IE

Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences (2024.naacl-long)

Challenge:	Existing approaches to event-centric natural language understanding (NLU) have been limited to linear and temporal ones.
Approach:	They propose a human-in-the-loop schema induction system powered by GPT-3 . they show that it transfers to new domains more easily than previous approaches .
Outcome:	The proposed system transfers to new domains more easily than previous approaches and reduces human curation.

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Hongyi Liu, Qingyun Wang, Payam Karisani, Heng Ji

Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks (2021.naacl-main)

Challenge:	Existing models for named entity recognition fail in scientific domains such as biomedicine and chemistry.
Approach:	They propose a model to transfer knowledge from the biomedical domain to the target domain . they use pseudo labeling and contrastive learning to enhance discrimination .
Outcome:	The proposed model outperforms baseline models by up to 5% . the proposed model is based on a biomedical domain model and a chemical domain model .

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Minh Van Nguyen, Viet Dac Lai, Thien Huu Nguyen

New Frontiers of Information Extraction (2022.naacl-tutorials)

Challenge:	Existing work on information extraction (IE) has solved the four main tasks separately, thus failing to benefit from inter-dependencies between tasks.
Approach:	They propose a model to solve four IE tasks in a single model that captures inter-dependencies between tasks.
Outcome:	The proposed model achieves state-of-the-art performance on monolingual and multilingual learning settings with three different languages.

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Muhao Chen, Lifu Huang, Manling Li, Ben Zhou, Heng Ji, Dan Roth

Scalable Construction and Reasoning of Massive Knowledge Bases (N18-6)

Challenge:	Information extraction (IE) is the process of automatically extracting structural information from unstructured or semi-structured data.
Approach:	This tutorial will provide an introduction to recent advances in IE by answering several important research questions.
Outcome:	The tutorial will address several important research questions and outline directions for further investigation.

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Xiang Ren, Nanyun Peng, William Yang Wang

Knowledge Representation

Challenge:	Existing knowledge mining systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages.
Approach:	They introduce how to extract structured facts from text corpora to construct knowledge bases.
Outcome:	The proposed methods are weakly-supervised and domain-independent for knowledge base construction across various domains.

General Collaborative Framework between Large Language Model and Experts for Universal Information Extraction (2024.findings-emnlp)

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K Bao, Ning Wang

ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations (2022.naacl-demo)

Challenge:	Existing unified information extraction approaches face challenges such as noise interference, abstract label semantics, and diverse span granularity.
Approach:	They propose a general Collaborative Information Extraction framework to address these challenges in universal information extraction tasks.
Outcome:	The proposed framework is based on a general Recognizer and task-specific Experts for recognizing predefined types and extracting spans respectively.

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Oscar Sainz, Haoling Qiu, Oier Lopez de Lacalle, Eneko Agirre, Bonan Min

Abstract Meaning Representation Guided Graph Encoding and Decoding for Joint Information Extraction (2021.naacl-main)

Challenge:	Information Extraction (IE) analysts use supervised machine learning to define the schema and build a training corpus with annotated examples.
Approach:	They propose a workflow where the analyst verbalizes the entities/relations, which are then used by a Textual Entailment model to perform zero-shot IE.
Outcome:	The proposed workflow performs very well on four IE tasks with a single user interface and a video demonstration is available on vimeo.

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Zixuan Zhang, Heng Ji

NESTLE: a No-Code Tool for Statistical Analysis of Legal Corpus (2024.eacl-demo)

Challenge:	Abstract Meaning Representation (IE) and Information Extraction (IE), both focus on extracting the main information from natural language texts.
Approach:	They propose an AMR-guided framework for joint information extraction using a pre-trained AMR parser.
Outcome:	The proposed framework achieves state-of-the-art on all IE subtasks.

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Kyoungyeon Cho, Seungkum Han, Young Rok Choi, Wonseok Hwang

Challenge:	a comprehensive statistical analysis of legal corpus requires specialized tools or programming skills.
Approach:	They propose a no-code tool for large-scale statistical analysis of legal corpus . NESTLE can extract any type of information that has not been predefined in the IE system .
Outcome:	The proposed tool can perform comparable to LexGLUE on 15 Korean precedent IE tasks and 3 legal text classification tasks.

FS-DAG: Few Shot Domain Adapting Graph Networks for Visually Rich Document Understanding (2025.coling-industry)

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Amit Agarwal, Srikant Panda, Kulbhushan Pachauri

GLiNER2: Schema-Driven Multi-Task Learning for Structured Information Extraction (2025.emnlp-demos)

Challenge:	Recent advances in vision-language models have significantly enhanced performance across various natural language processing and computer vision tasks.
Approach:	They propose a few shot domain adapting graph (FS-DAG) that leverages domain-specific and language/vision specific backbones within a modular framework to adapt to diverse document types with minimal data.
Outcome:	The proposed model is highly performant with less than 90M parameters, making it well-suited for complex real-world applications for information extraction tasks where computational resources are limited.

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Urchade Zaratiana, Gil Pasternak, Oliver Boyd, George Hurn-Maloney, Ash Lewis

JSEEGraph: Joint Structured Event Extraction as Graph Parsing (2023.starsem-1)

Challenge:	Existing solutions for information extraction (IE) require specialized models for different tasks or require expensive large language models.
Approach:	They propose a framework that enhances the original GLiNER architecture to support named entity recognition, text classification, and hierarchical structured data extraction within a single efficient model.
Outcome:	The proposed framework improves performance across diverse IE tasks and accessibility compared to LLM-based alternatives.

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Huiling You, Lilja Vrelid, Samia Touileb

Text2DB: Integration-Aware Information Extraction with Large Language Model Agents (2024.findings-acl)

Challenge:	Existing approaches model event extraction using simplified datasets or sequence-labeling-based encodings.
Approach:	They propose a graph-based event extraction framework that explicitly encodes entities and events in a single semantic graph.
Outcome:	The proposed framework can handle nested event structures and solve different IE tasks jointly.

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Yizhu Jiao, Sha Li, Sizhe Zhou, Heng Ji, Jiawei Han

IEPile: Unearthing Large Scale Schema-Conditioned Information Extraction Corpus (2024.acl-short)

Challenge:	Current methods for information extraction (IE) focus on integrating IE output with the database . a long-overlooked question is what counts as "relevant knowledge"
Approach:	They propose a task that emphasizes integration of IE output and the database . they introduce a benchmark and an LLM agent framework for this task .
Outcome:	The proposed task integrates IE output and the target database (or knowledge base) it meets common demands such as data infilling, row population, and column addition .

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Honghao Gui, Lin Yuan, Hongbin Ye, Ningyu Zhang, Mengshu Sun, Lei Liang, Huajun Chen

Prompts Can Play Lottery Tickets Well: Achieving Lifelong Information Extraction via Lottery Prompt Tuning (2023.acl-long)

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.

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Zujie Liang, Feng Wei, Yin Jie, Yuxi Qian, Zhenghong Hao, Bing Han

mOKB6: A Multilingual Open Knowledge Base Completion Benchmark (2023.acl-short)

Challenge:	Existing research on information extraction tasks focuses on one specific task, but in real-world scenarios, new data of different IE tasks and domains come in a stream over time.
Approach:	They propose a parameter- and deployment-efficient prompt tuning method to evaluate the UIE system under a “lifelong learning” setting.
Outcome:	The proposed method is able to learn new tasks without forgetting old ones and expand knowledge and functionalities without retraining the whole system.

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Shubham Mittal, Keshav Kolluru, Soumen Chakrabarti, null Mausam

A Graphical Interface for Curating Schemas (2021.acl-demo)

Challenge:	Open knowledge bases (Open KBCs) are constructed from triples of the form, which can be denoted as (s, r, o) by using open information extraction (Open IE) systems.
Approach:	They construct a dataset with facts from Wikipedia in six languages . they use open information extraction systems to extract triples from text .
Outcome:	The proposed dataset contains facts from Wikipedia in six languages including English . it improves existing Open KB construction pipeline by doing multilingual coreference resolution and keeping only entity-linked triples .

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Piyush Mishra, Akanksha Malhotra, Susan Windisch Brown, Martha Palmer, Ghazaleh Kazeminejad

AutoRE: Document-Level Relation Extraction with Large Language Models (2024.acl-demos)

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.

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Lilong Xue, Dan Zhang, Yuxiao Dong, Jie Tang

Graphene: a Context-Preserving Open Information Extraction System (C18-2)

Challenge:	Existing methods for relation extraction are limited to Sentence-level Relation Extraction (SentRE) tasks.
Approach:	They propose an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts) Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios.
Outcome:	The proposed model surpasses TAG by 10.03% and 9.03% on the dev and test set.

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Matthias Cetto, Christina Niklaus, André Freitas, Siegfried Handschuh

Large Language Models for Scientific Information Extraction: An Empirical Study for Virology (2024.findings-eacl)

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 .

