RE

On the Effectiveness of the Pooling Methods for Biomedical Relation Extraction with Deep Learning (D19-62)

Challenge:	Automated Named Entity Recognition (NER) and Relation Extraction (RE) models are tailored to the polymer domain.
Approach:	They propose to automate the annotation process by providing a web-based interface where users can visualize, verify, and refine the extracted information before finalizing the annotations.
Outcome:	The proposed system streamlines the annotation process by providing a web-based interface where users can visualize, verify, and refine the extracted information before finalizing the annotations.

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Tuan Ngo Nguyen, Franck Dernoncourt, Thien Huu Nguyen

Clear Up Confusion: Advancing Cross-Domain Few-Shot Relation Extraction through Relation-Aware Prompt Learning (2024.naacl-short)

Challenge:	Existing models for relation extraction use different pooling mechanisms to perform pooling for RE.
Approach:	They conduct a comprehensive study to evaluate the effectiveness of different pooling mechanisms for deep learning in biomedical RE.
Outcome:	The proposed model outperforms the previous models on two biomedical datasets.

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Ge Bai, Chenji Lu, Daichi Guo, Shilong Li, Ying Liu, Zhang Zhang, Guanting Dong, Ruifang Liu, Sun Yong

What Do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification (2022.acl-srw)

Challenge:	Existing approaches to few-shot Relation Extraction (RE) are prone to confusion when applying knowledge to a target domain with entirely new types of relations.
Approach:	They propose a relation-aware prompt learning method with pre-training to clear confusion by decomposing relation types through an innovative label prompt.
Outcome:	The proposed method outperforms previous sota methods and yields better results on cross-domain few-shot RE tasks.

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Elisa Bassignana, Barbara Plank

From What to Why: Improving Relation Extraction with Rationale Graph (2021.findings-acl)

Challenge:	Existing RE surveys focus on modeling techniques, but there are few that are based on real-world scenarios.
Approach:	They propose to survey RE datasets and revisit the task definition and its adoption by the community.
Outcome:	The proposed approach improves the reliability of RE evaluations across multiple datasets and reveals significant discrepancies in annotations.

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Zhenyu Zhang, Bowen Yu, Xiaobo Shu, Xue Mengge, Tingwen Liu, Li Guo

DeepGeneMD: A Joint Deep Learning Model for Extracting Gene Mutation-Disease Knowledge from PubMed Literature (D19-57)

Challenge:	Existing neural relation extraction models are limited by entity type and textual context.
Approach:	They propose a novel RAtionale Graph to organize co-occurrence constraints among entity types, triggers and relations in a holistic graph view.
Outcome:	The proposed method outperforms baselines significantly and achieves state-of-the-art performance on document-level and sentence-level RE benchmarks.

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Feifan Liu, Xiaoyu Zheng, Bo Wang, Catarina Kiefe

Predicting Document Coverage for Relation Extraction (2022.tacl-1)

Challenge:	Identifying and understanding the pathogenesis of genetic diseases is an essential task.
Approach:	They propose a joint deep learning model for gene mutation-disease knowledge extraction that adapts the state-of-the-art hierarchical multi-task learning framework for joint inference on named entity recognition and relation extraction.
Outcome:	The proposed model achieves the average score of 0.45 on recognizing gene activities and disease entities and the average F1 score of 0.3 on extracting relations, ranking 1st in the AGAC RE task.

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Sneha Singhania, Simon Razniewski, Gerhard Weikum

ODDA: An OODA-Driven Diverse Data Augmentation Framework for Low-Resource Relation Extraction (2025.findings-acl)

Challenge:	Existing methods for predicting document coverage for relation extraction (RE) are limited in their predictive power.
Approach:	They propose a task of predicting the coverage of a text document for relation extraction . they analyze a dataset of 31,366 diverse documents for 520 entities .
Outcome:	The proposed model achieves an F1 score of up to 46% on two use cases.

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Yijie Zhong, Yunfan Gao, Xiaolian Zhang, Haofen Wang

Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019 (D19-57)

Challenge:	Existing methods for low-resource relation extraction (LRE) lack diversity, leading to suboptimal performance.
Approach:	They propose to use large language models to augment relation extraction models by observing the RE model's behavior and replacing schema constraints with attribute constraints.
Outcome:	Experiments on three widely-used benchmarks show that the proposed method outperforms state-of-the-art methods while maintaining enhanced model stability.

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Pankaj Gupta, Usama Yaseen, Hinrich Schütze

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

Challenge:	Named Entity Recognition (NER) and Relation Extraction (RE) are essential tools in distilling knowledge from biomedical literature.
Approach:	They propose to use Named Entities to perform nested entities extraction, Entity Normalization and Relation Extraction to generalize the approach to different languages.
Outcome:	The proposed approach can be generalized to different languages and showed it’s effectiveness for English and Spanish text.

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Lilong Xue, Dan Zhang, Yuxiao Dong, Jie Tang

An Improved Baseline for Sentence-level Relation Extraction (2022.aacl-short)

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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Wenxuan Zhou, Muhao Chen

Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion (2022.findings-acl)

Challenge:	Sentence-level relation extraction (RE) aims at identifying the relationship between two entities in a sentence.
Approach:	They propose to improve sentence-level relation extraction by adding entity representations with typed markers to the model.
Outcome:	The proposed model outperforms existing methods on entity representation and noisy labels on TACRED dataset.

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Yiqing Xie, Jiaming Shen, Sha Li, Yuning Mao, Jiawei Han

OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction (D19-3)

Challenge:	Document-level relation extraction (DocRE) aims to extract semantic relations among entity pairs in a document.
Approach:	They propose an evidence-enhanced framework that empowers document-level relation extraction (DocRE) Eider efficiently extracts evidence and effectively fuses extracted evidence in inference.
Outcome:	The proposed framework outperforms state-of-the-art methods on three benchmark datasets.

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Xu Han, Tianyu Gao, Yuan Yao, Deming Ye, Zhiyuan Liu, Maosong Sun

Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study (2022.findings-emnlp)

Challenge:	OpenNRE provides a framework to implement neural relation extraction (RE) . the toolkit provides various functional modules based on TensorFlow and PyTorch .
Approach:	OpenNRE is an open-source framework to implement neural relation extraction models. they also release an online system to meet real-time extraction without any training and deployment.
Outcome:	OpenNRE provides a framework to implement neural models for relation extraction (RE) the toolkit also includes an online system to meet real-time extraction without training and deployment .

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Xin Xu, Xiang Chen, Ningyu Zhang, Xin Xie, Xi Chen, Huajun Chen

DuRE: Dual Contrastive Self Training for Semi-Supervised Relation Extraction (2024.naacl-long)

Challenge:	Existing approaches to extract relational facts from text are limited in their ability to learn from limited labeled data.
Approach:	They propose to use prompt-based methods with few-shot labeled data to evaluate performance . data augmentation technologies and self-training are also proposed to generate more labeles in-domain data.
Outcome:	The proposed methods perform well in low-resource settings with 8 relation extraction datasets.

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Yuxi Feng, Laks Lakshmanan

SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction (2025.coling-main)

Challenge:	Existing document-level relation extraction methods require manual training and labeled data to obtain supervised learning.
Approach:	They propose a document-level relation extraction framework that integrates RE and text generation as a dual process.
Outcome:	The proposed framework significantly boosts recall and F1 score with comparable precision on two document-level RE tasks against several strong baselines.

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Yuxuan Feng, Qian Chen, Qianyou Wu, Xin Guo, Suge Wang

Simple Large-scale Relation Extraction from Unstructured Text (L18-1)

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 .

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Christos Christodoulopoulos, Arpit Mittal

AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction (2025.naacl-srw)

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.

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Peitao Han, Lis Pereira, Fei Cheng, Wan Jou She, Eiji Aramaki

Generating Diverse Training Samples for Relation Extraction with Large Language Models (2025.acl-long)

Challenge:	Existing in-context learning methods for relation extraction often overlook entity relationships . Existing methods for RE prioritize language similarity over structural similarity .
Approach:	They propose an AMR-enhanced retrieval-based ICL method for relation extraction . their method retrieves in-context examples based on semantic structure similarity .
Outcome:	The proposed method outperforms baselines on four English RE datasets and in the more demanding unsupervised setting.

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Zexuan Li, Hongliang Dai, Piji Li

Graph Based Network with Contextualized Representations of Turns in Dialogue (2021.emnlp-main)

Challenge:	Existing models for Relation Extraction (RE) have good results on many benchmarks, but data scarcity is a common problem.
Approach:	They propose to use Large Language Models to generate training data for Relation Extraction . they propose to make LLMs produce dissimilar samples by direct instruction .
Outcome:	The proposed approach improves the diversity of training samples generated with LLMs while maintaining correctness.

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Bongseok Lee, Yong Suk Choi

Structured Data in NLP

Challenge:	Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue.
Approach:	They propose a dialogue-based relation extraction model which is based on emotion recognition in conversations.
Outcome:	The proposed model outperforms the state-of-the-art models on most of the benchmark datasets.

READ: Improving Relation Extraction from an ADversarial Perspective (2024.findings-naacl)

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Dawei Li, William Hogan, Jingbo Shang

Robustness in NLP

Challenge:	Recent work in relation extraction (RE) has high generalization capability, but adversarial training methods rely on entities.
Approach:	They propose an adversarial training method specifically designed for relation extraction that introduces sequence- and token-level perturbations to the sample and uses a separate perturbation vocabulary to improve the search for entity and context perturbations.
Outcome:	The proposed method significantly improves accuracy and robustness in low-resource scenarios.

