Papers with relation extraction
Meta Learning and Its Applications to Natural Language Processing (2021.acl-tutorials)
Copied to clipboard
| Challenge: | Meta-learning is a new technique that aims to learn better learning algorithms, including better parameter initialization, optimization strategy, network architecture, distance metrics, and beyond. |
| Approach: | This tutorial introduces Meta-learning approaches and the theory behind them, and then reviews the works of applying this technology to NLP problems. |
| Outcome: | This tutorial will introduce Meta-learning approaches and the theory behind them, and then review the works of applying this technology to NLP problems. |
Deep Learning on Graphs for Natural Language Processing (2021.naacl-tutorials)
Copied to clipboard
| Challenge: | Graph Neural Networks (GNNs) are powerful tools for non-Euclidean data modeling and are used in many graph-related NLP tasks. |
| Approach: | This tutorial will cover applying deep learning on graph techniques to NLP using Graph Neural Networks (GNNs) Graph4NLP is the first library for researchers and practitioners for easy use of GNNs for various NLP tasks. |
| Outcome: | This tutorial will cover the latest developments in deep learning on graph techniques and their applications in various NLP tasks. |
NLP Workbench: Efficient and Extensible Integration of State-of-the-art Text Mining Tools (2023.eacl-demo)
Copied to clipboard
| Challenge: | NLP Workbench is a web-based text mining platform that allows non-expert users to obtain semantic understanding of large-scale corpora using state-of-the-art text mining models. |
| Approach: | They propose to use a microservice architecture to replace existing models or integrate a new one. |
| Outcome: | The proposed model is extensible and can be easily replaced or integrated with existing models. |
Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing work on information extraction (IE) has solved the four main tasks separately, thus failing to benefit from inter-dependencies between tasks. |
| Approach: | They propose a model to solve four IE tasks in a single model that captures inter-dependencies between tasks. |
| Outcome: | The proposed model achieves state-of-the-art performance on monolingual and multilingual learning settings with three different languages. |
CovRelex: A COVID-19 Retrieval System with Relation Extraction (2021.eacl-demos)
Copied to clipboard
| Challenge: | Existing challenges to making the system more practical include dealing with newly created and unknown data, and solving the performance gap when utilizing present data. |
| Approach: | They propose a scientific paper retrieval system targeting entities and relations via relation extraction on COVID-19 scientific papers. |
| Outcome: | The proposed system can be accessed via https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex/. |
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to extract relation triplets require large datasets and a fixed set of relations. |
| Approach: | They propose to use a sentence-based task setting to generalize relation extraction methods to unseen relation sets. |
| Outcome: | The proposed method can extract multiple relation triplets in a sentence using language model prompts and structured text approaches. |
NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation (P18-4)
Copied to clipboard
| Challenge: | Money laundering (AML) is the process of transferring criminal and illegal proceeds into ostensibly legitimate assets. |
| Approach: | They propose a framework that uses deep learning to augment AML monitoring and investigation . money laundering is the process of transferring criminal and illegal proceeds into ostensibly legitimate assets . |
| Outcome: | The proposed framework reduces time and cost by 30% compared to existing methods . money laundering is the world's third largest "industry" |
Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction (2024.naacl-short)
Copied to clipboard
| Challenge: | Existing methods to extract unseen relations require laborious manual annotation . a new approach uses fine-grained matching to reduce manual annotation cost . |
| Approach: | They propose an efficient multi-grained matching approach that uses virtual entity matching to reduce manual annotation cost. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods and achieves inference efficiency and accuracy in zero-shot relation extraction tasks. |
BiomedCurator: Data Curation for Biomedical Literature (2022.aacl-demo)
Copied to clipboard
Mohammad Golam Sohrab, Khoa N.A. Duong, Ikeda Masami, Goran Topić, Yayoi Natsume-Kitatani, Masakata Kuroda, Mari Nogami Itoh, Hiroya Takamura
| Challenge: | BiomedCurator uses state-of-the-art natural language processing techniques to extract structured data from scientific articles. |
| Approach: | They propose a web application that extracts structured data from PubMed and ClinicalTrials.gov . the application uses a combination of natural language processing techniques and a pattern-based extraction approach . |
| Outcome: | The proposed system extracts the structured data from PubMed and ClinicalTrials.gov datasets. |
Deep Bidirectional Transformers for Relation Extraction without Supervision (D19-61)
Copied to clipboard
| Challenge: | Existing frameworks for relation extraction use distant supervision instead of annotated data. |
| Approach: | They propose a framework to deal with relation extraction tasks without supervision . they use syntactic parsing and pre-trained word embeddings to extract relations . |
| Outcome: | The proposed framework outperforms baselines on four biomedical datasets and achieves slightly worse results than the state-of-the-art in three out of four data sets. |
Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods treat each span token equally important, ignoring significant features. |
| Approach: | They propose a span-based joint extraction framework with attention-based semantic representations that utilizes span-specific and contextual representations. |
| Outcome: | The proposed model outperforms existing models on ACE2005, CoNLL2004 and ADE. |
Thesis Proposal: LLMs post-training for multilingual medical tasks. Instruction-Tuning, Continual-Pretraining or Reasoning? (2026.acl-srw)
Copied to clipboard
| Challenge: | Adapting Large Language Models to the medical domain remains an active area of research . |
| Approach: | They propose to compare three common adaptation approaches to adapt large language models to the medical domain. |
| Outcome: | The proposed models are built on top of foundational LLMs and rely on different post-training methodologies for domain and task performance. |
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population (2022.emnlp-demos)
Copied to clipboard
Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei Li
| Challenge: | Existing knowledge extraction tools are not complete due to emerging entities and relations in real-world applications. |
| Approach: | They propose an open-source knowledge extraction toolkit DeepKE that supports low-resource, document-level and multimodal scenarios in the knowledge base population. |
| Outcome: | The proposed toolkit supports low-resource, document-level and multimodal scenarios in the knowledge base population. |
Utilizing Graph Measure to Deduce Omitted Entities in Paragraphs (C18-2)
Copied to clipboard
| Challenge: | Existing studies on relation extraction only take into account intrasentence relationships that contain pairs of entities. |
| Approach: | They propose to capture omitted arguments in relation extraction given a proper knowledge base for entities of interest. |
| Outcome: | The proposed method improves relation extraction quality by capturing omitted arguments in sentences. |
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet (2021.acl-demo)
Copied to clipboard
| Challenge: | CogNet is a knowledge base that integrates three types of knowledge: linguistic knowledge, world knowledge and commonsense knowledge. |
| Approach: | They propose an information extraction toolkit called CogIE that is a bridge connecting raw texts and CogNet. |
| Outcome: | The proposed toolkit can ground raw texts to CogNet and leverage different types of knowledge to enrich extracted results. |
Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning (D19-57)
Copied to clipboard
| Challenge: | Using natural language processing to discover and mine drug-related knowledge from text has been a hot topic in recent years. |
| Approach: | They propose to use a pre-trained biomedical language representation model to extract mutation-disease knowledge from PubMed. |
| Outcome: | The proposed approaches achieve 0.60 (ranks 1) and 0.25 (rank 2) on task 1 and task 2 respectively in terms of F1 metric. |
DeepGeneMD: A Joint Deep Learning Model for Extracting Gene Mutation-Disease Knowledge from PubMed Literature (D19-57)
Copied to clipboard
| 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. |
SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling (P18-4)
Copied to clipboard
| Challenge: | SANTO is an annotation tool designed for complex relation extraction tasks . a subset of information extraction tasks can be typed n-ary relation extraction or slot filling . |
| Approach: | They propose a domain-adaptive annotation tool for complex slot filling tasks . SANTO enables fast and clearly structured annotation for multiple users in parallel . |
| Outcome: | The proposed tool can be used for slot filling tasks and import and export procedures of standard formats enable interoperability with external sources and tools. |
Predicting Document Coverage for Relation Extraction (2022.tacl-1)
Copied to clipboard
| 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. |
A Walk-based Model on Entity Graphs for Relation Extraction (P18-2)
Copied to clipboard
| Challenge: | Existing models treat each relation in a sentence individually, but a graph-based model needs to consider multiple relations between entities to model the dependencies among them. |
| Approach: | They propose a graph-based neural network model that treats multiple pairs in a sentence simultaneously and considers interactions among them. |
| Outcome: | The proposed model performs comparable to the state-of-the-art systems on the ACE 2005 dataset without external tools. |