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Mahsa Shamsabadi, Jennifer D’Souza, Sören Auer

Spatial Dependency Parsing for Semi-Structured Document Information Extraction (2021.findings-acl)

Challenge:	Scholarly communication in the digital age is facing significant challenges due to the overwhelming volume of publications.
Approach:	They propose to use Wikipedia infoboxes and structured Amazon product descriptions to create structured scholarly contribution summaries using text generation capabilities of LLMs.
Outcome:	The proposed model can be applied to complex IE tasks within terse domains like Science with 1000x fewer parameters than the state-of-the-art GPT-davinci.

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Wonseok Hwang, Jinyeong Yim, Seunghyun Park, Sohee Yang, Minjoon Seo

Syntactic Text Processing

Challenge:	Information extraction (IE) for semistructured document images is often considered as a sequence tagging problem . however, such a setup cannot handle complex spatial relationships and is not suitable for highly structured information.
Approach:	They propose a spatial dependency parsing problem that models complex spatial relationships . they evaluate it on receipts, name cards, forms, and invoices and compare it to other methods .
Outcome:	The proposed parser achieves similar or better performance on various kinds of documents compared to baselines including BERT-based IOB taggger.

LED: A Dataset for Life Event Extraction from Dialogs (2023.findings-eacl)

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Yi-Pei Chen, An-Zi Yen, Hen-Hsen Huang, Hideki Nakayama, Hsin-Hsi Chen

RESIN-EDITOR: A Schema-guided Hierarchical Event Graph Visualizer and Editor (2023.emnlp-demo)

Challenge:	Existing methods for extracting life events from conversations are limited.
Approach:	They propose a dataset containing fine-grained life event annotations on conversational data.
Outcome:	The proposed dataset combines three information extraction frameworks to extract life events from conversations.

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Khanh Duy Nguyen, Zixuan Zhang, Reece Suchocki, Sha Li, Martha Palmer, Susan Windisch Brown, Jiawei Han, Heng Ji

Sample Design Engineering: An Empirical Study on Designing Better Fine-Tuning Samples for Information Extraction with LLMs (2024.emnlp-industry)

Challenge:	Existing IE tools for atomic events are limited when applied to such complex events.
Approach:	They propose to use event schemas to guide the organization of complex events and to edit hierarchical graphs.
Outcome:	The proposed tool outperforms existing IE visualization tools in both IE result analysis and general model improvements.

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Biyang Guo, He Wang, Wenyilin Xiao, Hong Chen, ZhuXin Lee, Songqiao Han, Hailiang Huang

Full-Stack Information Extraction System for Cybersecurity Intelligence (2022.emnlp-industry)

Challenge:	Prompt Engineering (PE) is renowned for improving IE performance through prompt modifications, but the realm of sample design for downstream fine-tuning remains unexplored.
Approach:	They propose a methodical approach to enhancing LLMs’ post-tuning performance by refining input, output, and reasoning designs.
Outcome:	The proposed approach outperforms heuristic design strategies on three complex IE tasks with four additional LLMs.

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Youngja Park, Taesung Lee

A Comprehensive Evaluation of Biomedical Entity-centric Search (2022.emnlp-industry)

Challenge:	a rapid increase in cyberattacks and attacks pose enormous challenges to security analysts.
Approach:	They propose a full-stack information extraction system for the cybersecurity domain that extracts 26 entity types, 20 fixed relations and the temporal information of the relations.
Outcome:	The proposed system can extract 26 entity types, 20 fixed rela and temporal information of relations.

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Elena Tutubalina, Zulfat Miftahutdinov, Vladimir Muravlev, Anastasia Shneyderman

Information Retrieval

Challenge:	a novel algorithm for information retrieval from biomedical abstracts is used to identify entities.
Approach:	They perform a fine-grained evaluation of a BERT-based biomedical search engine . they use manually annotated PubMed abstracts and off-she-shelf Elasticsearch .
Outcome:	The proposed system performs better for disease and gene search queries than other systems.

Neural Open Information Extraction (P18-2)

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Lei Cui, Furu Wei, Ming Zhou

Getting BART to Ride the Idiomatic Train: Learning to Represent Idiomatic Expressions (2022.tacl-1)

Challenge:	Existing Open IE systems are built on hand-crafted patterns from syntactic parsing, yet they face errors in propagation and compounding at each stage.
Approach:	They propose a neural Open IE approach with an encoder-decoder framework . they propose to learn highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open ie system.
Outcome:	The proposed approach outperforms baseline methods while maintaining comparable computational efficiency.

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Ziheng Zeng, Suma Bhat

Cuckoo: An IE Free Rider Hatched by Massive Nutrition in LLM’s Nest (2025.acl-long)

Challenge:	Prior work has identified deficiencies in their contextualized representation stemming from the underlying compositional paradigm of representation.
Approach:	They propose to use an adapter as a lightweight non-compositional language expert trained on idiomatic sentences to build idiomity into BART.
Outcome:	The proposed approach improves idiomaticity over baselines and up to 25% higher sequence accuracy on idiom processing tasks.

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Letian Peng, Zilong Wang, Feng Yao, Jingbo Shang

Neural Language Modeling for Contextualized Temporal Graph Generation (2021.naacl-main)

Challenge:	Massive high-quality data, both pre-training raw texts and post-training annotations, have been carefully prepared to incubate advanced large language models (LLMs).
Approach:	They propose to reframe next-token prediction into extraction for tokens already present in the context of LLMs by reframing next-tongue prediction into IE models.
Outcome:	The proposed model learns 102.6M extractive data converted from pre-training and post-training data with better performance than existing pre-trained IE models.

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Aman Madaan, Yiming Yang

Supervising Unsupervised Open Information Extraction Models (D19-1)

Challenge:	Existing methods for temporal reasoning have been used for a number of applications, but their potential for tempor reasoning over event graphs has not been explored.
Approach:	They propose to use large-scale pre-trained language models to generate an event-level temporal graph from a document using existing IE/NLP tools.
Outcome:	The proposed method outperforms the closest existing method on several metrics on a hand-labeled, out-of-domain corpus.

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Arpita Roy, Youngja Park, Taesung Lee, Shimei Pan

Syntactic and Semantic-driven Learning for Open Information Extraction (2020.findings-emnlp)

Challenge:	Existing supervised methods that use labeled training data are expensive and difficult to adapt to new domains.
Approach:	They propose a supervised open information extraction framework that leverages unsupervised Open IE systems and labeled data to improve system performance.
Outcome:	The proposed method outperforms existing supervised and unsupervised models by a significant margin.

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Jialong Tang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Xinyan Xiao, Hua Wu

Supervised Open Information Extraction (N18-1)

Challenge:	Experimental results show that our approach significantly outperforms the supervised counterparts, and can even achieve competitive performance to supervised state-of-the-art (SoA) model.
Approach:	They propose a syntactic and semantic-driven learning approach that can learn open IE models without human-labelled data by leveraging syntakic and semantic knowledge as noisier, higher-level supervision.
Outcome:	The proposed approach outperforms supervised counterparts and can achieve competitive performance to supervised state-of-the-art models.

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Gabriel Stanovsky, Julian Michael, Luke Zettlemoyer, Ido Dagan

GraphIE: A Graph-Based Framework for Information Extraction (N19-1)

Challenge:	Existing methods for Open Information Extraction (Open IE) use semisupervised approaches or rule-based algorithms.
Approach:	They propose a supervised approach to Open Information Extraction (Open IE) they build on recent deep Semantic Role Labeling models to extract Open IE tuples .
Outcome:	The proposed model outperforms state-of-the-art Open IE systems on benchmark datasets.

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Yujie Qian, Enrico Santus, Zhijing Jin, Jiang Guo, Regina Barzilay

Crowdsourcing Question-Answer Meaning Representations (N18-2)

Challenge:	Most modern Information Extraction (IE) systems are implemented as sequential taggers and model local dependencies.
Approach:	They propose a framework that operates over a graph representing a broad set of dependencies between textual units.
Outcome:	The proposed framework outperforms the state-of-the-art sequence tagging model on three different tasks.

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Julian Michael, Gabriel Stanovsky, Luheng He, Ido Dagan, Luke Zettlemoyer

Representation Learning

Challenge:	Existing datasets for predicate-argument relationships are lacking highly skilled and trained annotators.
Approach:	They propose a crowdsourcing scheme to generate question-answer pairs that represent predicate-argument relationships in sentences as a set of question-announcer pairs.
Outcome:	The proposed model covers the vast majority of predicate-argument relationships in existing datasets along with many previously under-resourced ones, including implicit arguments and relations.

Multiˆ2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT (2020.findings-emnlp)

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Youngbin Ro, Yukyung Lee, Pilsung Kang

Extraction of Information Provision Activity Requirements from EU Acquis (2025.emnlp-industry)

Challenge:	Existing open IE systems were based on handcrafted features or fine-grained rules.
Approach:	They propose a multi-head argument extraction method using multi-lingual BERT . they use a query, key, and value setting inspired by the Multimodal Transformer .
Outcome:	The proposed method outperforms existing sequence-labeling systems on two benchmark datasets and on two languages without training data.

