| Approach: | They propose to automatically extract course concepts using distant supervision to eliminate the heavy work of human annotations. |
| Outcome: | The proposed framework outperforms state-of-the-art methods with 7% absolute improvement in F1 score. |
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models (2020.emnlp-main)
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| Challenge: | We present a method to produce abstractive summaries of documents that exceed several thousand words . we compare transformer based methods to extractive methods, but extractive models score higher . |
| Approach: | They propose a method to generate abstractive summaries of documents that exceed several thousand words via neural abstractive summary. |
| Outcome: | The proposed method produces abstractive summaries of documents that exceed several thousand words . it is compared with baseline methods, state-of-the-art models and variants of the proposed method . |
Human Raters Cannot Distinguish English Translations from Original English Texts (2023.emnlp-main)
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| Challenge: | Prior work on translationese has identified common hallmarks of translationeses, but human accuracy of identifying translated text is understudied. |
| Approach: | They perform an evaluation of English original/translated texts to examine whether raters can classify texts as being original or translated English and the features that lead rater to judge text as being translated. |
| Outcome: | The results provide critical insight into work in translation studies and context for assessments of translationese classifiers. |
Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring (2020.acl-main)
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| Challenge: | Automated essay scoring (AES) can grade essays at scale, while automated writing evaluation (AWE) does not provide useful feature representations for supporting AWE. |
| Approach: | They propose a method for linking AWE and neural AES by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. |
| Outcome: | The proposed system is comparable to existing AWE systems for grading essays and representing essays as rubric-based features. |
Improving Faithfulness of Large Language Models in Summarization via Sliding Generation and Self-Consistency (2024.lrec-main)
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| Challenge: | Abstractive summarization models (LLMs) have demonstrated impressive performance in various tasks, but they are still suffering from factual inconsistency problem called hallucination. |
| Approach: | They propose to improve the faithfulness of large language models by impelling them to process the entire article more fairly and faithfully. |
| Outcome: | The proposed strategy improves the faithfulness of large language models in summarization while maintaining their fluency and informativeness. |
CrisisTS: Coupling Social Media Textual Data and Meteorological Time Series for Urgency Classification (2025.acl-long)
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| Challenge: | Existing studies on fusion of texts and tabular-based time series to improve performance of NLP applications have focused on coupling texts with tabular time series. |
| Approach: | They propose a multimodal and multilingual dataset for urgency classification that allows for temporal and location alignment even in the absence of location mention in the text. |
| Outcome: | The proposed dataset outperforms text-only models in many applications while ensuring model generalizability. |
CQE: A Comprehensive Quantity Extractor (2023.emnlp-main)
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| Challenge: | Quantities are essential in documents to describe factual information. |
| Approach: | They propose a comprehensive quantity extraction framework that detects combinations of values and units, the behavior of a quantity and the concept a quantity is associated with. |
| Outcome: | The proposed framework outperforms existing methods and is the first to detect concepts associated with identified quantities. |
Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction (2021.emnlp-main)
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| Challenge: | State-of-the-art NLP models adopt shallow heuristics that limit their generalization capability. |
| Approach: | They propose to use heuristics that limit their generalization capability to model lexical overlap with the training set in Named-Entity Recognition and Event or Type heuristic in Relation Extraction to test their models. |
| Outcome: | The proposed model can perform better on the two key tasks, while the retention of training relation triples. |
Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance (2023.findings-acl)
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| Challenge: | Document-level event argument extraction is a challenging task for cross-sentence inference . previous work focused on document-level EAE, but recent work focused more on documentlevel . |
| Approach: | They propose a document-level event argument extraction model that captures contextual clues and latent role information. |
| Outcome: | The proposed model outperforms existing methods on two public datasets with 1.13 F1 and 2.64 F1 improvements on RAMS and WikiEvents respectively. |
SC-CoMIcs: A Superconductivity Corpus for Materials Informatics (2020.lrec-1)
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| Challenge: | Existing corpus of superconducting materials in Materials Informatics (MI) is limited. |
| Approach: | They propose to create a corpus tailored for the text mining of superconducting materials in Materials Informatics. |
| Outcome: | The proposed corpus can find terms relevant to a query term within a specified Named Entity category. |