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages (2020.acl-demos)
Copied to clipboard
| Challenge: | Existing tools that support only a few major languages are under-optimized for accuracy due to a focus on efficiency or use of less powerful models. |
| Approach: | They introduce a Python natural language processing toolkit that supports 66 languages . they train Stanza on 112 datasets and show it generalizes well on all languages compared to other tools . |
| Outcome: | The proposed toolkit performs well on 112 datasets and is compatible with the popular Java CoreNLP software. |
LLMs Underperform Graph-Based Parsers on Supervised Relation Extraction for Complex Graphs (2026.acl-short)
Copied to clipboard
| Challenge: | Relation extraction is a core NLP task which involves extracting [head, relation, dependent] RDF triples from text. |
| Approach: | They evaluate four large language models against a graph-based parser on six relation extraction datasets with sentence graphs of varying sizes and complexities. |
| Outcome: | The graph-based parser outperforms the LLMs on six relation extraction datasets with sentence graphs of varying sizes and complexities. |
Relating Relations: Meta-Relation Extraction from Online Health Forum Posts (2021.eacl-srw)
Copied to clipboard
| Challenge: | Relation extraction is a key task in knowledge extraction, and is often defined as identifying relations that hold between entities in text. |
| Approach: | They propose to conceptualise relation extraction tasks for user-generated health texts and create a dataset and model for meta-relation extraction. |
| Outcome: | The proposed model will be able to extract meta-relations from user-generated health texts with tolerable cognitive load and a new dataset and annotation scheme with tolerance for annotations. |
Improving Knowledge Base Construction from Robust Infobox Extraction (N19-2)
Copied to clipboard
| Challenge: | Existing knowledge bases are incomplete, resulting in poor answers and incompleteness. |
| Approach: | They propose a method to extract Wikipedia infobox tables to populate an existing KB. |
| Outcome: | The proposed method improves accuracy and completeness of the final KB significantly compared to DBpedia's baseline method. |
Recognizing UMLS Semantic Types with Deep Learning (D19-62)
Copied to clipboard
Isar Nejadgholi, Kathleen C. Fraser, Berry De Bruijn, Muqun Li, Astha LaPlante, Khaldoun Zine El Abidine
| Challenge: | Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. |
| Approach: | They propose to use general and domain-specific information to combine general and specific information to create a new entity recognition method. |
| Outcome: | The proposed method produces a state-of-the-art result on a newly released dataset, MedMentions. |
LOME: Large Ontology Multilingual Extraction (2021.eacl-demos)
Copied to clipboard
Patrick Xia, Guanghui Qin, Siddharth Vashishtha, Yunmo Chen, Tongfei Chen, Chandler May, Craig Harman, Kyle Rawlins, Aaron Steven White, Benjamin Van Durme
| Challenge: | LOME is a system for performing multilingual information extraction with large ontologies. |
| Approach: | They propose a system for multilingual information extraction with a framenet parser . LOME is available as a Docker container on Docker Hub and a lightweight version is available on the web . |
| Outcome: | The proposed system outperforms or is competitive with the (monolingual) state-of-the-art . it can be used to build knowledge graphs with large ontologies and across multiple languages . |
Multilingual Entity, Relation, Event and Human Value Extraction (N19-4)
Copied to clipboard
| Challenge: | Existing systems that extract knowledge elements from multiple languages and documents do not aggregate knowledge from multiple documents and languages. |
| Approach: | They propose a multilingual knowledge extraction system that performs entity discovery and linking, relation extraction, event extraction, and coreference. |
| Outcome: | The proposed system performs entity discovery and linking, relation extraction, event extraction, and coreference. |
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction (2020.findings-emnlp)
Copied to clipboard
Ranran Haoran Zhang, Qianying Liu, Aysa Xuemo Fan, Heng Ji, Daojian Zeng, Fei Cheng, Daisuke Kawahara, Sadao Kurohashi
| Challenge: | Existing methods to extract relation triplets from plain text introduce exposure bias . prior work has focused on pipeline methods that ignore intrinsic interactions between subtasks and propagate classification errors through the tasks. |
| Approach: | They propose a model that reduces the decoding length to three within a triplet and removes the order among triplets. |
| Outcome: | The proposed model overfits to both datasets while showing better generalization. |
Neural Relation Extraction for Knowledge Base Enrichment (P19-1)
Copied to clipboard
| Challenge: | Existing studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into knowledge base (KB) enrichment. |
| Approach: | They propose an end-to-end relation extraction model for knowledge base enrichment based on a neural encoder-decoder model . they propose to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end to end manner. |
| Outcome: | The proposed model outperforms state-of-the-art baselines by 15.51% and 8.38% on two real-world datasets. |
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision (2022.findings-naacl)
Copied to clipboard
| Challenge: | Distant supervision uses triple facts to label corpus for relation extraction, leading to wrong labeling and long-tail problems. |
| Approach: | They propose a model to enrich distantly-supervised sentences with entity types by injecting context-free and -related backgrounds into sentences to alleviate sentence-level wrong labeling. |
| Outcome: | The proposed model achieves state-of-the-art on benchmarks and in overall and long-tail performance. |
Effective Crowdsourcing of Multiple Tasks for Comprehensive Knowledge Extraction (2020.lrec-1)
Copied to clipboard
Sangha Nam, Minho Lee, Donghwan Kim, Kijong Han, Kuntae Kim, Sooji Yoon, Eun-kyung Kim, Key-Sun Choi
| Challenge: | Existing studies on information extraction from unstructured texts lack a coherent evaluation of all tasks. |
| Approach: | They propose to use crowdsourcing data to develop a Korean information extraction initiative point . they propose to train and evaluate four Korean information extracting tasks using a state-of-the-art model . |
| Outcome: | The proposed model will be used to evaluate four Korean information extraction tasks using crowdsourcing data. |
OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction (D19-3)
Copied to clipboard
| 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 . |
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study (2022.findings-emnlp)
Copied to clipboard
| 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. |
LED: A Dataset for Life Event Extraction from Dialogs (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods for extracting life events from conversations are limited. |
| Approach: | They propose a dataset containing fine-grained life event annotations on conversational data. |
| Outcome: | The proposed dataset combines three information extraction frameworks to extract life events from conversations. |
X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension (D19-61)
Copied to clipboard
| Challenge: | Existing knowledge bases are heavily biased towards English, but Wikipedias cover very different topics in different languages. |
| Approach: | They propose a multilingual dataset that frams relation extraction as a machine reading problem. |
| Outcome: | The proposed model can be used to transfer models cross-lingually and improves knowledge base completion across languages. |
SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction (2025.coling-main)
Copied to clipboard
| 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 . |
UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering (N19-1)
Copied to clipboard
| Challenge: | Existing work restricts search from one entity to another to the maximum number of hops . a knowledge graph is a powerful graph structure that encodes knowledge to save and organize it . |
| Approach: | They propose an unrestricted-hop framework which relaxes the restriction by using a transition-based search framework. |
| Outcome: | The proposed framework performs well with state-of-the-art models and is competitive without exhaustive searches. |
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework (D19-1)
Copied to clipboard
| Challenge: | Existing methods for relation extraction assume that text is noisy, but its corresponding labels are clean. |
| Approach: | They propose a framework that combines neural network and probabilistic modelling to denoise noisy relation labels. |
| Outcome: | The proposed framework improves the current art in uncovering the ground-truth relation labels. |
Distant Supervised Relation Extraction with Separate Head-Tail CNN (D19-55)
Copied to clipboard
| Challenge: | Distant supervised relation extraction suffers from mislabeling problem due to strong assumption . et al., 2009: distant supervision can be used to extract relations between entities . |
| Approach: | They propose a neural relation extraction framework which uses convolution and pooling to extract better semantic features of sentences. |
| Outcome: | The proposed framework improves relation extraction performance on a widely used dataset. |
Knowledge Extraction From Texts Based on Wikidata (2022.naacl-industry)
Copied to clipboard
| Challenge: | Existing knowledge extraction pipelines for English are not suitable for enterprise use. |
| Approach: | They propose a knowledge extraction pipeline for English which can be further used for building an entreprise-specific knowledge base. |
| Outcome: | The proposed pipeline can be used to build an entreprise-specific knowledge base. |
AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction (2025.naacl-srw)
Copied to clipboard
| 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. |
READ: Improving Relation Extraction from an ADversarial Perspective (2024.findings-naacl)
Copied to clipboard
| 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. |