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Jakub Piskorski, Dominik Skotarczak

TRUE-UIE: Two Universal Relations Unify Information Extraction Tasks (2024.naacl-long)

Challenge:	Using knowledge-, classical ML-, transformer-, and generative AI-based approaches, we extract structured information from EU acquis documents.
Approach:	They propose a task of Information Provision Activity Requirement Extraction to identify text fragments that introduce an obligation to provide information and the extraction of structured information about the key entities involved.
Outcome:	The proposed task is based on knowledge-, classical ML-, transformer-, and generative AI-based approaches.

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Yucheng Wang, Bowen Yu, Yilin Liu, Shudong Lu

MPL: Multiple Programming Languages with Large Language Models for Information Extraction (2025.findings-acl)

Challenge:	Information extraction (IE) tasks have a variety of schemas and objectives that differ across tasks.
Approach:	They propose a paradigm where all IE tasks are aligned to learn the same goals . they use two universal relations to extract mention spans and type recognition .
Outcome:	The proposed model achieves state-of-the-art on established benchmarks spanning 16 datasets, spanning 7 diverse IE tasks.

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Bo Li, Gexiang Fang, Wei Ye, Zhenghua Xu, Jinglei Zhang, Hao Cheng, Shikun Zhang

Enhancing Multi-Document Summarization with Cross-Document Graph-based Information Extraction (2023.eacl-main)

Challenge:	Existing research focuses on Python for code-style simulation, overlooking the potential of other widely-used PLs during the supervised fine-tuning phase.
Approach:	They propose a framework that incorporates programming languages into IE tasks . they introduce function-prompt with virtual running to simulate code-style inputs .
Outcome:	The proposed framework exploits the potential of different programming languages during the supervised fine-tuning phase.

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Zixuan Zhang, Heba Elfardy, Markus Dreyer, Kevin Small, Heng Ji, Mohit Bansal

Integrating Structural Semantic Knowledge for Enhanced Information Extraction Pre-training (2024.emnlp-main)

Challenge:	Information extraction (IE) and summarization (summarization) are closely related, but both aims to abstract the most salient information into a generated text summary.
Approach:	They propose to use structured IE graphs to enhance the abstractive summarization task by using cross-document IE output to incorporate an alignment loss between IE nodes and their text spans to reduce inconsistencies.
Outcome:	The proposed model can generate summaries that are more factual while not losing abstractiveness.

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Xiaoyang Yi, Yuru Bao, Jian Zhang, Yifang Qin, Faxin Lin

A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder (2024.lrec-main)

Challenge:	Existing pre-training methods focus on exploiting textual knowledge, which limits scalability and versatility of resulting models.
Approach:	They propose a pre-training framework that integrates structural semantic knowledge via contrastive learning.
Outcome:	The proposed framework outperforms state-of-the-art pre-training methods across multiple tasks.

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Kedi Chen, Jie Zhou, Qin Chen, Shunyu Liu, Liang He

In Layman’s Terms: Semi-Open Relation Extraction from Scientific Texts (2020.acl-main)

Challenge:	Existing methods for information extraction (IE) focus on training task-specific models, while common knowledge among different IE tasks is not explicitly modeled.
Approach:	They propose a regularization-based transfer learning method for IE via an instructed graph decoder which decodes various complex structures into a graph uniformly based on corresponding instructions.
Outcome:	The proposed method can learn common knowledge from existing datasets and transfer it to a new dataset with new labels.

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Ruben Kruiper, Julian Vincent, Jessica Chen-Burger, Marc Desmulliez, Ioannis Konstas

Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction (2021.emnlp-main)

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.

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Mahsa Yarmohammadi, Shijie Wu, Marc Marone, Haoran Xu, Seth Ebner, Guanghui Qin, Yunmo Chen, Jialiang Guo, Craig Harman, Kenton Murray, Aaron Steven White, Mark Dredze, Benjamin Van Durme

Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models (2024.acl-long)

Challenge:	Zero-shot cross-lingual information extraction (IE) is a technique for training data in a source language but not in .
Approach:	They explore techniques including data projection and self-training to improve zero-shot cross-lingual information extraction (IE) IE is a construction of an IE model for some target language given existing annotations exclusively in English.
Outcome:	The proposed techniques show that they perform better than any single strategy.

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Qingkai Min, Qipeng Guo, Xiangkun Hu, Songfang Huang, Zheng Zhang, Yue Zhang

Explaining Relationships Between Scientific Documents (2021.acl-long)

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.

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Kelvin Luu, Xinyi Wu, Rik Koncel-Kedziorski, Kyle Lo, Isabel Cachola, Noah A. Smith

Explainability & Interpretability in NLP

Challenge:	Existing approaches to explain relationships between scientific documents using natural language text can be useful for research efficiency.
Approach:	They propose a task of explaining relationships between scientific documents using natural language text.
Outcome:	The proposed models can be automated and humanely evaluated.

Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution (2021.findings-acl)

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Severine Verlinden, Klim Zaporojets, Johannes Deleu, Thomas Demeester, Chris Develder

Text-to-Table: A New Way of Information Extraction (2022.acl-long)

Challenge:	Using unsupervised entity linking, we solve named entity recognition, coreference resolution and relation extraction tasks together.
Approach:	They propose to use a knowledge base to inject information into a joint IE model by using unsupervised entity linking.
Outcome:	The proposed model improves on two datasets with 5% F1 score.

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Xueqing Wu, Jiacheng Zhang, Hang Li

Massively Multilingual Instruction-Following Information Extraction (2025.findings-acl)

Challenge:	Existing methods for information extraction are not well understood . text-to-table is a problem that aims to extract information from text data .
Approach:	They propose a new problem setting of information extraction, called text-to-table . they formalize text- to-table as a sequence-tosequence problem .
Outcome:	The proposed method outperforms existing methods on text-to-table tasks.

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Thang Le, Huy Huu Nguyen, Anh Tuan Luu, Thien Huu Nguyen

Open Information Extraction from Conjunctive Sentences (C18-1)

Challenge:	Past literature on information extraction (IE) has focused on a few high-resource languages, hindering their applications on multilingual corpora.
Approach:	They propose a collection of data that unifies and standardizes instruction-following multilingual IE and introduce a structure-aware metric that captures partially matched spans.
Outcome:	The proposed framework standardizes and unifies 215 manually annotated datasets, covering 96 typologically diverse languages from 18 language families.

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Swarnadeep Saha, null Mausam

Graphene: Semantically-Linked Propositions in Open Information Extraction (C18-1)

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.

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Matthias Cetto, Christina Niklaus, André Freitas, Siegfried Handschuh

Retrieval Augmented Instruction Tuning for Open NER with Large Language Models (2025.coling-main)

Challenge:	Existing Open IE systems focus on identifying and extracting relations of interest, but this manual labor scales linearly with the number of target relations.
Approach:	They propose an Open Information Extraction approach that uses a two-layered transformation stage and rhetorical relation identification to transform sentences into syntactically sound sentences.
Outcome:	The proposed approach outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures.

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Tingyu Xie, Jian Zhang, Yan Zhang, Yuanyuan Liang, Qi Li, Hongwei Wang

INFORM : Information eNtropy based multi-step reasoning FOR large language Models (2023.emnlp-main)

Challenge:	Existing studies have focused on integrating large language models (LLMs) with information extraction (IE) however, the best approach to incorporate information with LLMs for IE remains an open question.
Approach:	They propose to use a Chinese IT dataset to perform RA-IT for IE . they use semantically similar examples from the training dataset as the context .
Outcome:	The proposed approach is evaluated in English and Chinese scenarios.

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Chuyue Zhou, Wangjie You, Juntao Li, Jing Ye, Kehai Chen, Min Zhang

UTC-IE: A Unified Token-pair Classification Architecture for Information Extraction (2023.acl-long)

Challenge:	Large language models (LLMs) have demonstrated exceptional performance with dedicated Chain-of-Thought (CoT) prompts.
Approach:	They propose a new method by introducing information entropy as a criteria on for CoT prompt selection.
Outcome:	The proposed model outperforms existing models on seven reasoning benchmarks using two language models.

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Hang Yan, Yu Sun, Xiaonan Li, Yunhua Zhou, Xuanjing Huang, Xipeng Qiu

Cross-lingual Structure Transfer for Zero-resource Event Extraction (2020.lrec-1)

Challenge:	Information Extraction (IE) tasks have been solved with different models because of their output structures.
Approach:	They propose a Unified Token-pair Classification architecture for Information Extraction that introduces Plusformer on top of the token-pear feature matrix.
Outcome:	The proposed approach outperforms task-specific and unified models on all tasks in 10 datasets and achieves better results on 2 joint IE datasets.