Learning Event-aware Measures for Event Coreference Resolution (2023.findings-acl)
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| Challenge: | Existing models for event coreference resolution are based on entity-level tasks, but event coreferent resolution is a challenge. |
| Approach: | They propose a model that learns and integrates multiple representations from event alone and event pair on the basis of event but not entity as before. |
| Outcome: | The proposed model achieves new state-of-the-art on the ACE 2005 benchmark, demonstrating the effectiveness of the proposed framework. |
From Speculation Detection to Trustworthy Relational Tuples in Information Extraction (2023.findings-emnlp)
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| Challenge: | Existing studies on speculation detection are defined at sentence level, but not all factual tuples extracted from a sentence are speculative. |
| Approach: | They propose to study speculations in OIE tuples and determine whether a tample is speculative. |
| Outcome: | The proposed model is based on the LSOIE dataset and provides labels for speculative tuples. |
Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction (2023.acl-long)
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| Challenge: | Document-level relation extraction (DocRE) aims to extract semantic relations between entities in a document. |
| Approach: | They propose a Document-level distant relation extraction framework with unreliable pseudo labels to denoise DS data. |
| Outcome: | The proposed framework outperforms strong baselines on two public datasets. |
BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance (2023.findings-emnlp)
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Karel D’Oosterlinck, François Remy, Johannes Deleu, Thomas Demeester, Chris Develder, Klim Zaporojets, Aneiss Ghodsi, Simon Ellershaw, Jack Collins, Christopher Potts
| Challenge: | pharmacovigilance (PV) is a tool for analyzing adverse drug events from biomedical literature . pharmacologists use natural language processing to extract core information from papers . |
| Approach: | They propose a resource for biomedical adverse drug event eXtraction using natural language processing. |
| Outcome: | The proposed model achieves 59.1% F1 (validation) and estimates human performance to be 72.0% F1 . the proposed model could be used to improve drug safety monitoring, also called pharmacovigilance, in the future. |
Toward Reliable Ad-hoc Scientific Information Extraction: A Case Study on Two Materials Dataset (2024.findings-acl)
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Satanu Ghosh, Neal Brodnik, Carolina Frey, Collin Holgate, Tresa Pollock, Samantha Daly, Samuel Carton
| Challenge: | Existing methods for ad-hoc schema-based information extraction are brittle and non-transferable, limiting their practicality for this type of one-off extraction task. |
| Approach: | They propose to use GPT-4 to perform ad-hoc schema-based information extraction from scientific literature. |
| Outcome: | The proposed model can replicate two existing material science datasets, one pertaining to multi-principal element alloys and one to silicate diffusion, and draw on their insights to suggest future research directions. |
Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization (2023.emnlp-main)
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| Challenge: | Existing methods for biomedical named entity recognition require laborious human effort. |
| Approach: | They propose a Synonym Generalization framework that recognizes biomedical concepts using span-based predictions. |
| Outcome: | The proposed framework outperforms dictionary-based approaches on a wide range of benchmarks. |
Prototype-Guided Pseudo Labeling for Semi-Supervised Text Classification (2023.acl-long)
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| Challenge: | Existing semi-supervised text classification methods suffer from categorical boundary issues . existing methods suffer by ambiguous categoric boundaries, making it difficult to generate reliable pseudo-labels for each category. |
| Approach: | They propose a semi-supervised framework that assigns pseudo-labels to unlabeled data . they exploit categorical prototypes to assimilate instance representations within the same category . |
| Outcome: | Empirical studies show that the proposed framework is effective . it uses prototypical cluster separation and prototypical-center data selection . |
VeriFact: Enhancing Long-Form Factuality Evaluation with Refined Fact Extraction and Reference Facts (2025.emnlp-main)
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| Challenge: | Prior work focuses on accuracy and precision, but factuality evaluation is difficult due to inter-sentence dependencies. |
| Approach: | They introduce a factuality evaluation framework to enhance fact extraction . they also introduce 'factRBench' that evaluates both precision and recall . |
| Outcome: | The proposed framework enhances fact extraction by identifying incomplete and missing facts . it also evaluates precision and recall in long-form models, whereas prior work focuses on precision. |
Multi-label Sequential Sentence Classification via Large Language Model (2024.findings-emnlp)
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| Challenge: | Existing approaches to sequential sentence classification are constrained by model size, sequence length, and single-label setting. |
| Approach: | They propose a large language model-based framework for both single- and multi-label SSC tasks that generate SSC labels through designed prompts. |
| Outcome: | The proposed framework enhances task understanding by incorporating demonstrations and a query to describe the prediction target. |
CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities (2023.emnlp-main)
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| Challenge: | Existing methods for event coreference resolution (ECR) do not leverage human-summarized rules to guide the model. |
| Approach: | They propose to transform ECR into a cloze-style MLM task using a prompt-based approach . they introduce two auxiliary prompt tasks, event-type compatibility and argument compatibility . |
| Outcome: | The proposed method performs well in a state-of-the-art (SOTA) benchmark. |
Semantic Component Analysis: Introducing Multi-Topic Distributions to Clustering-Based Topic Modeling (2025.findings-emnlp)
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| Challenge: | Existing methods for topic modeling fail to scale to large datasets or assume one topic per document. |
| Approach: | They propose a topic modeling technique that discovers multiple topics per sample . they evaluate SCA on Twitter datasets in English, Hausa and Chinese . |
| Outcome: | The proposed technique outperforms the LLM-based TopicGPT on Twitter datasets with similar compute budgets. |
Decoding the Rule Book: Extracting Hidden Moderation Criteria from Reddit Communities (2025.emnlp-main)
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| Challenge: | Existing methods to train classifiers that predict norm violations are often opacity-prone . a new approach to identify and extract these implicit criteria from historical moderation data is proposed . |
| Approach: | They propose to extract implicit criteria from historical moderation data using an interpretable architecture. |
| Outcome: | The proposed model replicates neural moderation models while providing transparent insights into decision-making processes. |
Closed Boundary Learning for Classification Tasks with the Universum Class (2023.findings-emnlp)
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| Challenge: | Existing methods treat the Universum class equally with the classes of interest, leading to problems such as overfitting, misclassification, and diminished model robustness. |
| Approach: | They propose a closed boundary learning method that applies closed decision boundaries to classes of interest and designates the area outside all closed boundaries as the Universum class. |
| Outcome: | The proposed method improves accuracy and robustness of classification models on six state-of-the-art tasks. |
NeuroTrialNER: An Annotated Corpus for Neurological Diseases and Therapies in Clinical Trial Registries (2024.emnlp-main)
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Simona Doneva, Tilia Ellendorff, Beate Sick, Jean-Philippe Goldman, Amelia Cannon, Gerold Schneider, Benjamin Ineichen
| Challenge: | Despite substantial investment, developing new treatments for neurological conditions is a challenging and often unsuccessful endeavour. |
| Approach: | They propose a corpus for named entity recognition that is annotated clinical trial summaries from ClinicalTrials.gov. |
| Outcome: | The proposed corpus is annotated for neurological diseases, therapeutic interventions, and control treatments and achieves a close-to-human performance. |
Unsupervised Extraction of Dialogue Policies from Conversations (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are used to extract dialogue policies from conversational data. |
| Approach: | They propose a method for extracting dialogue policies from conversational data using canonical forms and graph traversal algorithms. |
| Outcome: | The proposed method gives conversation designers greater control and improves the process of developing dialogue policies. |
Penetrating Linguistic Disguises: A Slang-aware Label-Aligned Framework for Fine-Grained Toxicity Extraction in Chinese Hate Speech Detection (2026.findings-acl)
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| Challenge: | Flexible word boundaries and linguistic obfuscation, particularly slang, challenge precise span-level hate speech detection in Chinese. |
| Approach: | They propose a Slang-aware Label-Aligned Framework that maps slang to explicit hate semantics and uses task-specific branches to mitigate feature interference. |
| Outcome: | The proposed framework reduces ambiguity by mapping obscure slang to explicit hate semantics. |
On an Intermediate Task for Classifying URL Citations on Scholarly Papers (2024.lrec-main)
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| Challenge: | Citations using URLs can be used as information source for research resource search engines. |
| Approach: | They propose a method to classify URL citations using a simple fine-tuning strategy. |
| Outcome: | The proposed method outperforms methods using a simple fine-tuning strategy with higher macro F-scores for different model sizes and architectures. |
ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction (2026.findings-acl)
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Wenda Liu, Song Zhigang, Shuai Nie, Guangyao Liu, Lisung Chen, Binyu Yang, Yaran Chen, Peng Zhou, Hongzhen Wang, Yuchen Liu, Wenyue Hu, Jiaming Xu, Runyu Shi, Ying Huang
| Challenge: | ProUIE improves universal information extraction (UIE) without external information . many LLM-based methods rely on extra schema cues, external resources or complex alignment and verification pipelines . |
| Approach: | They propose a Macro-to-Micro progressive learning approach that improves UIE without external information. |
| Outcome: | ProUIE outperforms instruction-tuned baselines on average for NER and RE while using a smaller backbone. |
PPORTAL_ner: An Annotated Corpus of Portuguese Literary Entities (2024.lrec-main)
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| Challenge: | Annotated corpus of 25 literary texts provides a rich set of annotations for Named Entity Recognition models. |