Leveraging 2-hop Distant Supervision from Table Entity Pairs for Relation Extraction (D19-1)
Copied to clipboard
| 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. |
Framing Named Entity Linking Error Types (L18-1)
Copied to clipboard
| Challenge: | Named Entity Linking (NEL) and relation extraction forms the backbone of Knowledge Base Population tasks. |
| Approach: | They propose a taxonomy to frame common errors and apply it to four well-known Named Entity Linking systems. |
| Outcome: | The proposed taxonomy was applied to four well-known Named Entity Linking systems on three gold standards. |
XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)
Copied to clipboard
Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li
| 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. |
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation (2020.acl-demos)
Copied to clipboard
Dong-Ho Lee, Rahul Khanna, Bill Yuchen Lin, Seyeon Lee, Qinyuan Ye, Elizabeth Boschee, Leonardo Neves, Xiang Ren
| Challenge: | Existing frameworks for sequence labeling and classification require massive human effort and labeling data is limited. |
| Approach: | They propose a web-based, Label-Efficient AnnotatioN framework that allows an annotator to provide the needed labels for a task and can capture explanations for each labeling decision. |
| Outcome: | The proposed framework surpasses baseline F1 scores by 5-10 percentage points while using 2X times fewer labeled instances. |
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection (N18-1)
Copied to clipboard
| Challenge: | Existing unsupervised methods for learning hypernyms from unlabeled text are not scaled to large vocabularies or yield unacceptably poor accuracy. |
| Approach: | They propose an unsupervised method of hypernym discovery using word contexts . they use word2vec to embed word context distributions without supervision . |
| Outcome: | The proposed method provides double the precision and highest average performance on 11 datasets. |
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction (P18-1)
Copied to clipboard
| Challenge: | Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem. |
| Approach: | They propose a sentence-level true-positive generator to learn a true-negative generator from a fuzzy sentence bag. |
| Outcome: | The proposed method significantly improves the performance of distant supervision relation extraction compared to state-of-the-art systems. |
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction (2021.findings-acl)
Copied to clipboard
Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang’, Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat
| 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. |
Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors (2023.findings-acl)
Copied to clipboard
| 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. |
Distantly Supervised Relation Extraction in Federated Settings (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to label training datasets using distant supervision are expensive and cannot cover all walks of life. |
| Approach: | They propose a federated denoising framework to suppress label noise in federation . they propose to use a multiple instance learning based denoisation method to select reliable sentences . |
| Outcome: | The proposed method can select reliable sentences via cross-platform collaboration. |
Disease Network Constructor: a Pathway Extraction and Visualization (2023.acl-demo)
Copied to clipboard
Mohammad Golam Sohrab, Khoa Duong, Goran Topić, Masami Ikeda, Nozomi Nagano, Yayoi Natsume-Kitatani, Masakata Kuroda, Mari Itoh, Hiroya Takamura
| 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. |
Full-Stack Information Extraction System for Cybersecurity Intelligence (2022.emnlp-industry)
Copied to clipboard
| Challenge: | a rapid increase in cyberattacks and attacks pose enormous challenges to security analysts. |
| Approach: | They propose a full-stack information extraction system for the cybersecurity domain that extracts 26 entity types, 20 fixed relations and the temporal information of the relations. |
| Outcome: | The proposed system can extract 26 entity types, 20 fixed rela and temporal information of relations. |
End-to-End Argument Mining as Biaffine Dependency Parsing (2021.eacl-main)
Copied to clipboard
| Challenge: | Argumentation mining (AM) is a new field of research that uses dependency parsing to analyse arguments. |
| Approach: | They propose a neural end-to-end approach to argument mining based on dependency parsing . their model is biaffine dependency parsed and outperforms the current state-of-the-art . |
| Outcome: | The proposed model outperforms the state-of-the-art in component identification and relation identification. |
Thesis Proposal: Stability-Aware, Evidence-Grounded Knowledge Graph for Substance Use Disorders and Social Determinants of Health (2026.eacl-srw)
Copied to clipboard
| 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. |
Exploiting Structure in Representation of Named Entities using Active Learning (C18-1)
Copied to clipboard
| Challenge: | Named entities are atomic objects of reference and reasoning in many knowledge-centric applications. |
| Approach: | They propose an active-learning based framework that drastically reduces the labeled data required to learn entities' structures. |
| Outcome: | The proposed framework outperforms handwritten programs and supervised learning models in relation extraction and entity resolution tasks. |
Permutation Invariant Strategy Using Transformer Encoders for Table Understanding (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for encoding text in tables require additional training and require additional pretraining. |
| Approach: | They propose a novel encoding strategy that preserves the critical property of permutation invariance across rows or columns. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three table interpretation tasks: column type annotation, relation extraction, and entity linking. |
Learning Rich Representation of Keyphrases from Text (2022.findings-naacl)
Copied to clipboard
| 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. |
KnowledgeNet: A Benchmark Dataset for Knowledge Base Population (D19-1)
Copied to clipboard
| Challenge: | KnowledgeNet provides text exhaustively annotated with facts . high-quality KBs still rely almost exclusively on human-curated structured or semi-structured data. |
| Approach: | They propose five baseline approaches to populating a knowledge base with facts . the best approach achieves an F1 score of 0.50, significantly outperforming a traditional approach by 79% . |
| Outcome: | The best approach achieves an F1 score of 0.50, outperforming a traditional approach by 79%, indicating the dataset is challenging. |
Fine-grained Contrastive Learning for Relation Extraction (2022.emnlp-main)
Copied to clipboard
| 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. |
DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)
Copied to clipboard
Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun
| 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. |
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)
Copied to clipboard
Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun
| 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. |
Multi-facet Universal Schema (2021.eacl-main)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction (N18-1)
Copied to clipboard
| Challenge: | Existing work in relation extraction ignores relationships expressed across sentence boundaries . document-level annotation is common in biological text . |
| Approach: | They propose a model which simultaneously predicts relationships between all mention pairs in a document. |
| Outcome: | The proposed model is larger than existing human-annotated biological information extraction datasets and more accurate than distantly supervised alternatives. |
Recipe Instruction Semantics Corpus (RISeC): Resolving Semantic Structure and Zero Anaphora in Recipes (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing approaches to understanding recipe instructions make assumptions that are domain specific. |
| Approach: | They propose a new dataset for information extraction on recipes . they avoid a priori pre-defining domain-specific predicates to recognize . instead, they focus on basic understanding of the expressed semantics . |
| Outcome: | The proposed dataset avoids a priori pre-defining domain-specific predicates to recognize . instead, it focuses on basic understanding of the expressed semantics rather than reducing them to a simplified state representation. |
Sentence Embedding Alignment for Lifelong Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to relation extraction require a fixed set of relations . Existing methods assume a closed set of relationships and perform once-and-for-all training on a set of datasets. |
| Approach: | They propose to improve the stochastic gradient methods with a replay memory to alleviate the forgetting problem by anchoring the sentence embedding space. |
| Outcome: | The proposed method outperforms state-of-the-art methods on multiple benchmarks. |
Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Relation extraction systems require large amounts of labeled examples which are costly to annotate. |
| Approach: | They propose to use hand-made relation extraction tasks to refine a pretrained textual entailment engine which is run as-is or further fine-tuned on labeled examples. |
| Outcome: | The proposed system achieves 63% F1 zero-shot, 69% with 16 examples per relation and 4 points short of the state-of-the-art system on the same conditions. |
Chaining Event Spans for Temporal Relation Grounding (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing approaches to understanding temporal relations between events have relied on answer overlaps as a proxy label to distinguish similar and dissimilar questions. |
| Approach: | They propose a timeline reasoning network that elicits proper reasoning behaviors through a module for predicting time spans of events. |
| Outcome: | The proposed approach outperforms existing methods by resolving spurious overlaps using the predicted timeline. |
Sentences with Gapping: Parsing and Reconstructing Elided Predicates (N18-1)