Towards Better Graph-based Cross-document Relation Extraction via Non-bridge Entity Enhancement and Prediction Debiasing (2024.findings-acl)

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Hao Yue, Shaopeng Lai, Chengyi Yang, Liang Zhang, Junfeng Yao, Jinsong Su

Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping (D19-1)

Challenge:	Existing studies on relation extraction ignore non-bridge entities, leading to bias during inference.
Approach:	They propose a graph-based cross-document Relation Extraction model with non-bridge entity enhancement and prediction debiasing that integrates non-cross entities with target entities and bridge entities.
Outcome:	The proposed model outperforms baseline models on open and closed datasets.

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Jian Ni, Radu Florian

Leveraging 2-hop Distant Supervision from Table Entity Pairs for Relation Extraction (D19-1)

Challenge:	Relation extraction (RE) is an important information extraction task that seeks to detect and classify semantic relationships between entities.
Approach:	They propose a bilingual word embedding mapping approach for cross-lingual RE model transfer . they use a small bilingual dictionary with only 1K word pairs to embed word pairs .
Outcome:	The proposed approach achieves very good performance on target and target languages . it uses bilingual word embedding mapping to transfer a source-language model .

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Xiang Deng, Huan Sun

XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)

Challenge:	Existing methods to construct noisy labeled data for relation extraction (RE) are expensive and lacks the labeling capability.
Approach:	They propose a 2-hop DS strategy to enhance distantly supervised relation extraction (RE) by combining sentences that mention entities that are linked to each other.
Outcome:	The proposed method outperforms baselines on a benchmark dataset by a substantial margin.

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Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li

KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction (2021.findings-acl)

Challenge:	Document AI parsing semi-structured image form is a key information extraction task.
Approach:	They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework.
Outcome:	The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings.

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Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang’, Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat

Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors (2023.findings-acl)

Challenge:	Existing methods for relation extraction (RE) use only expanded facts from the knowledge graph .
Approach:	They propose a method for relation extraction from a single sentence . they use a neural network to expand the context with additional facts from the KG .
Outcome:	The proposed method is more accurate than state-of-the-art methods on standard datasets.

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Kai Zhang, Bernal Jimenez Gutierrez, Yu Su

Disease Network Constructor: a Pathway Extraction and Visualization (2023.acl-demo)

Challenge:	Recent work has shown that fine-tuning large language models on large instruction-following datasets improves their performance on a wide range of NLP tasks, but they fail to outperform small LMs on relation extraction (RE), a fundamental information extraction task.
Approach:	They propose a framework that aligns RE with question answering (QA), a predominant task in instruction-tuning datasets.
Outcome:	The proposed framework outperforms small LLMs on relation extraction (RE), a fundamental information extraction task, by a large margin.

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Mohammad Golam Sohrab, Khoa Duong, Goran Topić, Masami Ikeda, Nozomi Nagano, Yayoi Natsume-Kitatani, Masakata Kuroda, Mari Itoh, Hiroya Takamura

A Variational Approach for Mitigating Entity Bias in Relation Extraction (2025.acl-short)

Challenge:	In the human body, various substances (entities) such as proteins and compounds interact and regulate each other, forming huge pathway networks.
Approach:	They present a system that extracts and visualizes a disease network derived through regulation events found in scientific articles on idiopathic pulmonary fibrosis.
Outcome:	The proposed system extracts and visualizes a disease network from biomedical articles on idiopathic pulmonary fibrosis (IPF) it includes two-dimensional (2D) and 3D visualizations of the constructed disease network.

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Samuel Mensah, Elena Kochkina, Jabez Magomere, Joy Prakash Sain, Simerjot Kaur, Charese Smiley

Thesis Proposal: Stability-Aware, Evidence-Grounded Knowledge Graph for Substance Use Disorders and Social Determinants of Health (2026.eacl-srw)

Challenge:	Relation Extraction (RE) models often rely excessively on entities, resulting in poor generalization.
Approach:	They propose a Variational Information Bottleneck (VIB) framework to reduce entity bias in Relation Extraction (RE) . their method extracts relational information from unstructured data to improve generalization .
Outcome:	The proposed method achieves state-of-the-art on general and financial domain RE datasets, excelling in in-domain settings and out-of domain.

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Gautham Vijay Kumar

Structured Data in NLP

Challenge:	Existing methods for NER and RE annotation are costly and difficult to scale.
Approach:	They propose a semantic stability framework for constructing explainable KGs using NER and RE annotations.
Outcome:	The proposed framework supports multi-hop reasoning, triadic SUD–SDOH–SUD mediation patterns, and feedback loop analysis.

Improving Continual Relation Extraction by Distinguishing Analogous Semantics (2023.acl-long)

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Wenzheng Zhao, Yuanning Cui, Wei Hu

Learning Rich Representation of Keyphrases from Text (2022.findings-naacl)

Challenge:	Existing works store a small number of typical samples to re-train the model for alleviating forgetting.
Approach:	They propose a continual relation extraction model that uses memory-insensitive relation prototypes and memory augmentation to overcome the overfitting problem.
Outcome:	The proposed model outperforms existing models on analogous relations and overcomes overfitting problem.

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Mayank Kulkarni, Debanjan Mahata, Ravneet Arora, Rajarshi Bhowmik

Fine-grained Contrastive Learning for Relation Extraction (2022.emnlp-main)

Challenge:	Prior work has referred to extractive (part of document) or abstractive (not part of document).
Approach:	They propose to use a new pre-training objective to introduce keyphrases into transformer language models in discriminative and generative settings.
Outcome:	The proposed model improves performance in discriminative and generative settings and also improves on named entity recognition, question answering, relation extraction and abstractive summarization tasks.

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William Hogan, Jiacheng Li, Jingbo Shang

DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)

Challenge:	Existing methods assume all silver labels are accurate and treat them equally, but distant supervision is noisy–some silver labels more reliable than others.
Approach:	They propose a noise-aware contrastive learning approach that leverages fine-grained information about which silver labels are and are not noisy to improve the quality of learned relationship representations.
Outcome:	The proposed approach improves relation extraction performance over state-of-the-art methods on several RE benchmarks.

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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun

More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)

Challenge:	Existing relation extraction methods focus on extracting intra-sentence relations for single entities.
Approach:	They propose a relation extraction dataset from Wikipedia and Wikidata with three features . document-level relation extraction is a task to identify relational facts between entities .
Outcome:	The proposed dataset is the largest human-annotated dataset for document-level RE from plain text.

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Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun

Multi-facet Universal Schema (2021.eacl-main)

Challenge:	Existing methods for extracting relational facts from text have been successful . but with explosion of Web text, human knowledge is increasing drastically .
Approach:	They propose to improve relation extraction methods to extract relational facts from text . they analyze existing methods and show promising directions towards more powerful RE .
Outcome:	The proposed methods can extract relational facts from text, but they are still lacking in the current field.

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Rohan Paul, Haw-Shiuan Chang, Andrew McCallum

Representation Learning

Challenge:	Existing methods to solve relation extraction tasks violate USchema's assumption that sentence patterns that share the same entity pairs are similar to each other.
Approach:	They propose a multi-facet universal schema that embeds multiple sentence patterns as facets and encourages one to be close to that of another if they co-occur with the same entity pair.
Outcome:	The proposed model outperforms its single-facet embedding counterpart in relation extraction tasks.

FPC: Fine-tuning with Prompt Curriculum for Relation Extraction (2022.aacl-main)

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Sicheng Yang, Dandan Song

Semi-supervised Relation Extraction via Data Augmentation and Consistency-training (2023.eacl-main)

Challenge:	Existing methods for relation extraction ignore semantics of relation labels . prompt-based fine-tuning has been proposed for RE .
Approach:	They propose a method for relation extraction using prompt-based fine-tuning . they use auxiliary prompt-tuned learning task to make the model capture semantics of relation labels .
Outcome:	The proposed method outperforms existing methods on four widely used RE benchmarks under fully supervised and low-resource settings.

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Komal Teru

Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction (2021.findings-acl)

Challenge:	Obtaining high-quality human labelled data is an expensive and noisy process.
Approach:	They propose to leverage unlabelled data to improve the sample efficiency of the models.
Outcome:	The proposed methods can be used to extract the Cause-Effect relation between a given head entity and tail entity based on context in the input sentence.

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Tianyu Gao, Xu Han, Yuzhuo Bai, Keyue Qiu, Zhiyu Xie, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction (2022.emnlp-main)

Challenge:	Distantly supervised relation extraction (RE) has attracted much attention in the past few years . previous methods to evaluate models manually or directly on autolabeled data have produced inaccurate evaluations .
Approach:	They propose to use distant supervision to generate large-scale autolabeled data . they build manually-annotated test sets for two DS-RE datasets and evaluate models .
Outcome:	The proposed method produces 53% wrong labels at the entity pair level in the popular NYT10 dataset.

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Zhen Wan, Qianying Liu, Zhuoyuan Mao, Fei Cheng, Sadao Kurohashi, Jiwei Li

Enhancing Discriminative Representation in Similar Relation Clusters for Few-Shot Continual Relation Extraction (2025.naacl-long)

Challenge:	Existing RE models are incapable of handling implicit expressions and long-tail relation types due to language complexity and data sparsity.
Approach:	They propose a method to enhance relation extraction using k nearest neighbors (kNN-RE) kNN is a nearest-neighbor search tool that allows the model to consult training relations at test time .
Outcome:	The proposed model outperforms the best model to date on ACE05, SciERC, and Wiki80 datasets and outperformed the best on i2b2 and Wik80 dataset.