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Di Lu, Ananya Subburathinam, Heng Ji, Jonathan May, Shih-Fu Chang, Avi Sil, Clare Voss

Universal Information Extraction with Meta-Pretrained Self-Retrieval (2023.findings-acl)

Challenge:	Existing approaches for information extraction only use name tagging . Currently, most successful cross-lingual transfer learning methods are limited to sequence labeling .
Approach:	They propose a share-and-transfer framework to transfer graph structures across languages . they propose to convert sentences in any language to language-universal graph structures .
Outcome:	The proposed framework performs comparable to state-of-the-art models on three languages without annotations.

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Xin Cong, Bowen Yu, Mengcheng Fang, Tingwen Liu, Haiyang Yu, Zhongkai Hu, Fei Huang, Yongbin Li, Bin Wang

Few-TK: A Dataset for Few-shot Scientific Typed Keyphrase Recognition (2024.findings-naacl)

Challenge:	Existing methods for IE are task-specific, resulting in specialized and isolated approaches for different tasks.
Approach:	They propose a method to retrieve task-specific knowledge from pretrained language models to enhance universal IE by using a Meta-Pretraining Algorithm.
Outcome:	The proposed method achieves the new state-of-the-art on 4 IE tasks, 12 datasets under fully-supervised, low-resource and few-shot scenarios.

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Avishek Lahiri, Pratyay Sarkar, Medha Sen, Debarshi Kumar Sanyal, Imon Mukherjee

MyFixit: An Annotated Dataset, Annotation Tool, and Baseline Methods for Information Extraction from Repair Manuals (2020.lrec-1)

Challenge:	Named Entities are a common form of Information Extraction (IE) tasks for scientific texts.
Approach:	They propose a rechristening of Named Entities as Typed Keyphrases (TK) they advocate for exploring this task in the few-shot domain due to the scarcity of labeled scientific IE data.
Outcome:	The proposed dataset includes scientific Typed Keyphrase annotations on abstracts of 500 research papers.

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Nima Nabizadeh, Dorothea Kolossa, Martin Heckmann

ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images (2026.eacl-long)

Challenge:	Textual instructions are among the most widely used media for learning and teaching . however, to create autonomous systems, it is difficult to extract task knowledge from text .
Approach:	They propose methods that can extract information from repair manuals from a semi-structured dataset . they propose a bag-of-n-grams similarity method and deep-learning-based sequence labeling model .
Outcome:	The proposed methods can extract the needed tools and disassembled parts from repair manuals.

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Mathieu Sibue, Andrés Muñoz Garza, Samuel Mensah, Pranav Shetty, Zhiqiang Ma, Xiaomo Liu, Manuela Veloso

Learning to Update Knowledge Graphs by Reading News (D19-1)

Challenge:	Existing models for structured information extraction are limited by narrow entity ontologies, simple queries, or homogeneous document types.
Approach:	They propose a benchmark dataset for structured Information Extraction (IE) from document images . they analyze open and closed VLMs on this benchmark .
Outcome:	The proposed model can perform fine-grained structured extraction across document types and schemas.

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Jizhi Tang, Yansong Feng, Dongyan Zhao

Cost-effective End-to-end Information Extraction for Semi-structured Document Images (2021.emnlp-main)

Challenge:	Existing methods to update knowledge graphs rely on elaborately designed IE systems and domain-specific rules.
Approach:	They propose a novel neural network method to update knowledge graphs (KGs) they use a text-based attention mechanism to guide updating messages through KGs .
Outcome:	The proposed method can effectively broadcast news information to KG structures and perform necessary link-adding or link-deleting operations to ensure the KG up-to-date according to news snippets.

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Wonseok Hwang, Hyunji Lee, Jinyeong Yim, Geewook Kim, Minjoon Seo

No Context Needed: Contextual Quandary In Idiomatic Reasoning With Pre-Trained Language Models (2024.naacl-long)

Challenge:	a real-world information extraction system for semi-structured document images often involves a long pipeline of multiple modules, which can lead to unstable performance if not designed carefully.
Approach:	They propose to use a sequence generation task to build an end-to-end IE system . they propose to combine three manually engineered modules with one data-driven module .
Outcome:	The proposed system can be easily replaced and deployed in large-scale production.

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Kellen Cheng, Suma Bhat

Automatic Error Analysis for Document-level Information Extraction (2022.acl-long)

Challenge:	idiomatic expressions (IEs) are a non-compositional aspect of a text that makes it difficult for a model to comprehend . general purpose PTLMs are negatively affected by the context, as performance increases with its removal.
Approach:	They propose to use idiomatic expressions to infer additional meaning from IEs . they argue that only IE-aware models are suitable for idiom- matic reasoning tasks .
Outcome:	The proposed models can reason in the presence of idiomatic expressions, the authors show . they show that general purpose PTLMs are negatively affected by the context .

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Aliva Das, Xinya Du, Barry Wang, Kejian Shi, Jiayuan Gu, Thomas Porter, Claire Cardie

Weize Chen, Hao Zhu, Xu Han, Zhiyuan Liu, Maosong Sun

Challenge:	Document-level information extraction (IE) tasks have been revisited in earnest . evaluation of the approaches has been limited in a number of dimensions .
Approach:	They propose a transformation-based framework for automating error analysis in document-level event and (N-ary) relation extraction.
Outcome:	The proposed framework compares two state-of-the-art document-level template-filling approaches on datasets from three domains and four systems from the MUC-4 evaluation.

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CARE: Extracting Experimental Findings From Clinical Literature (2024.findings-naacl)

Challenge:	a conceptually simple and effective method to quantify the similarity between relations is presented . identifying relations is a crucial problem for several information extraction tasks.
Approach:	They propose a method to quantify the similarity between relations in knowledge bases . they use a neural network to parameterize conditional probability distributions over entity pairs .
Outcome:	The proposed method significantly correlates with human judgments, the authors show . it could be incorporated into negative sampling and softmax classification to alleviate these mistakes.

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Aakanksha Naik, Bailey Kuehl, Erin Bransom, Doug Downey, Tom Hope

Information Extraction from Legal Wills: How Well Does GPT-4 Do? (2023.findings-emnlp)

Challenge:	Existing annotation schemas and datasets fail to capture real-world complexity and nuance of experimental findings.
Approach:	They propose a new annotation schema capturing fine-grained findings as n-ary relations between entities and attributes.
Outcome:	The proposed schema captures fine-grained findings as n-ary relations between entities and attributes.

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Alice Kwak, Cheonkam Jeong, Gaetano Forte, Derek Bambauer, Clayton Morrison, Mihai Surdeanu

A Domain-Specific Curated Benchmark for Entity and Document-Level Relation Extraction (2026.findings-eacl)

Challenge:	Using information extraction from legal wills is an important application of artificial intelligence (AI)
Approach:	They propose a manually annotated dataset for Information Extraction (IE) from legal wills . they also use it to evaluate the performance of large language models (LLMs)
Outcome:	The proposed dataset can be used to evaluate large language models on IE from legal wills . it shows that the model performs reasonably well, but inconsistent outputs and overgeneralization are observed .

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Marco Martinelli, Stefano Marchesin, Vanessa Bonato, Giorgio Di Nunzio, Nicola Ferro, Ornella Irrera, Laura Menotti, Federica Vezzani, Gianmaria Silvello

CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction (2024.lrec-main)

Challenge:	Existing biomedical IE benchmarks are narrow in scope and rely heavily on distantly supervised annotations.
Approach:	They propose a benchmark for Information Extraction (IE) that annotates entities, concept-level links, and relations manually from PubMed abstracts.
Outcome:	The GutBrainIE benchmark is based on more than 1,600 PubMed abstracts, manually annotated by biomedical and terminological experts with fine-grained entities, concept-level links, and relations.

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Xiang Wei, Yufeng Chen, Ning Cheng, Xingyu Cui, Jinan Xu, Wenjuan Han

Joint Extraction of Entities, Relations, and Events via Modeling Inter-Instance and Inter-Label Dependencies (2022.naacl-main)

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.

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Minh Van Nguyen, Bonan Min, Franck Dernoncourt, Thien Nguyen

A Survey of Generative Information Extraction (2025.coling-main)

Challenge:	Existing models that perform information extraction tasks manually assume heuristic dependency between the task instances and mean-field factorization for the joint distribution of instance labels.
Approach:	They propose to induce a dependency graph among task instances to boost representation learning by estimating their joint distribution via Conditional Random Fields.
Outcome:	The proposed model outperforms previous models on multiple IE tasks across 5 datasets and 2 languages.

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Zikang Zhang, Wangjie You, Tianci Wu, Xinrui Wang, Juntao Li, Min Zhang

A Survey on Open Information Extraction (C18-1)

Challenge:	Information Extraction (IE) is a popular and fundamental task in natural language processing.
Approach:	They first review generative information extraction methods based on pre-trained language models and large language models focusing on their adaptation and generalization capabilities.
Outcome:	The proposed methods are based on pre-trained language models and large language models, and emphasize the importance of model collaboration.