| Approach: | They propose an annotation dataset that simplifies the development of Named Entity Recognition models for Portuguese literary texts. |
| Outcome: | The proposed dataset simplifies the development of Named Entity Recognition models for Portuguese literary works. |
Pruning before Fine-tuning: A Retraining-free Compression Framework for Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Structured pruning is an effective technique for compressing pre-trained language models (PLMs), but it requires retraining, leading to additional computational overhead. |
| Approach: | They propose a task-specific pruning framework that prunes redundant modules of pre-trained language models before fine-tuning them. |
| Outcome: | The proposed pruning framework achieves higher performance on GLUE, SQUAD, WikiText-2, Wik-103, and PTB datasets while reducing the time required for fine-tuning. |
BOOKCOREF: Coreference Resolution at Book Scale (2025.acl-long)
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| Challenge: | Existing benchmarks for coreference resolution systems are limited in length and do not adequately assess system capabilities at the book scale. |
| Approach: | They propose a novel pipeline that produces high-quality coreference resolution annotations on full narrative texts and a book-scale benchmark, BOOKCOREF. |
| Outcome: | The proposed pipeline produces high-quality coreference resolution annotations on full texts with an average document length of more than 200,000 tokens. |
Mahānāma: A Unique Testbed for Literary Entity Discovery and Linking (2025.emnlp-main)
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| Challenge: | High lexical variation, ambiguous references, and long-range dependencies make entity resolution in literary texts particularly challenging. |
| Approach: | They present a large-scale dataset for end-to-end Entity Discovery and Linking (EDL) in Sanskrit. |
| Outcome: | The proposed dataset is aligned with an English knowledge base to support cross-lingual linking. |
Semantic Frame Extraction in Multilingual Olfactory Events (2024.lrec-main)
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| Challenge: | Despite the interest in studying this domain, little effort has been devoted to develop tools and models that can extract olfactory information from large amounts of text in a structured and scalable way. |
| Approach: | They propose a system for multilingual olfactory information extraction covering six European languages, namely English, French, Italian, Dutch, German and Slovene. |
| Outcome: | The proposed system detects olfactory related text adopting a FrameNet-like structure and identifies the lexical units triggering the smell event and a set of frame elements. |
Semantic Networks Extracted from Students’ Think-Aloud Data are Correlated with Students’ Learning Performance (2025.emnlp-main)
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| Challenge: | Largescale open online courses (MOOCs) are available to hundreds of millions of learners, but efficiently evaluating these students' performance remains a crucial task for educators. |
| Approach: | They propose to use textbook-based information as a semantic network to extract concepts and relations from students' verbal data. |
| Outcome: | The proposed models extract concepts and relations from students’ verbal data and show that denser and more interconnected networks were associated with more elaborated knowledge acquisition. |
Query-driven Document-level Scientific Evidence Extraction from Biomedical Studies (2025.acl-long)
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Massimiliano Pronesti, Joao H Bettencourt-Silva, Paul Flanagan, Alessandra Pascale, Oisín Redmond, Anya Belz, Yufang Hou
| Challenge: | Systematic reviews are widely regarded as the gold standard in evidence-based medicine, heavily influencing medical decisions made by doctors, health authorities, and patients. |
| Approach: | They propose a retrieval-augmented generation framework to tackle the unique challenges of evidence extraction by leveraging forest plots from Cochrane systematic reviews. |
| Outcome: | The proposed framework outperforms existing methods by up to 10.3% in the F1 score on this task. |
CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization (2025.findings-acl)
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Mst. Fahmida Sultana Naznin, Adnan Ibney Faruq, Mostafa Rifat Tazwar, Md Jobayer, Md. Mehedi Hasan Shawon, Md Rakibul Hasan
| Challenge: | Pretrained models that excel in abstractive summarization problems face challenges when applied to specialized medical domains due to complex terminology and the necessity for accurate clinical context. |
| Approach: | They propose a sequential transfer learning model that ensures key content extraction and coherent summarization. |
| Outcome: | The proposed model shows 56.2% improvement in BLEU-1, 40.5% in ble-2, 84.3% in blu-3, 28.9% in ROUGE-1, 41.0% in Rough-2 and 26.5% of ROGUE-3 over benchmark studies. |
Text Embedding as Treatment: A Meta Causal Approach for Robust Sentiment Classification (2026.findings-acl)
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Fengxiang Cheng, Chuan Zhou, Xiang Li, Haoxuan Li, Wen-li Wang, Jinkun Chen, Mingming Gong, Kun Zhang
| Challenge: | Existing methods for sentiment classification use binary treatment of words . Existing approaches limit generalizability to novel words and low-frequency words if there is a word in a sentence that is not treated . |
| Approach: | They propose a meta-causal approach that uses a single training task to identify causal words for arbitrary words. |
| Outcome: | The proposed method reduces the spurious correlation between word treatment and sentiment classification by removing words with low treatment effects from a pre-trained language model. |