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Anh Duc Le, Nam Le Hai, Thanh Xuan Nguyen, Linh Ngo Van, Nguyen Thi Ngoc Diep, Sang Dinh, Thien Huu Nguyen

Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction (2021.acl-short)

Challenge:	Existing methods for relation extraction (RE) fail to address the problem of similar relations, which contributes to catastrophic forgetting.
Approach:	They propose a relation extraction method that utilizes relation descriptions and dynamic clustering to identify similar relations.
Outcome:	The proposed method mitigates catastrophic forgetting and outperforms state-of-the-art methods by a large margin.

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Quzhe Huang, Shengqi Zhu, Yansong Feng, Yuan Ye, Yuxuan Lai, Dongyan Zhao

GraphCache: Message Passing as Caching for Sentence-Level Relation Extraction (2022.findings-naacl)

Challenge:	Document-level relation extraction (RE) is more challenging than sentence RE as it often requires reasoning over multiple sentences.
Approach:	They propose a method to heuristically select evidence sentences for document-level relation extraction.
Outcome:	The proposed method can be easily combined with BiLSTM to achieve good performance on benchmark datasets even better than fancy graph neural network based methods.

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Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Bryan Hooi

Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction? (2023.acl-long)

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.

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Jiashu Xu, Mingyu Derek Ma, Muhao Chen

Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction (2020.acl-main)

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.

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Christoph Alt, Aleksandra Gabryszak, Leonhard Hennig

TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task (2020.acl-main)

Challenge:	Neural relation extraction models capture linguistic and semantic properties of the input, a recent study shows.
Approach:	They introduce 14 probing tasks targeting linguistic properties relevant to RE . they add contextualized word representations to enhance probing performance .
Outcome:	The proposed models achieve state-of-the-art on two datasets, TACRED and SemEval 2010 Task 8 . they show that the models capture linguistic and semantic properties relevant to the downstream task .

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Christoph Alt, Aleksandra Gabryszak, Leonhard Hennig

Document-level Relation Extraction with Dual-tier Heterogeneous Graph (2020.coling-main)

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.

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Zhenyu Zhang, Bowen Yu, Xiaobo Shu, Tingwen Liu, Hengzhu Tang, Wang Yubin, Li Guo

Relation Extraction using Explicit Context Conditioning (N19-1)

Challenge:	Existing methods focus on extracting relations from single sentence . document-level relation extraction requires a comprehension of the whole document .
Approach:	They propose a graph-based model with Dual-tier Heterogeneous Graph (DHG) for document-level relation extraction.
Outcome:	The proposed model achieves state-of-the-art performance on two widely used datasets.

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Gaurav Singh, Parminder Bhatia

Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking (2022.findings-acl)

Challenge:	Existing methods for relation extraction fail to capture complex and long dependencies . end-to-end models that learn both NER and RE can solve this problem .
Approach:	They propose to use second-order relations to compute relation scores for relation extraction (RE) . they propose to combine second- and first-order relation scores to obtain final relation scores .
Outcome:	The proposed method leads to state-of-the-art performance over two biomedical datasets.

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Yuanhe Tian, Yan Song, Fei Xia

Improving Relation Extraction with Relational Paraphrase Sentences (2020.coling-main)

Challenge:	Existing studies require modifications to existing baseline architectures to leverage syntactic information.
Approach:	They propose to leverage syntactic information to improve relation extraction by training a syntax-induced encoder on auto-parsed data through dependency masking.
Outcome:	The proposed approach outperforms baseline models and achieves state-of-the-art results on two English datasets.

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Junjie Yu, Tong Zhu, Wenliang Chen, Wei Zhang, Min Zhang

GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models (2024.naacl-long)

Challenge:	Existing annotated data is expensive and non-scalable, limiting performance of relation extraction models.
Approach:	They propose to enrich relation expressions by relational paraphrase sentences by annotating human-annotated data.
Outcome:	The proposed model improves performance even on a strong baseline.

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Pengcheng Jiang, Jiacheng Lin, Zifeng Wang, Jimeng Sun, Jiawei Han

RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information (D18-1)

Challenge:	Existing relation extraction methods rely on exact matching with human-annotated reference relations, while GRE methods produce diverse and semantically accurate relations.
Approach:	They propose a multi-dimensional assessment of relation extraction methods using human-annotated reference relations.
Outcome:	The proposed method is consistent with human preferences for RE quality.

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Shikhar Vashishth, Rishabh Joshi, Sai Suman Prayaga, Chiranjib Bhattacharyya, Partha Talukdar

Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction (2025.naacl-long)

Challenge:	Distantly-supervised Relation Extraction (RE) methods ignore readily available side information.
Approach:	They propose a distantly-supervised neural relation extraction method which uses additional side information from KBs to train an extractor.
Outcome:	The proposed method improves performance even when limited side information is available.

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William P Hogan, Jingbo Shang

SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction (2022.naacl-main)

Challenge:	Recent research has explored the potential of leveraging natural language inference (NLI) techniques to enhance relation extraction (RE).
Approach:	They propose a method that verbalizes relation classes into class-indicative hypotheses to align a traditionally multi-class classification task to one of textual entailment.
Outcome:	The proposed method improves relation extraction performance on BioRED and ReTACRED.

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Yuxin Xiao, Zecheng Zhang, Yuning Mao, Carl Yang, Jiawei Han

HistRED: A Historical Document-Level Relation Extraction Dataset (2023.acl-long)

Challenge:	Existing methods for relation extraction only implicitly learn to model relevant contexts and entity types while being trained for RE.
Approach:	They propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for RE.
Outcome:	The proposed method outperforms the runner-up method on three benchmarks by 5.04% . textual contexts and entity types are the major information sources that lead to the success of previous approaches.

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Soyoung Yang, Minseok Choi, Youngwoo Cho, Jaegul Choo

A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks (2024.lrec-main)

Challenge:	Relation extraction (RE) tasks are limited to sentencelevel RE, but are not feasible in real-world applications.
Approach:	They propose a bilingual relation extraction model that leverages both Korean and Hanja contexts to predict relations between entities.
Outcome:	The proposed model outperforms monolingual baselines on histRED . it supports various self-contained subtexts with different lengths .

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Yanis Labrak, Mickael Rouvier, Richard Dufour

Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning (2022.findings-naacl)

Challenge:	Large Language Models (LLMs) have enabled advances in the field of natural language processing . however, their application and potential are still underexplored .
Approach:	They evaluate four state-of-the-art instruction-tuned Large Language Models on 13 NLP tasks in English.
Outcome:	The evaluated models outperform state-of-the-art models on 13 real-world clinical and biomedical NLP tasks in English.

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Oscar Sainz, Itziar Gonzalez-Dios, Oier Lopez de Lacalle, Bonan Min, Eneko Agirre

Reasoning

Challenge:	Recent work shows that Relation Extraction tasks can be recasted as Textual Entailment tasks using verbalizations.
Approach:	They propose to recasted RE tasks as Textual Entailment tasks using verbalizations . they show that entailment reduces the need for manual annotation to 50% and 20% .
Outcome:	The proposed method reduces the need for manual annotation to 50% and 20% in event argument extraction tasks while achieving the same performance as with full training.

Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding (P18-1)

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Bingfeng Luo, Yansong Feng, Zheng Wang, Songfang Huang, Rui Yan, Dongyan Zhao

Challenge:	Experimental results show that the combination of regular expressions and NNs improves learning effectiveness when a small number of training examples are available.
Approach:	They propose to combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP by exploiting the rich expressiveness of REs at different levels within a NN.
Outcome:	The proposed approach significantly improves learning effectiveness when a small number of training examples are available.

LayoutPointer: A Spatial-Context Adaptive Pointer Network for Visual Information Extraction (2024.naacl-long)

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Huang Siyuan, Yongping Xiong, Wu Guibin

MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset (2023.acl-long)

Challenge:	Existing models inadequately utilize spatial information of entities, causing incorrectly linking spatially distant entities.
Approach:	They propose a Spatial-Context Adaptive Pointer Network to restore semantic order among entities . they propose XFUND-based tail-to-head pointer to restore the semantic order .
Outcome:	The proposed method outperforms existing state-of-the-art methods in F1 scores for RE tasks.

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Leonhard Hennig, Philippe Thomas, Sebastian Möller

GPT-RE: In-context Learning for Relation Extraction using Large Language Models (2023.emnlp-main)

Challenge:	Relation extraction (RE) is a fundamental task in information extraction, but its extension to multilingual settings is hindered by the lack of supervised resources comparable in size to large English datasets.
Approach:	They propose a dataset to analyze relation extraction (RE) in multilingual settings . they find machine translation is a viable strategy to transfer RE instances .
Outcome:	The proposed dataset covers 12 typologically diverse languages from 9 language families and is compared with existing datasets.

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Zhen Wan, Fei Cheng, Zhuoyuan Mao, Qianying Liu, Haiyue Song, Jiwei Li, Sadao Kurohashi

Regular Expression Guided Entity Mention Mining from Noisy Web Data (D18-1)

Challenge:	Existing approaches to in-context learning (ICL) are lacking in relation extraction (RE) . emergence of large language models (LLMs) such as GPT-3 represents a significant advancement in natural language processing.
Approach:	They propose to incorporate task-aware representations into demonstration retrieval and enrich the demonstrations with gold label-induced reasoning logic.
Outcome:	The proposed model achieves SOTA and competitive performances on the Semeval and SciERC datasets.

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Shanshan Zhang, Lihong He, Slobodan Vucetic, Eduard Dragut

Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis (2022.naacl-main)

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.

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Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, Bryan Hooi

Knowledge-Driven Cross-Document Relation Extraction (2024.findings-acl)

Challenge:	Existing studies rely on entity information for sentence-level relation extraction (RE) but this can leak superficial and spurious clues of relations.
Approach:	They propose to use entity mentions to extract relations from textual context . they use a causal graph to model dependencies between variables in RE models .
Outcome:	The proposed method yields significant gains on both effectiveness and generalization for RE.