Copied to clipboard
| Challenge: | Sentences with gapping lack an overt predicate to indicate the relation between two or more arguments. |
| Approach: | They propose two methods for parsing to a Universal Dependencies graph representation that explicitly encodes the elided material with additional nodes and edges. |
| Outcome: | The proposed methods reconstruct elided material from dependency trees with high accuracy when the parser correctly predicts the existence of a gap. |
Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision (N19-1)
Copied to clipboard
| Challenge: | Distant supervision is an important paradigm for automatically extracting relations . but the examples collected can be noisy and pose significant challenge for labeling . |
| Approach: | They propose a method to predict whether two entities participate in a relation at a given time spot. |
| Outcome: | The proposed model performs better in WIKI-TIME and NYT-10 datasets compared with the best existing models . the proposed model is based on a dataset with a valid period of a certain relation of two entities in the knowledge base . |
Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation (2021.eacl-main)
Copied to clipboard
| Challenge: | Public datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP). |
| Approach: | They identify leakage of training data into test data on several publicly available datasets used to evaluate NLP tasks, including named entity recognition and relation extraction. |
| Outcome: | The proposed model can memorize and generalize data on several publicly available datasets and is compared against previously unseen data. |
Rewarding Coreference Resolvers for Being Consistent with World Knowledge (D19-1)
Copied to clipboard
Rahul Aralikatte, Heather Lent, Ana Valeria Gonzalez, Daniel Hershcovich, Chen Qiu, Anders Sandholm, Michael Ringaard, Anders Søgaard
| Challenge: | Unresolved coreference is a bottleneck for relation extraction systems . a state-of-the-art system may be able to infer the relation using distributional information about the phrase the Sunshine State, but is likely to have limited evidence for the decision that it is coreferential with Florida rather than with Skynyrd. |
| Approach: | They propose to forward coreference input to relation extraction system and reward them for producing triples that are found in knowledge bases. |
| Outcome: | The proposed approach improves over the state-of-the-art by forwarding their input to a relation extraction system and rewarding resolvers for producing triples that are found in knowledge bases. |
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. |
| Approach: | They propose a new approach that uses text embeddings to obtain basis vectors by matrix decomposition and constructs a space for representing all prompts. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks. |
Dependency Position Encoding for Relation Extraction (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods to extract relation extraction from sentence are limited in focusing on leveraging dependency information. |
| Approach: | They propose dependency position encoding (DPE) that incorporates dependency connections and dependency types into the self-attention mechanism to distinguish the importance of different word dependencies. |
| Outcome: | The proposed method significantly outperforms the previous methods on SemEval 2010 Task 8, KBP37, and TACRED. |
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction (D18-1)
Copied to clipboard
| Challenge: | Existing neural networks focus on instance representation, and subsampling fails to retain precise spatial relationships between higher-level parts. |
| Approach: | They propose a neural approach based on capsule networks with attention mechanisms to extract relational information from a capsule. |
| Outcome: | The proposed method improves the precision of the predicted relations with different benchmarks. |
Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction (2022.findings-naacl)
Copied to clipboard
Xiang Chen, Ningyu Zhang, Lei Li, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen
| Challenge: | Existing approaches for named entity recognition and relation extraction suffer from error sensitivity when irrelevant object images are incorporated in texts. |
| Approach: | They propose a hierarchical visual prefix fusion NeTwork for visual-enhanced entity and relation extraction using pluggable visual prefixed visual features. |
| Outcome: | The proposed method achieves state-of-the-art on three benchmark datasets. |
Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines (D19-1)
Copied to clipboard
| Challenge: | Using dialog agents, we can collect family history data from in-person consultations and crowdsource it to a genetic counselor. |
| Approach: | They propose to use natural language interactions annotated with medical family histories to collect information from a genetic counselor and crowdsourcing. |
| Outcome: | The proposed system averages 0.87 on complex sentences on the targeted relations. |
Dynamic Graph Transformer for Implicit Tag Recognition (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing studies focus on using explicit information in articles and do not consider the implicit information. |
| Approach: | They propose a dynamic graph transformer that distills the textual information and the entity relations on the fly. |
| Outcome: | The proposed model can extract the textual information and the entity relations on the fly. |
Enhancing Discriminative Representation in Similar Relation Clusters for Few-Shot Continual Relation Extraction (2025.naacl-long)
Copied to clipboard
Anh Duc Le, Nam Le Hai, Thanh Xuan Nguyen, Linh Ngo Van, Nguyen Thi Ngoc Diep, Sang Dinh, Thien Huu Nguyen
| 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. |
Graph Neural Networks with Generated Parameters for Relation Extraction (P19-1)
Copied to clipboard
| Challenge: | Existing graph neural networks can only process multi-hop relational reasoning on pre-defined graphs and cannot be directly applied in natural language relational reasoning. |
| Approach: | They propose a graph neural network with generated parameters using natural language sentences as inputs. |
| Outcome: | The proposed model can process relational reasoning on graphs and in natural language processing tasks. |
Contextual Argument Component Classification for Class Discussions (2020.coling-main)
Copied to clipboard
| Challenge: | Argument mining systems often consider contextual information when training to perform tasks such as argument component identification, classification, and relation extraction. |
| Approach: | They propose to incorporate speaker context and local discourse context into a model for classifying argument components in multi-party classroom discussions. |
| Outcome: | The proposed model improves when varying context size and position . the results support the claim that context size is important . |
Large language models are few-shot clinical information extractors (2022.emnlp-main)
Copied to clipboard
| Challenge: | a long-running goal of clinical NLP is the extraction of important variables trapped in clinical notes. |
| Approach: | They propose to use large language models to tackle diverse clinical extraction tasks . they propose to reannote existing CASI datasets to compare their models with clinical text. |
| Outcome: | The proposed models outperform existing models on few-shot clinical information extraction tasks. |
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data (P19-1)
Copied to clipboard
| Challenge: | Existing methods to perform relation extraction are feature-based or kernel-based, but the results of our study show that they can improve the performance of a baseline model with more than 10% absolute increase in F1-score. |
| Approach: | They propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. |
| Outcome: | The proposed model outperforms the state-of-the-art models on ACE 2005 Chinese and English corpus and significantly improves the performance of a baseline model with more than 10% increase in F1-score. |
Event-Guided Denoising for Multilingual Relation Learning (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for general purpose relation extraction use a fixed set of predetermined relations, but research has shifted to the identification of unseen relations in any language. |
| Approach: | They propose a method for collecting high quality relation training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost. |
| Outcome: | The proposed method achieves comparable results to the current state-of-the-art when trained on a smaller multilingual encoder . |
Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders (2020.emnlp-main)
Copied to clipboard
| Challenge: | Named entity recognition and relation extraction are two important fundamental problems. |
| Approach: | They propose to design two separate encoders to capture two different types of information in the representation learning process. |
| Outcome: | The proposed methods show significant improvements on standard datasets. |
Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for knowledge base question answering lack causality modeling . previous work fails to model such causalities in their pipeline . |
| Approach: | They propose a causal-enhanced table-filler to overcome sequence-modelling issues . they propose an efficient beam-search algorithm to scale complex queries on large-scale KBs. |
| Outcome: | Experiments on LC-QuAD 1.0 show that the proposed method surpasses state-of-the-arts by a large margin while remaining time and space efficient. |
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction (P19-1)
Copied to clipboard
| Challenge: | GraphRel is an end-to-end relation extraction model that uses graph convolutional networks to learn named entities and relations. |
| Approach: | They propose a graph-based relation extraction model which uses graph convolutional networks to jointly learn named entities and relations. |
| Outcome: | The proposed model outperforms previous models on two public datasets: NYT and WebNLG. |
Document-level Relationship Extraction by Bidirectional Constraints of Beta Rules (2023.emnlp-main)
Copied to clipboard