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Christina Niklaus, Matthias Cetto, André Freitas, Siegfried Handschuh

Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again (2022.findings-emnlp)

Challenge:	Existing approaches to open information extraction (Open IE) focus on narrow, well-defined requests over a predefined set of target relations on small, homogeneous corpora.
Approach:	They propose to use unsupervised methods to extract all types of relations found in text . they propose to implement a system that can be automated to detect possible relations .
Outcome:	The proposed approaches have been compared with existing methods and are based on the results of a literature review.

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Bernal Jimenez Gutierrez, Nikolas McNeal, Clayton Washington, You Chen, Lang Li, Huan Sun, Yu Su

Transforming Complex Sentences into a Semantic Hierarchy (P19-1)

Challenge:	Large pre-trained language models (PLMs) such as GPT-3 have shown strong in-context learning capabilities, which are appealing for domains such as biomedicine that feature high and diverse demands of language technologies but also high data annotation costs.
Approach:	They propose to compare the few-shot performance of GPT-3 in-context learning with fine-tuning smaller (i.e., BERT-sized) PLMs on two representative biomedical information extraction tasks: named entity recognition and relation extraction.
Outcome:	The proposed model underperforms on two representative biomedical information extraction tasks.

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Christina Niklaus, Matthias Cetto, André Freitas, Siegfried Handschuh

Text Generation

Challenge:	Existing approaches for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences are lacking.
Approach:	They propose a method for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences.
Outcome:	The proposed approach outperforms state-of-the-art approaches in MT and information extraction tasks.

MMUIE: Massive Multi-Domain Universal Information Extraction for Long Documents (2026.findings-eacl)

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Shuyi Zhang, Zhenbin Chen, Shuting Li, Kewei Tu, Li Jing, Zixia Jia, Zilong Zheng

Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking (2025.emnlp-main)

Challenge:	Existing document-level information extraction systems operate at the sentence level or within narrow domains due to annotation constraints.
Approach:	They propose a large-scale universal dataset for multi-domain, document-level information extraction from long texts.
Outcome:	The proposed dataset integrates traditional knowledge bases with large language models to extract fine-grained entities, aliases, and relation triples across 34 domains.

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Tianle Gu, Zongqi Wang, Kexin Huang, Yuanqi Yao, Xiangliang Zhang, Yujiu Yang, Xiuying Chen

Translation and Fusion Improves Cross-lingual Information Extraction (2025.acl-long)

Challenge:	Existing methods to watermark low-entropy content are expensive and risky . IE reduces parameter size by 99% while achieving performance on par with state-of-the-art methods .
Approach:	They propose a logit-based watermarking paradigm that uses entropy-based features to predict whether the next token is high or low.
Outcome:	The proposed method reduces parameter size by 99% while achieving performance on par with state-of-the-art methods.

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Yang Chen, Vedaant Shah, Alan Ritter

An Empirical Study of Pre-trained Transformers for Arabic Information Extraction (2020.emnlp-main)

Challenge:	Large language models (LLMs) have shown significant progress in information extraction tasks due to lack of labeled data for fine-tuning and unlabeled text for pre-training.
Approach:	They propose a framework in which large language models are fine-tuned to use English translations of low-resource language data.
Outcome:	The proposed model improves cross-lingual transfer over the base model on 12 multilingual IE datasets spanning 50 languages.

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Wuwei Lan, Yang Chen, Wei Xu, Alan Ritter

ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly (2025.coling-main)

Challenge:	Multilingual pre-trained Transformers have been shown to enable effective cross-lingual zero-shot transfer, but their performance on Arabic information extraction tasks is not well studied.
Approach:	They pre-train a bilingual BERT that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning.
Outcome:	The pre-trained model significantly outperforms mBERT, XLM-RoBERTa, and AraBERT in both the supervised and zero-shot transfer settings.

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Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek, Benjamin Van Durme, Jordan Boyd-Graber

Unified Structure Generation for Universal Information Extraction (2022.acl-long)

Challenge:	Existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user’s needs.
Approach:	They propose a framework that uses human-in-the-loop refinement to adapt to changing user questions.
Outcome:	The proposed framework is domain-agnostic, responsive, efficient for helping users access useful information while quickly reorganizing information in response to evolving information needs.

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Yaojie Lu, Qing Liu, Dai Dai, Xinyan Xiao, Hongyu Lin, Xianpei Han, Le Sun, Hua Wu

ADELIE: Aligning Large Language Models on Information Extraction (2024.emnlp-main)

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.

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Yunjia Qi, Hao Peng, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li

WebIE: Faithful and Robust Information Extraction on the Web (2023.acl-long)

Challenge:	Large language models (LLMs) struggle to follow complex instructions of IE tasks due to not being aligned with humans.
Approach:	They propose an aligned large language moDEL that effectively solves various IE tasks including closed IE, open IE and on-demand IE.
Outcome:	The proposed model achieves state-of-the-art (SoTA) performance among open-source models.

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Chenxi Whitehouse, Clara Vania, Alham Fikri Aji, Christos Christodoulopoulos, Andrea Pierleoni

Robustness in NLP

Challenge:	Existing closed IE datasets are built using Wikipedia, but they have limitations when applied to web domains.
Approach:	They propose to annotate 25K triples from WebIE through crowdsourcing and introduce mWebIE, a translation of the annotated set in four other languages.
Outcome:	The proposed model trains on 1.6M sentences from the English Common Crawl corpus and includes negative examples to better reflect the data on the web.

Lifelong Event Detection with Knowledge Transfer (2021.emnlp-main)

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Pengfei Yu, Heng Ji, Prem Natarajan

Unsupervised Domain Adaptation for Joint Information Extraction (2022.findings-emnlp)

Challenge:	Traditional supervised Information Extraction (IE) methods can extract structured knowledge elements from unstructured data, but it is limited to a pre-defined target ontology.
Approach:	They propose a new lifelong event detection framework that is generalizable to other IE tasks and updates old knowledge with new event types’ mentions using a self-training loss.
Outcome:	The proposed framework outperforms baselines with a 5.1% gain in the F1 score and can boost the F2 score for over 30% on some new long-tail rare event types with few training instances.

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Nghia Ngo, Bonan Min, Thien Nguyen

Leveraging Structured Information for Explainable Multi-hop Question Answering and Reasoning (2023.findings-emnlp)

Challenge:	Current JIE methods focus on standard supervised learning setting where training and test data come from the same domain.
Approach:	They propose a method to induce domain-invariant representations for the tasks in JIE by a generalized version of domain-adversarial learning.
Outcome:	The proposed method improves out-of-domain performance for current pipeline approaches for all IE tasks.

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Ruosen Li, Xinya Du

Open Relation and Event Type Discovery with Type Abstraction (2022.emnlp-main)

Challenge:	Neural models, including large language models (LLMs), achieve superior performance on multi-hop question-answering tasks.
Approach:	They propose to use the chain-of-thought mechanism to generate both the reasoning chain and the answer.
Outcome:	Empirical results show that the proposed framework generates more faithful reasoning chains and significantly improves the QA performance on two benchmark datasets.

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Sha Li, Heng Ji, Jiawei Han

Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models (2025.acl-long)

Challenge:	Conventional "closed-world" information extraction methods rely on human ontologies to define scope for extraction.
Approach:	They propose a type abstraction approach where models are prompted to generalize and name the type . they use the similarity between inferred names to induce clusters .
Outcome:	The proposed method is complementary to token representations on relation extraction and event extraction datasets.

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Zhuojun Ding, Wei Wei, Chenghao Fan

Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference (2021.acl-long)

Challenge:	Existing approaches to align large language models with information extraction tasks are costly and not all training data benefits target domains.
Approach:	They propose a framework which dynamically Selects and Merges expert models at inference time and combines experts beneficial to target domains.
Outcome:	The proposed framework outperforms the unified model by 10% on multiple benchmarks.

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Tuan Lai, Heng Ji, ChengXiang Zhai, Quan Hung Tran

Knowledge Representation

Challenge:	Existing methods for information extraction from biomedical texts do not utilize external knowledge . despite the exponential growth of biomedically published articles, many existing methods fall behind .
Approach:	They propose a framework that utilizes external knowledge for entity and relation extraction . KECI uses an initial span graph to construct a knowledge graph containing relevant background knowledge .
Outcome:	The proposed framework achieves state-of-the-art results in two biomedical datasets . it achieves 4.59% and 4.91% improvement in F1 scores over the state- of-the art methods .

Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation (2021.acl-long)

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Zixuan Zhang, Nikolaus Parulian, Heng Ji, Ahmed Elsayed, Skatje Myers, Martha Palmer

Improving Open Information Extraction via Iterative Rank-Aware Learning (P19-1)

Challenge:	Compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements.
Approach:	They propose a novel biomedical Information Extraction model to extract scientific entities and events from English research papers using Abstract Meaning Representation (AMR) they construct a sentence-level knowledge graph from an external knowledge base and encode it to improve the model's understanding of complex scientific concepts.
Outcome:	The proposed model can extract scientific entities and events from scientific literature and improve its understanding of complex scientific concepts.

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Zhengbao Jiang, Pengcheng Yin, Graham Neubig

Mirror: A Universal Framework for Various Information Extraction Tasks (2023.emnlp-main)

Challenge:	Open information extraction (IE) is the task of extracting open-domain assertions from natural language sentences.
Approach:	They propose an additional binary classification loss to calibrate the extraction likelihood . they propose an iterative learning process where extractions generated by the open IE model are incrementally included as training samples to help the model learn from trial and error.
Outcome:	Experiments on open information extraction (IE) show that the extraction likelihood is not well calibrated when comparing quality of extracted assertions.

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Tong Zhu, Junfei Ren, Zijian Yu, Mengsong Wu, Guoliang Zhang, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Min Zhang

GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets (2023.findings-emnlp)

Challenge:	Recent studies often formulate IE tasks as a triplet extraction problem, but this paradigm does not support multi-span and n-ary extraction, leading to weak versatility.
Approach:	They propose a multi-span cyclic graph extraction problem and a non-autoregressive graph decoding algorithm to extract all spans in a single step.
Outcome:	The proposed model outperforms or reaches competitive performance with SOTA systems under few-shot and zero-shot settings and it is compatible with 57 datasets.

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Wolfgang Otto, Matthäus Zloch, Lu Gan, Saurav Karmakar, Stefan Dietze

Capability Decomposition for Unified Information Extraction via Hierarchical Mixture-of-Experts (2026.acl-long)

Challenge:	Named Entity Recognition (NER) models are crucial for academic writing . existing ground truth datasets do not treat fine-grained types like ML model and model architecture as separate entity types .
Approach:	They propose to annotate 100 full-text scientific publications and a first baseline model for 10 entity types centered around ML models and datasets.
Outcome:	The proposed model can be used to identify 10 entity types in scientific articles . existing models cannot recognize fine-grained models like ML models and model architecture .

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Jing Zhou, Peng Wang, Wenjun Ke, Jiajun Liu, Yao He

Quantifying Semantic Emergence in Language Models (2025.acl-long)

Challenge:	Existing methods for IE tasks suffer from inconsistent schema representation and implicitly intermediate reasoning . UC-UIE adopts a low-rank adapted hierarchical Mixture-of-Experts adapter for UIE tasks .
Approach:	They propose a framework that decomposes IE reasoning into three universal capabilities . UC-UIE adopts a low-rank Adaptation adapter to fine-tune LLMs for IE tasks .
Outcome:	The proposed framework outperforms full-parameter tuning methods with 1.24% trainable parameters and outperformed existing methods in generalization and interpretability.

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Hang Chen, Xinyu Yang, Jiaying Zhu, Wenya Wang

C-ICL: Contrastive In-context Learning for Information Extraction (2024.findings-emnlp)

Challenge:	Existing evaluation methods for large language models (LLMs) focus on coarse-grained text, not providing interpretations for the behavior of finergrained tokens.
Approach:	They propose a quantitative metric to measure large language models’ ability to extract semantics from input tokens.
Outcome:	The proposed metric compares the entropy reduction observed for a sequence of tokens and individual tokens.

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Ying Mo, Jiahao Liu, Jian Yang, Qifan Wang, Shun Zhang, Jingang Wang, Zhoujun Li

Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases (2024.findings-emnlp)

Challenge:	Existing methods for in-context learning with large language models focus on using correct or negative examples, ignoring the potential value of incorrect or negative samples.
Approach:	They propose a few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations.
Outcome:	The proposed technique outperforms previous few-shot in-context learning methods on a broad spectrum of related tasks.

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Wenhao Huang, Qianyu He, Zhixu Li, Jiaqing Liang, Yanghua Xiao

Learning Cross-Task Dependencies for Joint Extraction of Entities, Events, Event Arguments, and Relations (2022.emnlp-main)

Challenge:	Definition bias is a negative phenomenon that can mislead models.
Approach:	They propose a framework that measures definition bias, bias-aware fine-tuning and task-specific bias mitigation to mitigate definition bias in information extraction.
Outcome:	The proposed framework mitigates definition bias in information extraction tasks by measuring definition bias, bias-aware fine-tuning, and task-specific bias mitigation.

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Minh Van Nguyen, Bonan Min, Franck Dernoncourt, Thien Nguyen

CaRB: A Crowdsourced Benchmark for Open IE (D19-1)

Challenge:	Existing work on IE tasks that use two types of dependencies is not optimal . emr, event trigger detection, event argument extraction, and relation extraction are challenging .
Approach:	They propose a model that learns cross-task dependencies from data . they treat each task instance as a node in a dependency graph .
Outcome:	The proposed model outperforms strong baselines over four datasets with different languages.

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Sangnie Bhardwaj, Samarth Aggarwal, null Mausam

MEE: A Novel Multilingual Event Extraction Dataset (2022.emnlp-main)

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.

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Amir Pouran Ben Veyseh, Javid Ebrahimi, Franck Dernoncourt, Thien Nguyen

SciREX: A Challenge Dataset for Document-Level Information Extraction (2020.acl-main)

Challenge:	Existing methods for Event Extraction are limited for non-English languages . lack of high-quality multilingual datasets has been the main hindrance .
Approach:	They propose a multilingual event extraction dataset that provides annotation for more than 50K event mentions in 8 typologically different languages.
Outcome:	The proposed dataset provides annotation for more than 50K event mentions in 8 languages . the proposed dataset will be publicly available to foster future research .

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Sarthak Jain, Madeleine van Zuylen, Hannaneh Hajishirzi, Iz Beltagy

Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples! (2023.findings-emnlp)

Challenge:	Conventional datasets and methods for information extraction focus on within-sentence relations from general Newswire text.
Approach:	They propose a document-level IE dataset that integrates automatic and human annotations to annotate entities and document- level N-ary relation identification from scientific articles.
Outcome:	The proposed dataset extends state-of-the-art IE models to document-level IE.

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Yubo Ma, Yixin Cao, Yong Hong, Aixin Sun

“Covid vaccine is against Covid but Oxford vaccine is made at Oxford!” Semantic Interpretation of Proper Noun Compounds (2022.emnlp-main)

Challenge:	Large Language Models have made remarkable strides in various tasks, but whether they are competitive few-shot solvers remains an open question.
Approach:	They propose an adaptive filter-then-rerank paradigm to combine the strengths of LLMs and SLMs.
Outcome:	The proposed system achieves promising improvements on various IE tasks with acceptable time and cost investment.

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Keshav Kolluru, Gabriel Stanovsky, null Mausam

A Joint Neural Model for Information Extraction with Global Features (2020.acl-main)

Challenge:	Proper noun compounds are used in short-form domains but are largely ignored in information-seeking applications.
Approach:	They propose to annotate a manually annotated dataset of 22.5K proper noun compounds . they use supervised learning to generate interpretations from the compounds based on target knowledge .
Outcome:	The proposed dataset is 60 times larger than prior noun compound datasets and includes non-compositional examples.

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Ying Lin, Heng Ji, Fei Huang, Lingfei Wu

Mixture of LoRA Experts for Continual Information Extraction with LLMs (2025.findings-emnlp)

Challenge:	Existing joint neural models for Information Extraction use local task-specific classifiers to predict labels for individual instances.
Approach:	They propose a joint neural framework that extracts the optimal IE result as a graph from an input sentence.
Outcome:	The proposed model achieves new state-of-the-art on all subtasks and does not use any language-specific feature.

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Zitao Wang, Xinyi Wang, Wei Hu

CROSSAGENTIE: Cross-Type and Cross-Task Multi-Agent LLM Collaboration for Zero-Shot Information Extraction (2025.findings-acl)

Challenge:	Existing methods to continual information extraction are either task-specialized for a single task or suffer from catastrophic forgetting and insufficient knowledge transfer in continual IE.
Approach:	They propose a continual IE model that uses token-level mixture of LoRA experts with LLMs to extract emerging information across diverse IE tasks incessantly while avoiding forgetting.
Outcome:	The proposed model achieves state-of-the-art performance, effectively mitigating catastrophic forgetting and enhancing knowledge transfer in continual IE.

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Meng Lu, Yuzhang Xie, Zhenyu Bi, Shuxiang Cao, Xuan Wang

KnowCoder-X: Boosting Multilingual Information Extraction via Code (2025.findings-acl)

Challenge:	Large language models struggle with producing structured output while maintaining accuracy in zero-shot information extraction (IE)
Approach:	They propose a multi-agent framework that enhances zero-shot IE through multi-task collaboration.
Outcome:	CROSSAGENTIE outperforms state-of-the-art models in structured prediction . the framework significantly reduces inference cost while preserving accuracy .