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Monika Jain, Raghava Mutharaju, Kuldeep Singh, Ramakanth Kavuluru

Relation Extraction with Word Graphs from N-grams (2021.emnlp-main)

Challenge:	Existing approaches to extract relationships between entities are based on sentence-level tasks, but they do not consider domain knowledge, which are assumed to be known to the reader when documents are authored.
Approach:	They propose to embed domain knowledge of entities with input text for cross-document RE by embedding domain knowledge with the document.
Outcome:	The proposed framework offers interpretability by producing explanatory text for predicted relations between entities and improves performance over baseline methods.

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Han Qin, Yuanhe Tian, Yan Song

Paths to Relation Extraction through Semantic Structure (2021.findings-acl)

Challenge:	Recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to improve performance.
Approach:	They propose to use a graph convolutional network to build a context graph without dependency parsers.
Outcome:	The proposed approach improves neural RE methods without dependency parsers on English benchmark datasets.

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Jonathan Yellin, Omri Abend

Finding Influential Instances for Distantly Supervised Relation Extraction (2022.coling-1)

Challenge:	Syntactic and semantic structure directly reflect relations expressed by the text at hand and are therefore very useful for relation extraction (RE)
Approach:	They propose two methods for integrating broad-coverage semantic structure into supervised RE models by encoding semantic DAGs.
Outcome:	The proposed methods overshadow the use of syntactic integrations in RE . they reduce UCCA into a bilexical structure and encode semantic DAG structures .

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Zifeng Wang, Rui Wen, Xi Chen, Shao-Lun Huang, Ningyu Zhang, Yefeng Zheng

GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction (2021.findings-acl)

Challenge:	Distant supervision models suffer from high label noise and are not reliable for DS.
Approach:	They propose a model-agnostic instance sampling method for relation extraction (RE) by influence function, namely REIF.
Outcome:	The proposed method reduces the computational complexity from O(mn) to O(1), with analyzing its robustness on the selected sampling function.

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Junyi Bian, Li Huang, Xiaodi Huang, Hong Zhou, Shanfeng Zhu

REDFM: a Filtered and Multilingual Relation Extraction Dataset (2023.acl-long)

Challenge:	a funder name refers to an agency, organization, or program providing financial support for the research.
Approach:	They propose a funding sentence classifier and a relation extraction framework to extract grant information from scientific articles.
Outcome:	The proposed framework outperforms state-of-the-art BERT-based RE baselines against the PubMed Central and arXiv test sets.

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‪Pere-Lluís Huguet Cabot, Simone Tedeschi, Axel-Cyrille Ngonga Ngomo, Roberto Navigli

HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications (2021.findings-acl)

Challenge:	Existing Relation Extraction models rely on small datasets with low coverage of relation types . current systems rely only on small data sets with limited coverage of relationship types - especially when working with languages other than english.
Approach:	They propose to use an automatic annotated dataset to train relation extraction systems.
Outcome:	The proposed model can extract triplets in multiple languages from a human-revised dataset.

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Qiao Cheng, Juntao Liu, Xiaoye Qu, Jin Zhao, Jiaqing Liang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Yanghua Xiao

CrossRE: A Cross-Domain Dataset for Relation Extraction (2022.findings-emnlp)

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.

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Elisa Bassignana, Barbara Plank

M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models (2025.emnlp-main)

Challenge:	Relation Extraction (RE) evaluation is limited to in-domain setups . despite the drought of research on cross-domain RE, its practical importance remains .
Approach:	They propose a cross-domain benchmark for relation extraction which includes multi-label annotations and meta-data to include explanations and flags of difficult instances.
Outcome:	The proposed model includes explanations and flags of difficult instances.

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Zexuan Li, Hongliang Dai, Piji Li

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

Challenge:	Existing methods to extract training instances from unlabeled texts are expensive . sentences that contain the target relations in texts can be scarce and difficult to find .
Approach:	They propose a framework that can automatically extract training instances from unlabeled texts for RE.
Outcome:	The proposed method can extract training instances from unlabeled texts for RE.

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

A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling (2022.emnlp-main)

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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Ye Wang, Xinxin Liu, Wenxin Hu, Tao Zhang

CHisIEC: An Information Extraction Corpus for Ancient Chinese History (2024.lrec-main)

Challenge:	Existing document-level relation extraction methods focus on fully supervised scenarios but in real-world, incomplete labeling is a common problem because the number of entity pairs grows quadratically with the number.
Approach:	They propose a positive-unlabeled learning framework for document-level relation extraction (RE) that uses shift and squared ranking loss positive- unlabeles (SSR-PU) learning to solve incomplete labeling problem.
Outcome:	The proposed framework outperforms state-of-the-art methods under fully supervised and extremely unlabeled conditions and achieves 14 F1 points over the baseline with incomplete labeling.

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Xuemei Tang, Qi Su, Jun Wang, Zekun Deng

A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction (2024.findings-emnlp)

Challenge:	Historical and cultural heritage preservation is an important branch of digital humanities, where the rich tapestry of the past meets the cutting-edge tools of the digital age.
Approach:	They present a dataset to evaluate NER and RE tasks in ancient Chinese history . they use four distinct entity types and twelve relation types to identify them .
Outcome:	The "Chinese Historical Information Extraction Corpus" is a dataset from 13 dynasties spanning over 1830 years . the dataset encompasses four distinct entity types and twelve relation types .

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Yinghao Li, Rampi Ramprasad, Chao Zhang

Learning from Context or Names? An Empirical Study on Neural Relation Extraction (2020.emnlp-main)

Challenge:	Large language models have impressive abilities in generating unstructured natural language . performance inconsistent when tasked with producing text that adheres to structured formats .
Approach:	They propose a method to generate unstructured natural language using intermediate responses . they use the intermediate responses to organize the output into the desired structure .
Outcome:	The proposed method improves performance on NER and RE tasks with minimal effort.

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Hao Peng, Tianyu Gao, Xu Han, Yankai Lin, Peng Li, Zhiyuan Liu, Maosong Sun, Jie Zhou

Denoising Relation Extraction from Document-level Distant Supervision (2020.emnlp-main)

Challenge:	Existing datasets may leak shallow heuristics via entity mentions, thus contributing to the high performance on RE benchmarks.
Approach:	They propose an entity-masked contrastive framework for relation extraction to gain a deeper understanding on textual context and type information while avoiding rote memorization of entities.
Outcome:	The proposed framework improves the effectiveness and robustness of neural models in different RE scenarios.

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Chaojun Xiao, Yuan Yao, Ruobing Xie, Xu Han, Zhiyuan Liu, Maosong Sun, Fen Lin, Leyu Lin

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

Challenge:	Existing methods to generate auto-labeled sentences for relation extraction (RE) are difficult to extend to document-level relation extraction as noise from DS may be even multiplied in documents.
Approach:	They propose a pre-trained model which de-emphasizes noisy DS data via multiple pre-training tasks.
Outcome:	The proposed model can capture useful information from noisy data and achieve promising results on the large-scale DocRE benchmark.

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

Let’s Stop Incorrect Comparisons in End-to-end Relation Extraction! (2020.emnlp-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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Bruno Taillé, Vincent Guigue, Geoffrey Scoutheeten, Patrick Gallinari

Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data (2020.emnlp-main)

Challenge:	Existing literature on Relation Extraction (RE) uses multiple evaluation setups to compare performance.
Approach:	They propose to quantify the most common comparison mistake and evaluate it leads to overestimating the final RE performance by around 5% on ACE05.
Outcome:	The proposed meta-analysis overestimates the final RE performance by around 5% on ACE05.

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Shachar Rosenman, Alon Jacovi, Yoav Goldberg

Global-to-Local Neural Networks for Document-Level Relation Extraction (2020.emnlp-main)

Challenge:	Existing methods for relation extraction (RE) use shallow heuristics that do not generalize to challenge-set data.
Approach:	They propose to annotate a dataset to test whether relation extraction models are generalized to the challenge-set data.
Outcome:	The proposed model performs better on the challenge-set compared with the SOTA models on the same dataset.

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Difeng Wang, Wei Hu, Ermei Cao, Weijian Sun

KoCHET: A Korean Cultural Heritage Corpus for Entity-related Tasks (2022.coling-1)

Challenge:	Relation extraction (RE) aims to identify the semantic relations between named entities in text.
Approach:	They propose a novel relation extraction model that encodes document information in terms of entity global and local representations and context relation representations.
Outcome:	The proposed model achieves superior performance on two public datasets for document-level RE.

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Gyeongmin Kim, Jinsung Kim, Junyoung Son, Heuiseok Lim

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

Challenge:	Existing corpus for entity-related tasks is limited in terms of application and cannot be used for entity recognition.
Approach:	They propose to use a Korean cultural heritage corpus for the typical entity-related tasks named entity recognition (NER), relation extraction (RE) and entity typing (ET) .
Outcome:	The proposed corpus makes it more useful in terms of cultural heritage and provides practical insights in terms linguistic analysis.

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

Constructing Distributions of Variation in Referring Expression Type from Corpora for Model Evaluation (2022.lrec-1)

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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T. Mark Ellison, Fahime Same

Challenge:	referencing is a non-deterministic task, but the algorithms for RE generation are evaluated against corpora of written texts which only include one RE per reference.
Approach:	They propose a method for exploring variation in human RE choice on the basis of longitudinal corpora.
Outcome:	The proposed method shows agreement between the evaluations against human judgements and parallel evaluations.

Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction (N19-1)

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Peng Xu, Denilson Barbosa

How Knowledge Graph and Attention Help? A Qualitative Analysis into Bag-level Relation Extraction (2021.acl-long)

Challenge:	Knowledge Bases (KBs) require constant updating to reflect changes to the world they represent.
Approach:	They propose a framework that unifies learning of RE and KBE models . the framework is based on a relation extraction task that uses a KB relation to a phrase .
Outcome:	The proposed framework unifies learning of RE and KBE models, leading to significant improvements over the state-of-the-art RE framework.

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Zikun Hu, Yixin Cao, Lifu Huang, Tat-Seng Chua

CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild (2021.emnlp-main)

Challenge:	Knowledge Graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods.
Approach:	They propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE) they propose to incorporate entity prior to KG-enhanced attention to improve RE performance .
Outcome:	The proposed model achieves significant improvements on two real-world datasets compared with three state-of-the-art baselines.

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Yuan Yao, Jiaju Du, Yankai Lin, Peng Li, Zhiyuan Liu, Jie Zhou, Maosong Sun

Modeling Multi-Granularity Hierarchical Features for Relation Extraction (2022.naacl-main)

Challenge:	Existing relation extraction methods focus on extracting relational facts between entity pairs within single sentences or documents.
Approach:	They present a problem of cross-document relation extraction (CRE) using human annotations.
Outcome:	The proposed dataset is the first human-annotated cross-document RE dataset . it shows that it is challenging to existing RE methods including strong BERT-based models.

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Xinnian Liang, Shuangzhi Wu, Mu Li, Zhoujun Li

Intrinsic Task-based Evaluation for Referring Expression Generation (2024.acl-long)

Challenge:	Existing work on relation extraction focuses on constructing explicit structured features using knowledge graph and dependency tree.
Approach:	They propose a method to extract multi-granularity features based solely on the original input sentences.
Outcome:	The proposed method outperforms state-of-the-art models that even use external knowledge on three public benchmarks: SemEval 2010 Task 8, Tacred, and Tacred Revisited.

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Guanyi Chen, Fahime Same, Kees Van Deemter

Text Generation

Challenge:	Referring Expression Generation (REG) models generate referring expressions that refer to referents at different points in a discourse.
Approach:	They propose to use a purely ratings-based human evaluation to evaluate REG models by completing two meta-level tasks.
Outcome:	The proposed evaluation makes the models more reliable and discriminable, and improves the quality of the REs.

On the Relation between Syntactic Divergence and Zero-Shot Performance (2021.emnlp-main)

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Ofir Arviv, Dmitry Nikolaev, Taelin Karidi, Omri Abend

Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction (2022.naacl-main)

Challenge:	Recent advances in cross-lingual transfer methods have enabled significant advances in grammatical processing tasks.
Approach:	They examine the extent to which syntactic relations are preserved in translation and parsability in a zero-shot setting.
Outcome:	The proposed model is based on a translation task in English and a subset of a standard English RE benchmark translated to Russian and Korean.

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Liyan Xu, Jinho Choi

Towards Reverse Engineering of Language Models: A Survey (2025.findings-emnlp)

Challenge:	Existing work on document-level relation extraction has focused on end-to-end setting that extracts global entities and relations jointly.
Approach:	They propose to introduce a two-way interaction between COREF and RE that is specifically designed to leverage task characteristics, bridging decisions of two tasks for direct task interference.
Outcome:	The proposed model achieves the best performance by up to 2.3/5.1 F1 over the baseline.

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Xinpeng Ti, Wentao Ye, Zhifang Zhang, Junbo Zhao, Chang Yao, Lei Feng, Haobo Wang

Pre-training to Match for Unified Low-shot Relation Extraction (2022.acl-long)

Challenge:	Due to the vast amounts of data and computational resources required for model development, protecting the model’s parameters and training data has become an urgent and crucial concern.
Approach:	They define "reverse engineering" techniques as attacks on large language models and provide an in-depth analysis of them.
Outcome:	The proposed attacks are described as “reverse engineering” techniques on LMs and provide an introduction to existing protective strategies.

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Fangchao Liu, Hongyu Lin, Xianpei Han, Boxi Cao, Le Sun

Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction (D19-1)

Challenge:	Low-shot relation extraction (RE) aims to recognize novel relations with very few or even no samples.
Approach:	They propose a method that leverages triplet paraphrase to pre-train zero-shot label matching ability and uses meta-learning paradigm to learn few-shot instance summarizing ability.
Outcome:	The proposed method outperforms strong baselines and achieves the best performance on few-shot RE leaderboard.

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Qinyuan Ye, Liyuan Liu, Maosen Zhang, Xiang Ren

Easy First Relation Extraction with Information Redundancy (D19-1)

Challenge:	Existing studies on DS-based relation extraction (RE) methods focus on handling label noise, but other factors may have been overlooked.
Approach:	They propose a method to automatically adjust DS-RE models to a shifted label distribution problem . they find this problem exists in real-world DS datasets and can be overcome .
Outcome:	The proposed method achieves consistent performance gains on DS-trained models with an up to 23% relative F1 improvement, which verifies their assumptions.

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Shuai Ma, Gang Wang, Yansong Feng, Jinpeng Huai

Enhanced Entity Annotations for Multilingual Corpora (2022.lrec-1)

Challenge:	Existing relation extraction models make decisions globally using integer linear programming . Existing approaches require time and memory to encode redundant information for ILP .
Approach:	They propose an easy first approach for relation extraction with information redundancies embedded in local sentence extractors to resolve conflict decisions with domain and uniqueness constraints.
Outcome:	The proposed approach outperforms both ILP and neural network-based methods in relation extraction (RE) studies have shown that the proposed approach improves the efficiency and accuracy of RE models.

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Michael Strobl, Amine Trabelsi, Osmar Zaïane

Enhancing Dialogue-based Relation Extraction by Speaker and Trigger Words Prediction (2021.findings-acl)

Challenge:	Named Entity Recognition (NER) is a new language for natural language processing.
Approach:	They propose to improve the annotation quality of the English Wikipedia tool WEXEA . they propose to use a proven NER system to annotate entities in Wikipedia .
Outcome:	The proposed tool can be used to exhaustively annotate entities in Wikipedia articles.

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Tianyang Zhao, Zhao Yan, Yunbo Cao, Zhoujun Li

REAR: Reinforced Reasoning Optimization for Event Argument Extraction with Relation-Aware Support (2025.findings-emnlp)

Challenge:	Existing methods for identifying relations from dialogues do not fully consider the particularity of dialogues, making them difficult to understand the semantics between conversational arguments.
Approach:	They propose two tasks to enhance the extraction of dialogue-based relations . speaker prediction captures the characteristics of speakerrelated entities . the trigger words prediction provides supportive contexts for relations between arguments .
Outcome:	The proposed tasks improve the extraction of dialogue-based relations . speaker prediction captures the characteristics of speakerrelated entities . the trigger words prediction provides supportive contexts for relations between arguments .

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Jianwen Luo, Yu Hong, Shuai Yang, Jianmin Yao

Reasoning

Challenge:	Existing methods for EAE restrict integration of relation-level semantics, thereby overlooking the complementary cues from RE.
Approach:	They propose a Relation-aware EAE Reinforced optimization framework that integrates relation-level cues from RE into the Large Language Model (LLM)
Outcome:	The proposed framework surpasses existing decoder-only methods on the ACE-E, ACE+ and ERE benchmarks.

Combinatory Grammar Tells Underlying Relevance among Entities (2022.findings-emnlp)

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Yuanhe Tian, Yan Song

Enhancing SQL Table Acquisition with Reverse Engineering for Text-to-SQL (2025.findings-emnlp)

Challenge:	Existing approaches focus on dependencies among words while paying limited attention to other types of syntactic structure.
Approach:	They propose an alternative approach that takes advantage of combinatory categorial grammar to detect the relation between entities.
Outcome:	The proposed model performs state-of-the-art on two widely used English benchmark datasets.

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Shixin Liu, Haoyu Xu, Yu Hong

Multimodality

Challenge:	Text-to-SQL oriented table acquisition suffers from heterogeneous semantic gap.
Approach:	They propose a Reverse Engineering based table acquisition approach that reversely generates potentially-matched questions conditioned on table schemas instead of forward table search using queries.
Outcome:	The proposed approach achieves competitive performance on two benchmarks, including SpiderUnion and BirdUnion.

Document-level Entity-based Extraction as Template Generation (2021.emnlp-main)

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Kung-Hsiang Huang, Sam Tang, Nanyun Peng

Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED (2022.acl-long)

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.

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Quzhe Huang, Shibo Hao, Yuan Ye, Shengqi Zhu, Yansong Feng, Dongyan Zhao

Challenge:	Document-level relation extraction is a challenging task as it requires reasoning across multiple sentences.
Approach:	They propose to use a recommend-revise scheme to reduce the workload of annotators by providing them with candidate relation instances from distant supervision to supplement and remove relational facts.
Outcome:	The proposed dataset is the first large-scale and human-annotated dataset for relation extraction.

Silver Syntax Pre-training for Cross-Domain Relation Extraction (2023.findings-acl)

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Elisa Bassignana, Filip Ginter, Sampo Pyysalo, Rob van der Goot, Barbara Plank

Named Entity and Relation Extraction with Multi-Modal Retrieval (2022.findings-emnlp)

Challenge:	Relation Extraction (RE) is the task of extracting structured knowledge from unstructured text.
Approach:	They exploit the affinity between syntactic structure and semantic RE to obtain low-cost pre-training data.
Outcome:	The proposed model outperforms baseline models in five out of six cross-domain setups without additional annotated data.