| Challenge: | Document-level Relation Extraction (DocRE) aims to extract relations among entity pairs in documents. |
| Approach: | They propose a logic constraint framework that uses bidirectional constraints to model rules by beta contribtion and reconstruct rule consistency loss by bidirectional constraint. |
| Outcome: | The proposed framework outperforms existing models in relation extraction performance and logical consistency. |
Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to handle wrong labeling and long-tail relations are labor-intensive and scarce training data. |
| Approach: | They propose a neural network to handle wrong labeling and long-tail relations by collaborating relation-augmented attention. |
| Outcome: | The proposed neural network improves the state-of-the-art on the NYT dataset . |
DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction (2023.eacl-main)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) is a task of identifying relations between entities in a document. evidence retrieval (ER) in DocRE faces two major issues: high memory consumption and limited availability of annotations. |
| Approach: | They propose a memory-efficient approach that uses evidence as the supervisory signal . they propose er self-training to learn ER from automatically-generated evidence . |
| Outcome: | The proposed method exhibits state-of-the-art performance on the DocRED benchmark . it uses evidence as the supervisory signal and self-trains on massive data without annotations . |
Episodic Memory Retrieval from LLMs: A Neuromorphic Mechanism to Generate Commonsense Counterfactuals for Relation Extraction (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have achieved satisfactory performance in counterfactual generation, however, there are misalignments between LLMs and humans which hinder LLM from handling complex tasks like relation extraction. |
| Approach: | They propose to mimic the episodic memory retrieval mechanism of human hippocampus to align LLMs’ generation process with that of humans. |
| Outcome: | The proposed framework improves over existing methods in terms of quality of counterfactuals. |
Improving Relation Extraction with Relational Paraphrase Sentences (2020.coling-main)
Copied to clipboard
| 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. |
Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to few-shot relation extraction require training. |
| Approach: | They propose a method for few-shot relation extraction using large language models, called CoT-ER, chain-of-thought with explicit evidence reasoning. |
| Outcome: | The proposed approach achieves competitive performance compared to the fully-supervised state-of-the-art approach on the FewRel1.0 and FewRela2.0 datasets. |
GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models (2024.naacl-long)
Copied to clipboard
| 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. |
Holistic Evaluation of Automatic TimeML Annotators (2022.lrec-1)
Copied to clipboard
| Challenge: | TimeML is an annotation scheme for representing temporal information in texts. |
| Approach: | They propose to combine eight metrics for holistic evaluation of TimeML graphs. |
| Outcome: | The proposed system produces graphs with 1/3 of the time indeterminacy and 1/3 of gold standard . the proposed system is compared with four other systems and is a good fit for the proposed task. |
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information (D18-1)
Copied to clipboard
| 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. |
Generic and Trend-aware Curriculum Learning for Relation Extraction (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing curriculum learning approaches for relation extraction are lacking in text graphs. |
| Approach: | They propose a generic and trend-aware curriculum learning approach that integrates textual and structural information in text graphs for relation extraction between entities. |
| Outcome: | The proposed model shows improvement over state-of-the-art methods across several datasets. |
Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction (2025.naacl-long)
Copied to clipboard
| 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. |
Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution (2021.findings-acl)
Copied to clipboard
| Challenge: | Using unsupervised entity linking, we solve named entity recognition, coreference resolution and relation extraction tasks together. |
| Approach: | They propose to use a knowledge base to inject information into a joint IE model by using unsupervised entity linking. |
| Outcome: | The proposed model improves on two datasets with 5% F1 score. |
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction (2022.naacl-main)
Copied to clipboard
| 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. |
HistRED: A Historical Document-Level Relation Extraction Dataset (2023.acl-long)
Copied to clipboard
| 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 . |
Text-to-Table: A New Way of Information Extraction (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for information extraction are not well understood . text-to-table is a problem that aims to extract information from text data . |
| Approach: | They propose a new problem setting of information extraction, called text-to-table . they formalize text- to-table as a sequence-tosequence problem . |
| Outcome: | The proposed method outperforms existing methods on text-to-table tasks. |
A Survey on Recent Advances in Named Entity Recognition from Deep Learning models (C18-1)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. |
| Approach: | They propose to use recurrent neural networks to generate NERs over characters, sub-words and/or word embeddings to improve named entity recognition. |
| Outcome: | The proposed architectures are better than those based on feature engineering and other supervised or semi-supervised learning algorithms. |
Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning (C18-1)
Copied to clipboard
| Challenge: | Existing approaches to named entity recognition (NER) in Chinese are limited by the lack of annotated data. |
| Approach: | They propose a method which can automatically populate annotated training data without humancost by using distant supervision. |
| Outcome: | The proposed method performs better than comparison systems on two datasets. |
Combining Distant and Direct Supervision for Neural Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Existing methods to train relation extraction with distant supervision use noisy labels and implicitly assumes that all the KB facts are mentioned in the text. |
| Approach: | They propose to combine distant supervision data with additional directly-supervised data to train relation extraction models by using sigmoidal attention weights with max pooling. |
| Outcome: | The proposed method achieves state-of-the-art on the widely used FB-NYT dataset. |
A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks (2024.lrec-main)
Copied to clipboard
| 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. |
Generalizing Natural Language Analysis through Span-relation Representations (2020.acl-main)
Copied to clipboard
| Challenge: | a large number of natural language processing tasks are generated with specially designed architectures. |
| Approach: | They propose to represent a wide variety of tasks in a single unified format . they perform extensive experiments to demonstrate benefits of multi-task learning . |
| Outcome: | The proposed model performs comparable to state-of-the-art models on 10 tasks . it also shows that it can analyze differences and similarities in how the model handles different tasks compared to other models . |
Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning (P18-1)
Copied to clipboard
| Challenge: | Distant supervision is an efficient method for relation extraction, but it is noisy. |
| Approach: | They propose a deep reinforcement learning strategy to generate false-positive indicators . they redistribute false positives into negative examples to reduce false positive problem . |
| Outcome: | The proposed method significantly improves the performance of distant supervision compared to state-of-the-art systems. |
A Relation Extraction Dataset for Knowledge Extraction from Web Tables (2022.coling-1)
Copied to clipboard
| Challenge: | Existing datasets with relational web-tables are either synthetic, or very small in size. |
| Approach: | They propose to annotate relational web-tables against a human-annotated dataset using crowd sourced annotators from MTurk. |
| Outcome: | The proposed dataset has 50x larger number of column pairs than the existing human-annotated benchmark. |
Novel Relation Detection: Discovering Unknown Relation Types via Multi-Strategy Self-Supervised Learning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to relation extraction can only recognize predefined relation types . new or out-of-scope relation types may continually emerge after the model is deployed . |
| Approach: | They propose a novel relation detection task that uses self-supervised learning to handle shallow semantic similarity problem. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two datasets. |
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning (2022.coling-1)
Copied to clipboard
| Challenge: | Existing document-level relation extraction methods are sparse in relational entity pairs and the representation of entity pairs is insufficient. |
| Approach: | They propose a Pair-Aware and Entity-Enhanced(PAEE) model to solve two challenges . they propose predicting potential relational entity pairs and assembling directional entity pairs . |
| Outcome: | The proposed model can obtain state-of-the-art performance on four benchmark datasets . it can predict potential relational entity pairs and assemble directional entity pairs . |
GPT-RE: In-context Learning for Relation Extraction using Large Language Models (2023.emnlp-main)
Copied to clipboard
| 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. |
Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on joint entity and relation extraction fail to fully utilize the interdependence between entity types and relation types. |
| Approach: | They propose a synchronous dual network with cross-type attention via separately and interactively considering the entity types and relation types. |