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Yuxin Zuo, Wenxuan Jiang, Wenxuan Liu, Zixuan Li, Long Bai, Hanbin Wang, Yutao Zeng, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles (2023.acl-long)

Challenge:	Empirical evidence indicates that Large Language Models exhibit spontaneous cross-lingual alignment in Information Extraction (IE) however, a significant imbalance across languages persists, highlighting an underlying deficiency.
Approach:	They propose a code LLM with advanced cross-lingual and multilingual capabilities for universal IE that standardizes the representation of multilingual schemas using Python classes and conducts IE alignment instruction tuning on translated instance prediction task.
Outcome:	The proposed model surpasses ChatGPT and SoTA by 30.17% without training in 29 unseen languages and significantly improves cross-lingual IE transferability.

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Tanishq Gupta, Mohd Zaki, Devanshi Khatsuriya, Kausik Hira, N M Anoop Krishnan, null Mausam

Easy-to-Hard Learning for Information Extraction (2023.findings-acl)

Challenge:	Advanced knowledge of a science or engineering domain is typically found in domain-specific research papers.
Approach:	They propose a task of extracting compositions of materials from tables in materials science papers to facilitate research in this direction.
Outcome:	The proposed model outperforms previous table processing architectures by significant margins.

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Chang Gao, Wenxuan Zhang, Wai Lam, Lidong Bing

Uncovering Main Causalities for Long-tailed Information Extraction (2021.emnlp-main)

Challenge:	Existing models for information extraction (IE) use a one-stage learning strategy to extract the target structure from unstructured text data.
Approach:	They propose a unified easy-to-hard learning framework that mimics the human learning process by breaking down the learning process into multiple stages.
Outcome:	The proposed framework enables the model to acquire general IE task knowledge and improve its generalization ability on 13 out of 17 datasets.

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Guoshun Nan, Jiaqi Zeng, Rui Qiao, Zhijiang Guo, Wei Lu

Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field (2023.acl-long)

Challenge:	Information Extraction (IE) aims to extract structural information from unstructured texts.
Approach:	They propose a framework that aims to uncover the main causalities behind data in the view of causal inference.
Outcome:	The proposed framework can detect the main causalities behind data in the view of causal inference.

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Zixia Jia, Zhaohui Yan, Wenjuan Han, Zilong Zheng, Kewei Tu

Unified Representation for Non-compositional and Compositional Expressions (2023.findings-emnlp)

Challenge:	Existing approaches to joint Information Extraction (IE) neglect cross-instance or cross-task dependencies.
Approach:	They propose a joint IE framework that formulates joint 'conditional random field' to model cross-instance interactions . they incorporate a high-order neural decoder that is unfolded from a mean-field variational inference method .
Outcome:	The proposed approach improves on three IE tasks compared with baseline and prior work.

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Ziheng Zeng, Suma Bhat

DiZiNER: Disagreement-guided Instruction Refinement via Simulating Pilot Annotation for Zero-shot Named Entity Recognition (2026.acl-long)

Challenge:	Existing representations of non-compositional language are based on BART, but they are not as accurate as the state-of-the-art IE representation model, GIEA.
Approach:	They propose a language model, PIER+, that builds on BART and can generate semantically meaningful and contextually appropriate representations for English potentially idiomatic expressions.
Outcome:	The proposed model achieves 33% higher homogeneity score on embedding clustering than BART, while sacrificing performance on NLU tasks (+/- 1% accuracy)

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Siun Kim, Hyung-Jin Yoon

Information Extraction with Differentiable Beam Search on Graph RNNs (2024.lrec-main)

Challenge:	Large language models have advanced information extraction (IE) by enabling zero-shot and few-shot named entity recognition (NER) but their outputs still show persistent and systematic errors.
Approach:	They propose a framework that simulates the pilot annotation process and employs LLMs as both annotators and supervisors to refine model disagreements.
Outcome:	Using a pilot annotation process, the proposed framework outperforms its supervisor model on 18 benchmarks.

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Niama El Khbir, Nadi Tomeh, Thierry Charnois

Set Learning for Generative Information Extraction (2023.emnlp-main)

Challenge:	Existing approaches to information extraction suffer from exposure bias due to discrepancy between training and decoding.
Approach:	They propose to cast graph generation as auto-regressive sequence labeling and make it aware of decoding procedure by using differentiable beam search.
Outcome:	The proposed model outperforms its non-decoding-aware version on ACE05 and ConLL04 datasets.

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Jiangnan Li, Yice Zhang, Bin Liang, Kam-Fai Wong, Ruifeng Xu

On Construction of the ASR-oriented Indian English Pronunciation Dictionary (2020.lrec-1)

Challenge:	Recent efforts to employ sequence-to-sequence models to solve IE tasks have been focused on a single problem: structured objects are an unordered set, resulting in a potential order bias.
Approach:	They propose a sequence-to-sequence (Seq2Sequen) model that considers multiple permutations of structured objects to optimize set probability approximately.
Outcome:	The proposed model improves existing frameworks on vast tasks and datasets.

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Xian Huang, Xin Jin, Qike Li, Keliang Zhang

Speech & Audio in NLP

Challenge:	Indian English (IE) has distinctive characteristics, especially phonologically, from other varieties of English.
Approach:	They build a small IE spontaneous speech corpus and use a linguistically-guided IE pronunciation dictionary to apply it to IE.
Outcome:	The proposed system performs better on IE spontaneous speech data than the one trained with CMUdict.

Looking Right is Sometimes Right: Investigating the Capabilities of Decoder-only LLMs for Sequence Labeling (2024.findings-acl)

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David Dukić, Jan Snajder

Information Extraction from Visually Rich Documents using LLM-based Organization of Documents into Independent Textual Segments (2025.acl-long)

Challenge:	Pre-trained language models excel in natural language understanding (NLU) tasks.
Approach:	They propose to apply layer-dependent removal of the causal mask (CM) during LLM fine-tuning to improve SL performance.
Outcome:	The proposed approach outperforms state-of-the-art SL models on IE tasks, while achieving state- of-the art results is unclear.

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Aniket Bhattacharyya, Anurag Tripathi, Ujjal Das, Archan Karmakar, Amit Pathak, Maneesh Gupta

Probing Representations for Document-level Event Extraction (2023.findings-emnlp)

Challenge:	Specialized non-LLM NLP-based solutions lack reasoning and are not able to infer values not explicitly present in documents.
Approach:	They propose a novel LLM-based approach that organizes VRDs into localized semantic textual segments called semantic blocks.
Outcome:	The proposed approach outperforms the state-of-the-art on public VRD benchmarks by 1-3% in F1 scores and is resilient to document formats previously not encountered.

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Barry Wang, Xinya Du, Claire Cardie

CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors (2023.acl-long)

Challenge:	Document-level information extraction tasks require a more comprehensive understanding that often extends to the entire input document.
Approach:	They propose to use probing to analyze document-level information extraction representations by embedding probes into a standard dataset.
Outcome:	The proposed models improve argument detections but struggle with document length and cross-sentence discourse.

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Peng Li, Tianxiang Sun, Qiong Tang, Hang Yan, Yuanbin Wu, Xuanjing Huang, Xipeng Qiu

On Event Individuation for Document-Level Information Extraction (2023.findings-emnlp)

Challenge:	Large language models pre-trained on massive corpora have shown impressive few-shot learning ability on many NLP tasks.
Approach:	They propose to recast structured output in the form of code instead of natural language and use generative LLMs of code to perform IE tasks.
Outcome:	The proposed method outperforms fine-tuning moderate-size pre-trained models and prompting NL-LLMs under few-shot settings.

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William Gantt, Reno Kriz, Yunmo Chen, Siddharth Vashishtha, Aaron White

AniEE: A Dataset of Animal Experimental Literature for Event Extraction (2023.findings-emnlp)

Challenge:	a bomb exploded in a restaurant in Lima, and a second device was deactivated by the police .
Approach:	They argue that the task demands definitive answers to thorny questions of event individuation they argue that even human experts disagree on the task .
Outcome:	The proposed task demands definitive answers to thorny questions of event individuation . the proposed task also raises concerns about the usefulness of template filling metrics .

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Dohee Kim, Ra Yoo, Soyoung Yang, Hee Yang, Jaegul Choo

Recognizing Everything from All Modalities at Once: Grounded Multimodal Universal Information Extraction (2024.findings-acl)

Challenge:	Event extraction (EE) is a crucial information extraction task in biomedical domain . existing biomedically EE datasets focus on cell experiments or overall procedures .
Approach:	They propose an animal experiment customized entity and event scheme for event extraction . they create an expert-annotated high-quality dataset containing discontinuous entities and nested events .
Outcome:	The proposed dataset is based on the animal experiment stage and a NER and EE model.