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Xinyu Wang, Jiong Cai, Yong Jiang, Pengjun Xie, Kewei Tu, Wei Lu

Dialogue-Based Relation Extraction (2020.acl-main)

Challenge:	Existing approaches to name entity recognition and relation extraction are knowledge-based and may not be highly relevant.
Approach:	They propose a multi-modal named entity recognition framework that leverages image information to improve the performance of NER and relation extraction.
Outcome:	The proposed framework can achieve state-of-the-art on four multi-modal named entity recognition datasets and one multi-module relation extraction dataset.

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Dian Yu, Kai Sun, Claire Cardie, Dong Yu

LLM4RE: A Data-centric Feasibility Study for Relation Extraction (2025.coling-main)

Challenge:	Existing dialogue-based relation extraction tasks focus on texts from formal genres such as professionally written and edited news reports or well-edited websites.
Approach:	They propose to use DialogRE to study cross-sentence relation extraction . they propose to annotate 36 possible relation types between arguments in dialogues .
Outcome:	The proposed dataset supports the prediction of relation(s) between two arguments that appear in a dialogue.

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Anushka Swarup, Tianyu Pan, Ronald Wilson, Avanti Bhandarkar, Damon Woodard

Explore Unsupervised Structures in Pretrained Models for Relation Extraction (2022.findings-emnlp)

Challenge:	Relation Extraction (RE) is a critical step in information extraction due to its wide-scale applicability for downstream applications such as Knowledge Base creation and Question Answering (QA).
Approach:	They propose to conduct the first feasibility analysis to explore the viability of Large Language Models for RE by investigating their robustness to various RE scenarios stemming from data-specific characteristics.
Outcome:	The proposed models are robust to various RE scenarios stemming from data-specific characteristics, but their performance is not yet fully understood.

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Xi Yang, Tao Ji, Yuanbin Wu

Revisiting Large Language Models as Zero-shot Relation Extractors (2023.findings-emnlp)

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.

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Guozheng Li, Peng Wang, Wenjun Ke

LLM-OREF: An Open Relation Extraction Framework Based on Large Language Models (2025.emnlp-main)

Challenge:	Recent studies show that large language models (LLMs) transfer well to new tasks out-of-the-box . relationship extraction (RE) involves a certain degree of labeled or unlabeled data even under zero-shot setting.
Approach:	They propose a simple prompt recursively using LLMs to transform RE inputs to QA format . they propose qq prompting and qt prompting to improve their results .
Outcome:	The proposed method improves on different model sizes, benchmarks and settings.

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Hongyao Tu, Liang Zhang, Yujie Lin, Xin Lin, Haibo Zhang, Long Zhang, Jinsong Su

Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction (2020.coling-main)

Challenge:	Existing studies focus on building models that can only handle predefined relations . however, their reliance on human annotation limits their practicality .
Approach:	They propose an open relation extraction framework that can generalize to new relations not encountered during training.
Outcome:	The proposed framework can generalize to new relations not encountered during training.

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Huiwei Zhou, Yibin Xu, Weihong Yao, Zhe Liu, Chengkun Lang, Haibin Jiang

The State of Relation Extraction Data Quality: Is Bigger Always Better? (2024.findings-acl)

Challenge:	Existing approaches to document-level relation extraction are difficult to establish direct connections between distant entity pairs.
Approach:	They propose a global context-enhanced Graph Convolutional Network model which captures rich global context information of entities in a document.
Outcome:	The proposed model captures rich global context information of entities in a document.

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Erica Cai, Brendan O’Connor

Advancing Cross-Lingual Entity Alignment with Large Language Models: Tailored Sample Segmentation and Zero-Shot Prompts (2024.findings-emnlp)

Challenge:	Relation extraction (RE) methods extract tuples of relationships from text . many datasets with frequent label errors have been used .
Approach:	They review recent surveys and a sample of recent RE methods papers . they find that real-time evaluations of RE methods are possible .
Outcome:	a sample of 38 datasets currently being used shows that many have frequent label errors . a small number of relations in specific domains can more realistically evaluate methods .

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Linyan Yang, Jingwei Cheng, Fu Zhang

DeepREF: A Framework for Optimized Deep Learning-based Relation Classification (2022.lrec-1)

Challenge:	Existing approaches to integrate large language models into cross-lingual entity alignment tasks pose challenges in handling large-scale data, generating suitable data samples, and adapting prompts for the EA task.
Approach:	They propose a framework that integrates distance feature extraction, sample Segmentation, and zero-shot prompts to integrate LLMs into cross-lingual entity alignment tasks.
Outcome:	The proposed framework is able to extract features from large-scale data and adapt prompts to the task.

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Igor Nascimento, Rinaldo Lima, Adrian-Gabriel Chifu, Bernard Espinasse, Sébastien Fournier

CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction (2021.acl-long)

Challenge:	Existing frameworks for relation extraction (RE) are limited due to lack of implementation details.
Approach:	They propose to use deep learning to develop relation extraction systems using deep learning models.
Outcome:	The proposed framework is inspired by the OpenNRE and REflex existing frameworks.

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Tao Chen, Haizhou Shi, Siliang Tang, Zhigang Chen, Fei Wu, Yueting Zhuang

Summarization as Indirect Supervision for Relation Extraction (2022.findings-emnlp)

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.

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Keming Lu, I-Hung Hsu, Wenxuan Zhou, Mingyu Derek Ma, Muhao Chen

HARE: an entity and relation centric evaluation framework for histopathology reports (2025.findings-emnlp)

Challenge:	Relation extraction (RE) models rely on training data with expensive annotations . et al., 2018; Zhao e.t al, 2018) .
Approach:	They propose a method that converts RE into a summarization formulation by using constraint decoding techniques.
Outcome:	The proposed method improves relation extraction models with high-resource and high-contrast inferences.

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Yunsoo Kim, Michal Wen Sheue Ong, Alex Shavick, Honghan Wu, Adam P. Levine

Reward-based Input Construction for Cross-document Relation Extraction (2024.acl-long)

Challenge:	evaluating the clinical quality of medical domain automated text generation remains a challenge.
Approach:	They propose a framework for histopathology automated report evaluation that prioritizes clinically relevant content by aligning critical histo pathology entities and relations between reference and generated reports.
Outcome:	The proposed framework outperforms existing metrics in histopathology report evaluations.

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Byeonghu Na, Suhyeon Jo, Yeongmin Kim, Il-chul Moon

Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data (2023.findings-acl)

Challenge:	Existing methods for relation extraction are limited to a single sentence or document, but cross-document RE has emerged to address relations across multiple documents.
Approach:	They propose a sentence selector that extracts sentences based on relational evidence and rewards it with RE prediction scores.
Outcome:	The proposed method outperforms heuristic methods for relation extraction in multiple document scenarios.

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Qingyu Tan, Lu Xu, Lidong Bing, Hwee Tou Ng

Incorporating Global Contexts into Sentence Embedding for Relational Extraction at the Paragraph Level with Distant Supervision (L18-1)

Challenge:	Existing relation extraction models rely on supervised machine learning, but many datasets are incompletely annotated, causing false negatives and errors during inference stage.
Approach:	They propose a class-adaptive re-sampling self-training framework that favored the pseudo-labels of classes with high precision and low recall scores.
Outcome:	The proposed framework outperforms existing methods on the Re-DocRED and ChemDisgene datasets when the training data are incompletely annotated.

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Eun-kyung Kim, Key-Sun Choi

Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation (2020.coling-main)

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.

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Woohwan Jung, Kyuseok Shim

Revisiting Distant Supervision for Relation Extraction (L18-1)

Challenge:	Existing studies on relation extraction (RE) use labeled training data for relation extraction models but it is expensive and time-consuming.
Approach:	They propose a dual supervision framework which utilizes both types of data to train relation extraction models.
Outcome:	The proposed framework can predict labels by human annotation and distant supervision without labeling bias since it is expensive and time-consuming.

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Tingsong Jiang, Jing Liu, Chin-Yew Lin, Zhifang Sui

Towards Better Document-level Relation Extraction via Iterative Inference (2022.emnlp-main)

Challenge:	Existing approaches for relation extraction (RE) use supervised learning on relation-specific training data, which is expensive to acquire.
Approach:	They propose to use a new testing dataset to re-examine distant supervision approaches . they aim to draw new conclusions based on the new testing data .
Outcome:	The proposed method can generate training data without noise and bias issues . the proposed method is annotated by the researchers on Amzaon Mechanical Turk .

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Liang Zhang, Jinsong Su, Yidong Chen, Zhongjian Miao, Min Zijun, Qingguo Hu, Xiaodong Shi

Revisiting DocRED - Addressing the False Negative Problem in Relation Extraction (2022.emnlp-main)

Challenge:	Existing methods only consider feature information of entity pairs, but our model exploits both feature information and previous predictions of entity pair.
Approach:	They propose a document-level relation extraction model with iterative inference to extract relations between entities from raw texts.
Outcome:	The proposed model outperforms existing methods on three commonly-used datasets.

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Qingyu Tan, Lu Xu, Lidong Bing, Hwee Tou Ng, Sharifah Mahani Aljunied

Bio-RFX: Refining Biomedical Extraction via Advanced Relation Classification and Structural Constraints (2024.emnlp-main)

Challenge:	Using incomplete annotations, we find that false negative samples are prevalent in the DocRED dataset . we reannotate 4,053 documents in the dataset by adding the missed relation triples back to the original DocRED.
Approach:	They propose to re-annotate 4,053 documents in the document-level relation extraction dataset by adding missing relation triples back to the original DocRED.
Outcome:	The proposed dataset improves on the existing DocRED dataset by 13 F1 points.