| Outcome: | The proposed model achieves state-of-the-art on NYT and WebNLG datasets. |
Relation Extraction with Type-aware Map Memories of Word Dependencies (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies focus on the dependency connections between words with limited attention paid to exploiting dependency types. |
| Approach: | They propose a neural approach for relation extraction with type-aware map memories . they map all associated words along with dependencies among them to memory slots . |
| Outcome: | The proposed approach achieves state-of-the-art on two English benchmark datasets. |
Weakly-Supervised Questions for Zero-Shot Relation Extraction (2023.eacl-main)
Copied to clipboard
| Challenge: | Zero-Shot Relation Extraction (ZRE) is a task where the training and test sets have no shared relation types. |
| Approach: | They propose to learn a model that can translate relation descriptions into relevant questions, which are then leveraged to generate the correct tail entity. |
| Outcome: | The proposed model outperforms the state-of-the-art on the fewrel and WikiZSL datasets by more than 16 F1 points without using gold question templates. |
UTC-IE: A Unified Token-pair Classification Architecture for Information Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Information Extraction (IE) tasks have been solved with different models because of their output structures. |
| Approach: | They propose a Unified Token-pair Classification architecture for Information Extraction that introduces Plusformer on top of the token-pear feature matrix. |
| Outcome: | The proposed approach outperforms task-specific and unified models on all tasks in 10 datasets and achieves better results on 2 joint IE datasets. |
Relation Extraction with Word Graphs from N-grams (2021.emnlp-main)
Copied to clipboard
| 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. |
Do Syntax Trees Help Pre-trained Transformers Extract Information? (2021.eacl-main)
Copied to clipboard
| Challenge: | Recent work suggests that incorporating syntax information from dependency trees can improve task-specific transformer models. |
| Approach: | They propose to incorporate dependency tree information into pre-trained transformers for three tasks . they propose a late fusion approach and a joint fusion technique to infuses syntax structure into attention layers. |
| Outcome: | The proposed models obtain state-of-the-art results on SRL and relation extraction tasks. |
H-FND: Hierarchical False-Negative Denoising for Distant Supervision Relation Extraction (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing work on distant supervision denoising introduces false-positive (FP) and falsenegative (FN) training instances to the generated datasets. |
| Approach: | They propose a hierarchical false-negative denoising framework for distant supervision relation extraction that denoises false-positive and false- negative training instances. |
| Outcome: | The proposed framework can revise FN instances correctly and maintain high F1 scores even when 50% of instances have been turned into negatives. |
Paths to Relation Extraction through Semantic Structure (2021.findings-acl)
Copied to clipboard
| 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 . |
GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction (2021.findings-acl)
Copied to clipboard
| 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. |
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning (D18-1)
Copied to clipboard
| Challenge: | Existing methods for extracting relations are slow and lack precision . a novel approach to extract relations is proposed to reduce noise between sentences . |
| Approach: | They propose a word-level distant supervised approach for relation extraction using New York Times and Freebase. |
| Outcome: | The proposed method improves the area of precision/call(PR) from 0.35 to 0.39 over the state-of-the-art methods. |
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction (D18-1)
Copied to clipboard
| Challenge: | Existing dependency-based models neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively. |
| Approach: | They propose an extension of graph convolutional networks that is tailored for relation extraction by pruning dependency trees too aggressively. |
| Outcome: | The proposed model outperforms existing sequence and dependency-based models on the large-scale TACRED dataset and has complementary strengths to sequence models. |
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding (D18-1)
Copied to clipboard
| Challenge: | Existing methods to generate large scale labeled data for relation extraction produce noisy relation labels when there are multiple relationships between entities. |
| Approach: | They propose a method which assumes that a pair of entities appears in a Knowledge Graph and trains a relation classifier. |
| Outcome: | The proposed method performs well in the current distant supervision dataset. |
CARE: Co-Attention Network for Joint Entity and Relation Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing joint entity and relation extraction methods suffer from feature confusion or inadequate interaction between the two subtasks. |
| Approach: | They propose a Co-Attention network for joint entity and relation extraction that adopts a parallel encoding strategy to learn separate representations for each subtask. |
| Outcome: | The proposed model outperforms existing models on three datasets . it uses a parallel encoding strategy to learn separate representations for each subtask . |
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning (2021.acl-long)
Copied to clipboard
Yujia Qin, Yankai Lin, Ryuichi Takanobu, Zhiyuan Liu, Peng Li, Heng Ji, Minlie Huang, Maosong Sun, Jie Zhou
| Challenge: | Existing pre-training objectives do not explicitly model relational facts in text . Experimental results show that ERICA can improve typical PLMs on several language understanding tasks, including relation extraction, entity typing and question answering. |
| Approach: | They propose a contrastive learning framework ERICA to obtain a deep understanding of entities and relations in text. |
| Outcome: | The proposed framework can improve PLMs on several language understanding tasks, especially under low-resource settings. |
Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to predict knowledge base relations are limited by maintenance costs and text-based formats. |
| Approach: | They propose a system that can extend relational database tables with information extracted from a document corpus. |
| Outcome: | The proposed system outperforms existing methods by incorporating embeddings of text-based representations of the entities and relations. |
MRL Parsing Without Tears: The Case of Hebrew (2024.findings-acl)
Copied to clipboard
| Challenge: | a new approach to parsing morphologically rich languages (MRLs) is needed to overcome the deficiencies of current approaches. |
| Approach: | They propose a "flipped pipeline" where multiple layers are predicted independently on whole-token basis and then synthesized. |
| Outcome: | The proposed model achieves near-SOTA performance on Hebrew NLP tasks. |
Domain Adapted Distant Supervision for Pedagogically Motivated Relation Extraction (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing systems for question generation based on shallow linguistic information at text level generate questions for reading comprehension. |
| Approach: | They propose a relation extraction system that extracts pedagogically motivated relation types from a text to generate questions for reading comprehension. |
| Outcome: | The proposed system extracts pedagogically motivated relations from a text . it performs better on the subset of relations suitable for pedagical purposes. |
ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to relation extraction require labeled data, but labeling is difficult . Existing models cannot recognize rare instances that are never covered by training data . |
| Approach: | They propose a multi-task learning model that directly predicts unseen relations without hand-crafted attribute labeling and multiple pairwise classifications. |
| Outcome: | The proposed model outperforms existing methods by 13.54% on two well-known datasets. |
Revisiting the Negative Data of Distantly Supervised Relation Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for relation extraction with distant supervision generate plenty of training samples but noisy labels and imbalanced training data cause problems. |
| Approach: | They propose a method that automatically labels a sentence with relational triples from a knowledge base. |
| Outcome: | The proposed method outperforms existing methods even with false positive samples. |
A French Corpus and Annotation Schema for Named Entity Recognition and Relation Extraction of Financial News (2020.lrec-1)
Copied to clipboard
| Challenge: | Strict regulatory regimes mandate financial institutions to rigorously monitor their customers' financial activities. |
| Approach: | They propose to use an ontology of compliance-related concepts and relationships along with a corpus annotated according to it to train and evaluate named entity recognition algorithms. |
| Outcome: | The proposed ontology allows for training and evaluating domain-specific named entity recognition and relation extraction algorithms. |
CHisIEC: An Information Extraction Corpus for Ancient Chinese History (2024.lrec-main)
Copied to clipboard
| 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 . |
Open Information Extraction on Scientific Text: An Evaluation (C18-1)
Copied to clipboard
| Challenge: | Open Information Extraction (OIE) is the unsupervised creation of structured information from text. |
| Approach: | They propose to use two state-of-the-art OIE systems to evaluate the performance of OIE on scientific texts originating from 10 different disciplines. |
| Outcome: | The proposed methods perform significantly worse on scientific text than encyclopedic text. |
CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies integrate word embeddings with cognitive features into neural models of natural language processing (NLP) but there are some issues in the use of cognitive features in NLP. |