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Meishan Zhang, Hao Fei, Bin Wang, Shengqiong Wu, Yixin Cao, Fei Li, Min Zhang

Adaptive Ordered Information Extraction with Deep Reinforcement Learning (2023.findings-acl)

Challenge:	Existing studies on IE tasks have focused on recognizing and analyzing cross-modal information . a multimodal large language model (MLLM) is developed to analyze IE across modalities .
Approach:	They propose a multimodal large language model (MLLM) capable of grounding information from all modalities.
Outcome:	The proposed framework provides a framework to analyze IE tasks over various modalities and their fine-grained groundings.

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Wenhao Huang, Jiaqing Liang, Zhixu Li, Yanghua Xiao, Chuanjun Ji

IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions (2023.emnlp-main)

Challenge:	Existing methods for information extraction follow a fixed extraction order for complex tasks with multiple elements to be extracted in one instance.
Approach:	They propose an adaptive ordered IE paradigm to find optimal element extraction order for different instances and a reinforcement learning framework to generate optimal order dynamically.
Outcome:	The proposed method beats existing methods and improves on several public datasets.

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Ziheng Zeng, Kellen Cheng, Srihari Nanniyur, Jianing Zhou, Suma Bhat

FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction (2023.acl-long)

Challenge:	Prior work on IE comprehension has focused on detecting idiomaticity, but this fails to account for IEs' non-compositionality.
Approach:	They construct a commonsense knowledge graph for figurative interpretations of IEs that can be used to convert PTLMs into knowledge models that encode and infer commonsensical knowledge related to IE use.
Outcome:	The proposed model can generalize to IEs unseen during training.

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Tianshuo Peng, Zuchao Li, Lefei Zhang, Bo Du, Hai Zhao

UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective (2023.acl-long)

Challenge:	Existing Universal Information Extraction models rely heavily on span boundaries in data during training, which does not reflect the reality of span annotation challenges.
Approach:	They propose a framework that uses fuzzy spans to model various IE tasks . they propose generative Universal Information Extraction (UIE) to unify various ie tasks based on fuzzy span boundaries .
Outcome:	The proposed framework improves on a series of main IE tasks with small amounts of data and training epochs.

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Yang Ping, JunYu Lu, Ruyi Gan, Junjie Wang, Yuxiang Zhang, Pingjian Zhang, Jiaxing Zhang

CoRec: An Easy Approach for Coordination Recognition (2023.emnlp-main)

Challenge:	Existing approaches for information extraction (IE) are limited by the number of subtasks and the isolation of the subtask.
Approach:	They propose a new paradigm for universal information extraction that is compatible with any schema format and applicable to a list of IE tasks.
Outcome:	The proposed framework outperforms generative universal IE models on 14 benchmarks with the supervised setting and the state-of-the-art performance in low-resource scenarios.

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Qing Wang, Haojie Jia, Wenfei Song, Qi Li

Syntactic Text Processing

Challenge:	Existing syntactic parsers are slow and suffer from errors, especially for long and complicated sentences.
Approach:	They propose a pipeline model COordination RECognizer with coordinator identifier and conjunct boundary detector.
Outcome:	The proposed model improves the yield of state-of-the-art Open IE models by reducing errors and slow processing time.

Guideline Learning for In-Context Information Extraction (2023.emnlp-main)

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Chaoxu Pang, Yixuan Cao, Qiang Ding, Ping Luo

Problem Solved? Information Extraction Design Space for Layout-Rich Documents using LLMs (2025.findings-emnlp)

Challenge:	Large language models can perform a task by conditioning on task instructions and a few input-output examples without optimizing any parameters.
Approach:	They propose a guideline learning framework for In-context IE which reflectively learns and follows guidelines.
Outcome:	The proposed framework improves the performance of in-context IE by synthesizing and following guidelines.

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Gaye Colakoglu, Gürkan Solmaz, Jonathan Fürst

Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering (2024.findings-emnlp)

Challenge:	a new open-source layout-aware IE test suite is available for download at https://github.com/gayecolakoglu/layIE-LLM.
Approach:	They propose an open-source layout-aware IE test suite that provides a layout-based IE pipeline.
Outcome:	The proposed method achieves 13.3–37.5 F1 points more than a baseline configuration using the same LLM.

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Zimu Wang, Lei Xia, Wei Xjtlu, Xinya Du

Natural Language Interfaces

Challenge:	Existing research on Event-Event Causal Relation Extraction (ECRE) has highlighted the lack of document-level modeling and causal hallucinations.
Approach:	They propose a Knowledge-guided binary Question Answering method with event structures for ECRE that utilizes cross-task knowledge in IE.
Outcome:	The proposed method achieves state-of-the-art on the MECI and MAVEN-ERE datasets.

PIVOINE: Instruction Tuning for Open-world Entity Profiling (2023.findings-emnlp)

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Keming Lu, Xiaoman Pan, Kaiqiang Song, Hongming Zhang, Dong Yu, Jianshu Chen

On the Use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction (2024.lrec-main)

Challenge:	Existing methods for information extraction focus on a closed-world setting, but PIVOINE is a promising solution to tackle the open-world problem of entity profiling.
Approach:	They propose to develop an LLM that performs Open-world Entity Profiling with instruction tuning to extract desirable entity profiles . they construct INSTRUCTOPENWIKI, a substantial instruction-tuning dataset for Open-World Entity Profiles .
Outcome:	The proposed model outperforms existing methods and ChatGPT-based baselines on unseen and out-of-ontology cases.

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Jianwei Wang, Tianyin Wang, Ziqian Zeng

GUIDEX: Guided Synthetic Data Generation for Zero-Shot Information Extraction (2025.findings-acl)

Challenge:	Existing zero-shot methods for information extraction use large amounts of gold standard data.
Approach:	They propose a framework to utilize silver data to enhance zero-shot classification methods . they propose to use off-the-shelf models of other NLP tasks to perform inference on test data .
Outcome:	The proposed framework outperforms baseline methods on TACRED and Wiki80 datasets by 5% and 6% on the zero-shot relation classification task and by 3% 7 % on Smile (Korean and Polish)

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Neil De La Fuente, Oscar Sainz, Iker García-Ferrero, Eneko Agirre

Towards Robust Universal Information Extraction: Dataset, Evaluation, and Solution (2025.acl-long)

Challenge:	Existing domain-specific IE systems require expert schema design, data annotation, and model training.
Approach:	They propose a method that automatically defines domain-specific schemas and infers guidelines and generates synthetically labeled instances.
Outcome:	The proposed method improves on seven zeroshot Named Entity Recognition benchmarks.

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Jizhao Zhu, Akang Shi, Zixuan Li, Long Bai, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

Robustness in NLP

Challenge:	Existing robust benchmark datasets generate only a limited range of perturbations for a single Information Extraction (UIE) task, which fails to evaluate the robustness of UIE models effectively.
Approach:	They propose a new benchmark dataset that utilizes Large Language Models to generate more diverse and realistic perturbations across different IE tasks.
Outcome:	The proposed model performs better with only 15% of the data and is more robust with other models.

Transformer-Based Temporal Information Extraction and Application: A Review (2025.emnlp-main)

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Xin Su, Phillip Howard, Steven Bethard

BCL: Bayesian In-Context Learning Framework for Information Extraction (2026.findings-acl)

Challenge:	Temporal information extraction (IE) aims to extract structured temporal information from unstructured text, thereby uncovering the implicit timelines within.
Approach:	They summarize and analyze the work using Transformers to highlight potential future directions.
Outcome:	The proposed method is applied across healthcare, newswire, and intelligence analysis domains.

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Haoliang Liu, Chengkun Cai, Xu Zhao, Han Zhu, Shizhou Huang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Zhang Huaping, Lei Li

Towards Fast and Accurate Modeling for Cross-Lingual Label Projection (2026.acl-long)

Challenge:	Existing information extraction (IE) tasks rely on in-context learning with large language models.
Approach:	They propose a Bayesian-based in-context learning framework that refines label representations across IE tasks using particle filtering and Bayes updates.
Outcome:	The proposed framework improves performance over existing methods (up to 30%) it underperforms one-shot prompting by a substantial margin on NER tasks and CodeIE fails on RE tasks with near-zero micro-F1.

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Thang Le, Huy Huu Nguyen, Anh Tuan Luu, Thamar Solorio, Thien Huu Nguyen

AnchorAlign: A Novel Anchor Alignment-enhanced Generative Method for Joint Named Entity Recognition and Relation Extraction (2026.findings-acl)

Challenge:	Existing methods for label projection are inaccurate or slow for large-scale use.
Approach:	They propose to synthesize alignment sequence pairs and fine-tune an encoder model with span alignment objective while controlling data influence during training.
Outcome:	The proposed method outperforms state-of-the-art methods while maintaining fast inference speed across 50+ languages.

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Xiaolong Weng, Yuanyun Zhou, Boyu Qiu, Zehua Wang, Ying Xiong, Buzhou Tang