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Minjia Wang, Fangzhou Liu, Xiuxing Li, Bowen Dong, Zhenyu Li, Tengyu Pan, Jianyong Wang

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

Challenge:	Existing methods for extracting structured data from unstructured texts neglect unique features of the biomedical literature, such as ambiguous entities and nested proper nouns.
Approach:	They propose a model that leverages sentence-level relation classification before entity extraction to tackle entity ambiguity.
Outcome:	The proposed model outperforms baselines in both NER and RE tasks and has competitive performance compared to the state-of-the-art fine-tuned baselines for RE.

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

Experimental versus In-Corpus Variation in Referring Expression Choice (2024.lrec-main)

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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T. Mark Ellison, Fahime Same

Ontology-Style Relation Annotation: A Case Study (2020.lrec-1)

Challenge:	Against our expectations, the divergence is greatest between the corpus and the GPT model.
Approach:	They compare the results of three studies to examine how well the corpus can model variation . they find that experimental methodology introduces substantial noise .
Outcome:	The results show that the corpus can model variation captured from the corpuse and RE form choices made during experiments.

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Savong Bou, Naoki Suzuki, Makoto Miwa, Yutaka Sasaki

GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks (2023.findings-acl)

Challenge:	Existing methods for Relation Extraction (RE) annotations use links between entities . a domain link connects the relation mention to the source entity while a range link connect the relation to the destination entity.
Approach:	They propose an Ontology-Style Relation (OSR) annotation approach to find relation mentions in relation annotations.
Outcome:	The proposed approach can be easily converted to Ontology RDF triples to populate an Ontologies.

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Xuming Hu, Aiwei Liu, Zeqi Tan, Xin Zhang, Chenwei Zhang, Irwin King, Philip S. Yu

Multi-hop Evidence Retrieval for Cross-document Relation Extraction (2023.findings-acl)

Challenge:	Existing work adopts data augmentation techniques to generate pseudo-annotated sentences . existing methods neither preserve semantic consistency of original sentences nor preserve syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations.
Approach:	They propose a dedicated augmentation technique for relational texts, named GDA, which uses two complementary modules to preserve both semantic consistency and syntax structures.
Outcome:	The proposed technique can bring 2.0% F1 improvements in three datasets under low-resource setting.

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Keming Lu, I-Hung Hsu, Wenxuan Zhou, Mingyu Derek Ma, Muhao Chen

Enhancing Relation Extraction via Adversarial Multi-task Learning (2022.lrec-1)

Challenge:	Relation Extraction (RE) is a task that seeks to identify the relation of entities described according to some context.
Approach:	They propose a multi-hop evidence retrieval method based on evidence path mining and ranking to support cross-document relation extraction.
Outcome:	The proposed method acquires cross-document evidence and boosts performance in both closed and open environments.

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Han Qin, Yuanhe Tian, Yan Song

Responsible & Trustworthy NLP

Challenge:	Existing studies have focused on re-modeling the given NEs and thus lead to inferior results when NE is sometimes ambiguous.
Approach:	They propose a relation extraction model with two training stages that uses adversarial multi-task learning to recover the given NEs.
Outcome:	The proposed model improves on two English benchmark datasets and shows state-of-the-art performance.

Entity-centered Cross-document Relation Extraction (2022.emnlp-main)

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Fengqi Wang, Fei Li, Hao Fei, Jingye Li, Shengqiong Wu, Fangfang Su, Wenxuan Shi, Donghong Ji, Bo Cai

Consistent Document-level Relation Extraction via Counterfactuals (2024.findings-emnlp)

Challenge:	Existing methods for relation extraction only use text snippets surrounding target entities in multiple documents.
Approach:	They propose a relation-extraction model that uses cross-path entity relation attention to detect the semantic relations between entities in a given text.
Outcome:	The proposed method outperforms the state-of-the-art methods in the dataset CodRED by 10%.

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Ali Modarressi, Abdullatif Köksal, Hinrich Schuetze

Exploiting the Syntax-Model Consistency for Neural Relation Extraction (2020.acl-main)

Challenge:	Document-level relation extraction models trained on factual data exhibit inconsistent behavior, relying on spurious signals such as specific entities and external knowledge to extract triples.
Approach:	They propose a counterfactual data generation approach for document-level relation extraction datasets using entity replacement to generate triples from factual data.
Outcome:	The proposed approach extracts triples from factual data but fails on counterfactual modification.

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Amir Pouran Ben Veyseh, Franck Dernoncourt, Dejing Dou, Thien Huu Nguyen

Syntactic Text Processing

Challenge:	Existing deep learning models for Relation Extraction (RE) have limited generalization beyond the syntactic structures of the input sentences.
Approach:	They propose a deep learning model that uses dependency trees to extract syntactic importance of words for Relation Extraction.
Outcome:	The proposed model outperforms existing models on three RE benchmark datasets.

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

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

Generating Labeled Data for Relation Extraction: A Meta Learning Approach with Joint GPT-2 Training (2023.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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Amir Pouran Ben Veyseh, Franck Dernoncourt, Bonan Min, Thien Nguyen

Continual Contrastive Finetuning Improves Low-Resource Relation Extraction (2023.acl-long)

Challenge:	Relation Extraction (RE) is the task of identifying semantic relation between entities mentioned in text.
Approach:	They propose a framework to automatically generate labeled data for Relation Extraction . they propose 'reward function' to update pre-trained language model for RE .
Outcome:	The proposed framework generates labeled data for relation extraction using a pre-trained language model and a meta learning approach to improve the generated samples.

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Wenxuan Zhou, Sheng Zhang, Tristan Naumann, Muhao Chen, Hoifung Poon

Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction (2024.emnlp-main)

Challenge:	Relation extraction (RE) has been challenging in low-resource domains and with limited resources.
Approach:	They propose to pretrain and finetune the RE model using consistent objectives of contrastive learning.
Outcome:	The proposed method outperforms PLM-based RE classifier on two document-level RE datasets.

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Sizhe Zhou, Yu Meng, Bowen Jin, Jiawei Han

Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction (2021.emnlp-main)

Challenge:	Existing Relation extraction models require extensive annotated training data, which is costly and labor-intensive to collect.
Approach:	They propose a new zero-shot RE task where only relation definitions are provided instead of seen-unseen relation instances.
Outcome:	The proposed task significantly improves cost-effective zero-shot performance by large margins.

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Kailong Hao, Botao Yu, Wei Hu

Robustness in NLP

Challenge:	Existing methods for relation extraction ignore the incompleteness of existing knowledge bases . current methods are too weak and cause noises when training and testing are not based on training data.
Approach:	They propose a method to automatically align unstructured text with relation instances in a knowledge base . they use heuristics to leverage the memory mechanism of deep neural networks to find out possible FN samples .
Outcome:	Experiments on two wildly-used benchmark datasets show the effectiveness of the proposed method.

Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting (2024.findings-emnlp)

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Siyi Liu, Yang Li, Jiang Li, Shan Yang, Yunshi Lan

AliGATr: Graph-based layout generation for form understanding (2024.findings-emnlp)

Challenge:	Existing methods for zero-shot Relation Extraction (RE) lack detailed, context-specific prompts for understanding various sentences and relations.
Approach:	They propose a framework that uses a three-stage diversity approach to prompt LLMs by generating multiple synthetic samples that encapsulate specific relations from scratch.
Outcome:	The proposed framework outperforms existing LLM-based zero-shot RE methods on benchmark datasets and shows that it produces high-quality synthetic data that enhances performance.

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Armineh Nourbakhsh, Zhao Jin, Siddharth Parekh, Sameena Shah, Carolyn Rose

Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modelling (2025.emnlp-main)

Challenge:	State of the art forms understanding models often rely on poorly calibrated output probabilities and low performance on relation extraction tasks.
Approach:	They propose a graph-based model that uses a generative objective to represent complex grid-like layouts that are often found in forms.
Outcome:	The proposed model performs better on the KIE and RE tasks and is more accurate than existing models.

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Sydney Anuyah, Mehedi Mahmud Kaushik, Sri Rama Krishna Reddy Dwarampudi, Rakesh Shiradkar, Arjan Durresi, Sunandan Chakraborty

Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction (2023.findings-acl)

Challenge:	integrating coreference and decomposition increases recall on rare relations by over 20%.
Approach:	They propose an open-source pipeline for extracting sentence-level knowledge graphs by combining robust coreference resolution with syntactic sentence decomposition.
Outcome:	The proposed pipeline achieves a 99.8% exact-match accuracy on sentence simplification.

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Xinyi Wang, Zitao Wang, Wei Hu

Responsible & Trustworthy NLP

Challenge:	Existing models for few-shot relation extraction (RE) are not suitable for continual few-sshot RE.
Approach:	They propose a new model to train a model for new relations with few labeled training data.
Outcome:	The proposed model outperforms state-of-the-art models on two benchmark datasets.

ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget (2024.findings-acl)

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Riccardo Orlando, Pere-Lluís Huguet Cabot, Edoardo Barba, Roberto Navigli

Revisiting Relation Extraction in the era of Large Language Models (2023.acl-long)

Challenge:	Entity Linking and Relation Extraction (EL) are fundamental tasks in Natural Language Processing.
Approach:	They propose a Retriever-Reader architecture for Entity Linking and Relation Extraction . they propose an input representation that incorporates the candidate entities alongside the text .
Outcome:	The proposed architecture achieves state-of-the-art in in- and out-of domain benchmarks while using academic budget training and with 40x inference speed compared to competitors.

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Somin Wadhwa, Silvio Amir, Byron Wallace

Retrieval over Classification: Integrating Relation Semantics for Multimodal Relation Extraction (2025.emnlp-main)

Challenge:	Standard supervised approaches to RE learn to tag tokens comprising entity spans and then predict the relationship between them.
Approach:	They propose to use large language models for RE to evaluate their performance . they use GPT-3 and Flan-T5 large to train RE .
Outcome:	The proposed model outperforms existing models on a sequence-to-sequence task under varying levels of supervision.