| Approach: | They propose a cog-align approach that aligns textual and cognitive inputs to capture differences and commonalities. |
| Outcome: | The proposed model improves on three NLP tasks with multiple cognitive features over state-of-the-art models. |
A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction (2024.findings-emnlp)
Copied to clipboard
| 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. |
Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing KBQA methods focus on the natural language but ignore textual information carried by the nodes and edges. |
| Approach: | They propose to perform relation extraction, relation matching, and relation reasoning tasks to align the natural language expressions to the relations in the KB and reason over the missing connections. |
| Outcome: | Experiments on WebQSP show that the proposed model outperforms baselines even when the KB is incomplete. |
Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze? (2022.acl-long)
Copied to clipboard
| Challenge: | We compare attention functions in pre-trained language models to human eye fixation patterns during task-specific reading tasks. |
| Approach: | They compare attention functions in large-scale pre-trained language models to classical cognitive models of human attention by using a dataset with eye-tracking recordings of native speakers of English. |
| Outcome: | The proposed model is as predictive of human eye fixation patterns as classical cognitive models of human attention. |
Towards Better Multi-task Learning: A Framework for Optimizing Dataset Combinations in Large Language Models (2025.findings-naacl)
Copied to clipboard
| Challenge: | Using a neural network, large language models can be trained on multiple tasks, allowing them to perform tasks efficiently. |
| Approach: | They propose a framework that leverages a neural network to select the best dataset combinations for enhancing multi-task learning (MTL) They propose to iteratively refine the selection, greatly improving efficiency while being model-, dataset-, and domain-independent. |
| Outcome: | The proposed framework iteratively refines the selection, greatly improving efficiency, while being model-, dataset-, and domain-independent. |
Learning from Context or Names? An Empirical Study on Neural Relation Extraction (2020.emnlp-main)
Copied to clipboard
| 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. |
TERMinator: A System for Scientific Texts Processing (2022.coling-1)
Copied to clipboard
| Challenge: | Existing datasets with annotations of scientific terms and relations are difficult to find for other fields, such as biomedical and multi-domains. |
| Approach: | They present a dataset that includes annotations for two tasks and develop a system called TERMinator for the study of the influence of language models on term recognition. |
| Outcome: | The proposed system improves the quality of the extracted entities and relations in Russian. |
GAN Driven Semi-distant Supervision for Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Existing methods for relation extraction are limited to costly hand-labeled training sets and hard to be extended to large-scale relations. |
| Approach: | They propose a semi-distant supervision approach for relation extraction by constructing a small accurate dataset and properly leveraging numerous instances without relation labels. |
| Outcome: | The proposed approach achieves significant improvements over baselines on real-world datasets. |
Adversarial training for multi-context joint entity and relation extraction (D18-1)
Copied to clipboard
| Challenge: | Existing models that use adversarial training (AT) have been used in various tasks such as parsing, POS tagging, relation extraction and translation. |
| Approach: | They propose to use adversarial training (AT) to regularize neural network methods by adding small perturbations to the input data. |
| Outcome: | The proposed model improves state-of-the-art on news, biomedical, and real estate datasets and for different languages. |
KoCHET: A Korean Cultural Heritage Corpus for Entity-related Tasks (2022.coling-1)
Copied to clipboard
| 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. |
OpenCeres: When Open Information Extraction Meets the Semi-Structured Web (N19-1)
Copied to clipboard
| Challenge: | Open Information Extraction (OpenIE) is a problem of extracting triples from natural language text whose predicate relations are not aligned to any pre-defined ontology. |
| Approach: | They propose an open-source method to extract triples from semi-structured websites . they use a semi-supervised label propagation technique to create training data for relations . |
| Outcome: | The proposed method extracts over 2 million triples from 31 websites in the movie vertical. |
GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding (2023.emnlp-main)
Copied to clipboard
| Challenge: | Pretrained language models do not utilize valuable geospatial information in large databases, e.g., OpenStreetMap. |
| Approach: | They propose a geospatially grounded language model that connects linguistic and geospheric contexts. |
| Outcome: | The proposed model bridges the gap between natural language processing and geospatial sciences. |
An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning (2021.eacl-main)
Copied to clipboard
| Challenge: | Using a multi-task approach, we extract facts from documents at entity level. |
| Approach: | They propose a multi-task approach that builds upon coreference resolution and gathers relevant signals via multi-instance learning with multi-level representations combining global entity and local mention information. |
| Outcome: | The proposed model is on par with task-specific learning, though more efficient due to shared parameters and training steps. |
Joint Extraction of Entities, Relations, and Events via Modeling Inter-Instance and Inter-Label Dependencies (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing models that perform information extraction tasks manually assume heuristic dependency between the task instances and mean-field factorization for the joint distribution of instance labels. |
| Approach: | They propose to induce a dependency graph among task instances to boost representation learning by estimating their joint distribution via Conditional Random Fields. |
| Outcome: | The proposed model outperforms previous models on multiple IE tasks across 5 datasets and 2 languages. |
Learning Relational Representations by Analogy using Hierarchical Siamese Networks (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to learn representations of relations by textual mentions require a large amount of examples for each relation to reach satisfactory performance. |
| Approach: | They propose a method to learn representations of relations expressed by their textual mentions by matching triples in knowledge bases with web-scale corpora through distant supervision. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on a relation extraction task. |
Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Large pre-trained language models (PLMs) such as GPT-3 have shown strong in-context learning capabilities, which are appealing for domains such as biomedicine that feature high and diverse demands of language technologies but also high data annotation costs. |
| Approach: | They propose to compare the few-shot performance of GPT-3 in-context learning with fine-tuning smaller (i.e., BERT-sized) PLMs on two representative biomedical information extraction tasks: named entity recognition and relation extraction. |
| Outcome: | The proposed model underperforms on two representative biomedical information extraction tasks. |
Benchmarking Large Language Models on CFLUE - A Chinese Financial Language Understanding Evaluation Dataset (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in large language models have revolutionized natural language processing (NLP) there is an urgent need for new benchmarks to keep pace with the development of LLMs. |
| Approach: | They propose a benchmark to assess the capability of large language models (LLMs) they use a dataset to provide both knowledge assessment and application assessment . |
| Outcome: | The proposed benchmark provides datasets tailored for knowledge assessment and application assessment. |
Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies suffer from noise in dependency trees, which can cause confusions in relation extraction. |
| Approach: | They propose a dependency-driven approach for relation extraction with attentive graph convolutional networks . they apply an attention mechanism upon graph convolutional networks to different word dependencies . |
| Outcome: | The proposed approach outperforms previous studies on two English datasets and achieves state-of-the-art performance. |
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to relation extraction use concatenating embeddings of head and tail entities . however, such representations capture the types of the entities involved, leading to false positives and confusion between relations involving entities of the same type. |
| Approach: | They propose a model which combines [MASK] embeddings with entity embedds to learn relation embeddations. |
| Outcome: | The proposed model outperforms the state-of-the-art on several benchmarks . it uses a self-supervised pre-training strategy which further improves the results. |
MRE-MI: A Multi-image Dataset for Multimodal Relation Extraction in Social Media Posts (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to Multimodal Relation Extraction focus on single image scenarios . current approaches focus on text paired with a single image, ignoring valuable insights provided by remaining images. |
| Approach: | They propose a human-annotated dataset that includes multi-image and single-image instances for relation extraction. |
| Outcome: | The proposed model significantly improves relation extraction in multi-image scenarios. |
KEPL: Knowledge Enhanced Prompt Learning for Chinese Hypernym-Hyponym Extraction (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on hypernym-hyponym (“is-a”) relations is mostly in the English language. |
| Approach: | They propose a Knowledge Enhanced Prompt Learning method for Chinese hypernym-hyponym relation extraction using Hearst-like patterns as the prior knowledge. |