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Lei Hei, Tingjing Liao, null Peiyingxin, Yiyang Qi, Jiaqi Wang, Ruiting Li, Feiliang Ren

Generation-Augmented Retrieval: Rethinking the Role of Large Language Models in Zero-Shot Relation Extraction (2025.findings-emnlp)

Challenge:	Existing approaches to multimodal relation extraction ignore structural constraints and lack semantic expressiveness for fine-grained relation understanding.
Approach:	They propose a framework that reformulates multimodal relation extraction as a retrieval task driven by relation semantics.
Outcome:	The proposed framework achieves state-of-the-art performance on the benchmark datasets MNRE and MORE and exhibits stronger robustness and interpretability.

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Zehan Li, Fu Zhang, Tianyue Peng, He Liu, Jingwei Cheng

A Causal View of Entity Bias in (Large) Language Models (2023.findings-emnlp)

Challenge:	Recent advances in Relation Extraction (RE) emphasize Zero-Shot methodologies, aiming to recognize unseen relations between entities with no annotated data.
Approach:	They propose a plug-in retrieval adjuster that allows rapid fine-tuning without accessing LLMs’ parameters.
Outcome:	The proposed model demonstrates comparable performance on multiple benchmarks.

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Fei Wang, Wenjie Mo, Yiwei Wang, Wenxuan Zhou, Muhao Chen

RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction (2023.findings-emnlp)

Challenge:	Entity bias affects pretrained (large) language models, causing them to rely on (biased) parametric knowledge to make unfaithful predictions.
Approach:	They propose a structured causal model whose parameters are easier to estimate . they propose to perturb the original entity with neighboring entities .
Outcome:	The proposed model reduces biasing information pertaining to the original entity while still preserving sufficient semantic information from similar entities.

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Chengyuan Liu, Fubang Zhao, Yangyang Kang, Jingyuan Zhang, Xiang Zhou, Changlong Sun, Kun Kuang, Fei Wu

ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction (2026.findings-acl)

Challenge:	Named Entity Recognition (NER) and Relation Extraction (RE) models have limited success when extracting general schemas such as quadruples and quintuples.
Approach:	They propose a formal formulation that covers almost all extraction schemas and a Recursive Method with Explicit Schema Instructor for UIE.
Outcome:	The proposed method shows strong performance under full-shot and few-shot settings and achieves state-of-the-art results on the tasks of extracting complex schemas.

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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

Pointing Out the Shortcomings of Relation Extraction Models with Semantically Motivated Adversarials (2024.lrec-main)

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.

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Gennaro Nolano, Moritz Blum, Basil Ell, Philipp Cimiano

PolyNERE: A Novel Ontology and Corpus for Named Entity Recognition and Relation Extraction in Polymer Science Domain (2024.lrec-main)

Challenge:	Recent large language models have achieved state-of-the-art performance on many NLP tasks, but they rely on shortcut features and are unreliable when put under pressure.
Approach:	They propose to use semantically-motivated strategies to generate adversarial examples by replacing entity mentions to generate relation extraction models.
Outcome:	The proposed models show a lack of robustness when put under pressure.

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Van-Thuy Phi, Hiroki Teranishi, Yuji Matsumoto, Hiroyuki Oka, Masashi Ishii

Prompting Large Language Models for Counterfactual Generation: An Empirical Study (2024.lrec-main)

Challenge:	a new ontology for polymer-relevant entities and relations is available for training data . the ontologies are customizable to adapt to specific research needs.
Approach:	They propose a polymer-relevant ontology featuring crucial entities and relations . the ontologies are customizable to adapt to specific research needs .
Outcome:	The proposed ontology can extract polymer-relevant information from scientific papers . it can be customized to adapt to specific research needs .

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Yongqi Li, Mayi Xu, Xin Miao, Shen Zhou, Tieyun Qian

Relation Classification via Bidirectional Prompt Learning with Data Augmentation by Large Language Model (2024.lrec-main)

Challenge:	Large language models (LLMs) have made remarkable progress in a wide range of natural language understanding and generation tasks, but their ability to generate counterfactuals has not been examined systematically.
Approach:	They propose a framework to evaluate LLMs' ability to generate counterfactuals based on key factors including intrinsic properties and prompt design.
Outcome:	The proposed framework examines the strengths and weaknesses of large language models (LLMs) and identifies factors that influence their ability to generate counterfactuals.

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Yizhi Jiang, Jinlong Li, Huanhuan Chen

RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation (2024.emnlp-main)

Challenge:	Recent studies investigate Relation Extraction task from two different aspects.
Approach:	They propose to use Large Language Model (LLM) to do data augmentation and propose a bidirectional prompt template for prompt learning.
Outcome:	The proposed model outperforms the state-of-the-art on four datasets and outperformed existing methods on TACREV, RETACRED and Semeval.

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Kiseung Kim, Jay-Yoon Lee

Responsible & Trustworthy NLP

Challenge:	Existing approaches to retrieval augmented generation (RAG) are based on parametric knowledge and external knowledge.
Approach:	They propose a weakly supervised method for training a relevance estimator (RE) that provides relative relevance between contexts as previous rerankers did, and provides confidence, which can be used to classify whether given context is useful for answering the given question.
Outcome:	The proposed framework improves previously unreferenced large language models and can be trained with a small generator without labels for correct contexts.

Exploring Multimodal Relation Extraction of Hierarchical Tabular Data with Multi-task Learning (2025.acl-long)

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Xinyu Zhang, Aibo Song, Jingyi Qiu, Jiahui Jin, Tianbo Zhang, Xiaolin Fang

Multimodality

Challenge:	Existing studies overlook the need of mining relations among multiple columns rather than just the semantic relation between two specific columns in real-world practice.
Approach:	They propose a Chain-of-Thought distillation framework with self-correction mechanism to enhance MLLMs’ reasoning capabilities without increasing parameter scale.
Outcome:	The proposed method significantly outperforms baselines on wide datasets.

Synergetic Interaction Network with Cross-task Attention for Joint Relational Triple Extraction (2024.lrec-main)

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Da Luo, Run Lin, Qiao Liu, Yuxiang Cai, Xueyi Liu, Yanglei Gan, Rui Hou

R1-RE: Cross-Domain Relation Extraction with RLVR (2026.acl-long)

Challenge:	Existing approaches to joint entity-relation extraction are limited in their ability to capture the interdependence between the two sub-tasks.
Approach:	They propose a synergistic approach to capture interdependence between named entity recognition and relation extraction sub-tasks in a Synergetic Interaction Network.
Outcome:	The proposed model achieves significantly better performance on three benchmark datasets.

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Runpeng Dai, Tong Zheng, Run Yang, Kaixian Yu, Hongtu Zhu

WojoodRelations: Arabic Relation Extraction Corpus and Modeling (2025.emnlp-main)

Challenge:	Relation extraction (RE) is a core task in natural language processing.
Approach:	They propose a supervised learning task for relation extraction (RE) based on annotation guidelines.
Outcome:	The proposed model achieves an average OOD accuracy of 70%, on par with leading proprietary models such as GPT-4o.

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Alaa Aljabari, Mohammed Khalilia, Mustafa Jarrar

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

Challenge:	Existing work on Arabic RE remains limited due to the language’s rich morphology and syntactic complexity, and the lack of large, high-quality datasets.
Approach:	They propose to use WojoodRelations to extract relation relationships from Arabic textual data using relation-aware templates and GPT-Joint to perform relation-based retrieval.
Outcome:	The proposed method achieves a Cohen’s of 0.92, indicating high reliability, and supervised models achieve 92.89% F1 for RE, while LLMs obtain 72.73% F1 .

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

HCRE: LLM-based Hierarchical Classification for Cross-Document Relation Extraction with a Prediction-then-Verification Strategy (2026.findings-acl)

Challenge:	Named Entity Recognition and Relation Extraction are interdependent tasks in information extraction.
Approach:	They propose a generative method enhanced by anchor alignment to bridge NER and RE tasks . they use anchor entities as semantic pivots to align the two tasks based on their semantic representations .
Outcome:	The proposed method outperforms state-of-the-art models on five benchmark datasets.

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Guoqi Ma, Liang Zhang, Hongyao Tu, Hao Fu, Hui Li, Yujie Lin, Longyue Wang, Weihua Luo, Jinsong Su

On the Role of Discriminative Models in Generative Relation Extraction (2026.acl-long)

Challenge:	Existing approaches to cross-document relation extraction (RE) focus on identifying relations between head and tail entities from single sentence or document.
Approach:	They propose a hierarchical relation tree-based LLM-based hierarchic classification model for cross-document relation extraction (HCRE) based on predefined relations, the model can perform hierarchically classification level by level.
Outcome:	The proposed model outperforms existing baselines and validates its effectiveness.

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Guozheng Li, Peng Wang, Zijie Xu, Jing Zhou, Jiajun Liu, Ziyu Shang

Combining Distantly Supervised Models with In Context Learning for Monolingual and Cross-Lingual Relation Extraction (2026.acl-long)

Challenge:	Existing methods for relation extraction (RE) are discriminative and generative . previous studies show that discriminative models can support generative RE .
Approach:	They propose a framework that leverages discriminative models to produce a top-k set of candidate relations and integrates this knowledge into generative models via in-context or prompt learning.
Outcome:	The proposed framework achieves state-of-the-art on five widely used RE benchmarks.

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Vipul Kumar Rathore, Malik Hammad Faisal, Parag Singla, null Mausam