| Outcome: | The proposed method is able to extract hypernym-hyponym relations from Chinese unstructured texts using Hearst-like patterns and embed patterns and text simultaneously. |
CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild (2021.emnlp-main)
Copied to clipboard
| 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. |
Modeling Multi-Granularity Hierarchical Features for Relation Extraction (2022.naacl-main)
Copied to clipboard
| 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. |
BioMegatron: Larger Biomedical Domain Language Model (2020.emnlp-main)
Copied to clipboard
Hoo-Chang Shin, Yang Zhang, Evelina Bakhturina, Raul Puri, Mostofa Patwary, Mohammad Shoeybi, Raghav Mani
| Challenge: | Existing studies on domain language models do not study the factors affecting performance on domain languages. |
| Approach: | They empirically evaluate factors that can affect performance on domain language applications . sub-word vocabulary set, model size, pre-training corpus, and domain transfer are important . |
| Outcome: | The results show language models trained on biomedical text perform better on biomedicine benchmarks than those trained on general domain text corpora. |
RTE-GMoE: A Model-agnostic Approach for Relation Triplet Extraction via Graph-based Mixture-of-Expert Mutual Learning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Relation Triplet Extraction (RTE) is a fundamental while challenge task in knowledge acquisition. |
| Approach: | They propose a mutual learning framework for Relation Triplet Extraction to address this limitation. |
| Outcome: | The proposed framework improves on four state-of-the-art backbones and benchmarks. |
An Empirical Study of Pre-trained Transformers for Arabic Information Extraction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Multilingual pre-trained Transformers have been shown to enable effective cross-lingual zero-shot transfer, but their performance on Arabic information extraction tasks is not well studied. |
| Approach: | They pre-train a bilingual BERT that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning. |
| Outcome: | The pre-trained model significantly outperforms mBERT, XLM-RoBERTa, and AraBERT in both the supervised and zero-shot transfer settings. |
An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training (2020.emnlp-main)
Copied to clipboard
| Challenge: | Pre-training large language models is a standard practice in the natural language processing community. |
| Approach: | They propose to use elastic weight consolidation to mitigate catastrophic forgetting when pre-trained large language models are evaluated on generic benchmarks. |
| Outcome: | The proposed model achieves state-of-the-art on out-of domain tasks with minimal pre-training . elastic weight consolidation provides best overall scores yielding only a 0.33% drop in performance across seven generic tasks while remaining competitive in bio-medical tasks. |
Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction (2022.naacl-main)
Copied to clipboard
| 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. |
Improving Distantly-Supervised Relation Extraction with Joint Label Embedding (D19-1)
Copied to clipboard
| Challenge: | Existing methods for relation extraction treat labels as independent and meaningless one-hot vectors, which cause a loss of potential label information for selecting valid instances. |
| Approach: | They propose a multi-layer attention-based model to improve relation extraction with joint label embedding by gating integration and using the embeddable entities as an atten- tion. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods in relation extraction with joint label embedding. |
Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction (D19-1)
Copied to clipboard
| 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. |
JaMIE: A Pipeline Japanese Medical Information Extraction System with Novel Relation Annotation (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing tools for analyzing medical information extraction are limited . empirical results show satisfactory analyzing performance . |
| Approach: | They propose a relation annotation schema for investigating medical and temporal relations in Japanese medical reports. |
| Outcome: | The proposed schema shows that it performs better than existing models and is feasible for high-accuracy applications. |
Easy First Relation Extraction with Information Redundancy (D19-1)
Copied to clipboard
| 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. |
Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction (2022.lrec-1)
Copied to clipboard
| Challenge: | Recent research shows that prompt-based learning improves performance on relation extraction tasks. |
| Approach: | They propose a prompt-based learning method that generates comprehensive prompts for biomedical relation extraction using a ChemProt dataset. |
| Outcome: | The proposed method improves fine-tuning on a biomedical relation extraction task with a cloze-test task and fewer training examples to make reasonable predictions. |
Linguistic representations for fewer-shot relation extraction across domains (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work has demonstrated the positive impact of incorporating linguistic representations as additional context and scaffolds on performance in several NLP tasks. |
| Approach: | They extend previous work to examine whether linguistic representations enhance generalizability . they incorporate syntactic and semantic graphs from off-the-shelf tools into a transformer-based architecture . |
| Outcome: | The proposed approach enhances generalization by providing cross-domain pivots . it also shows that syntactic and semantic graphs exhibit roughly equivalent utility . |
GLiREL - Generalist Model for Zero-Shot Relation Extraction (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing approaches to zero-shot named entity recognition rely on distant supervision and training data for unseen labels. |
| Approach: | They propose an efficient architecture and training paradigm for zero-shot relation classification . they use a protocol to generate multiple relation labels in a single forward pass . |
| Outcome: | The proposed architecture and training paradigm achieve state-of-the-art results on the zero-shot relation classification task. |
REAR: Reinforced Reasoning Optimization for Event Argument Extraction with Relation-Aware Support (2025.findings-emnlp)
Copied to clipboard
| 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. |
Document-level Entity-based Extraction as Template Generation (2021.emnlp-main)
Copied to clipboard
| 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. |
DocNLI: A Large-scale Dataset for Document-level Natural Language Inference (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies focus on sentence-level inference, which limits its application in downstream NLP problems. |
| Approach: | They propose to construct a large-scale dataset for document-level NLI that can be used to study NLP problems. |
| Outcome: | The proposed model performs well on popular sentence-level benchmarks and generalizes well to out-of-domain NLP tasks that rely on inference at document granularity. |
ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning (2021.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models (PTLMs) have achieved noticeable success on many NLP tasks, but struggle for tasks that require event temporal reasoning. |
| Approach: | They propose a continual pre-training approach that equips PTLMs with targeted knowledge about event temporal relations by focusing on masked-out event and temporal indicators and discriminating sentences from their corrupted counterparts. |
| Outcome: | The proposed framework improves the PTLMs’ fine-tuning performances across five relation extraction and question answering tasks and achieves new or on-par state-of-the-art in most of our downstream tasks. |
Named Entity and Relation Extraction with Multi-Modal Retrieval (2022.findings-emnlp)
Copied to clipboard
| 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. |
Open Hierarchical Relation Extraction (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing OpenRE methods cast different relation types in isolation without considering their hierarchical dependency. |
| Approach: | They propose a framework to establish bidirectional connections between OpenRE and relation hierarchies by integrating hierarchy information into relation representations. |
| Outcome: | The proposed framework outperforms state-of-the-art models on relation clustering and hierarchy expansion. |
Explore Unsupervised Structures in Pretrained Models for Relation Extraction (2022.findings-emnlp)
Copied to clipboard
| 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. |
Domain-aware and Co-adaptive Feature Transformation for Domain Adaption Few-shot Relation Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to relation extraction focus on the source domain, which makes it difficult to accurately transfer useful knowledge to the target domain. |
| Approach: | They propose a domain-aware and co-adaptive feature transformation approach to address these issues by leveraging the target domain distribution features to guide the domain-based feature transformations. |
| Outcome: | The proposed method outperforms existing models and achieves state-of-the-art performance on a benchmark dataset. |
Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR) (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing medical social media corpora focus on a small set of entities and relations . existing text mining and information extraction methods focus on scientific text generated by researchers but their access to individual patient experiences or patient-doctor interactions is limited. |
| Approach: | The dataset consists of 2,100 medical tweets with approx. 6,000 entities and 2,200 relations. |
| Outcome: | The proposed dataset consists of 2,100 tweets with approx. 6,000 entities and 2,200 relations. |
Improving Relation Extraction by Sequence-to-sequence-based Dependency Parsing Pre-training (2025.coling-main)
Copied to clipboard
| Challenge: | Existing studies show that dependency information is used only for encoder-only-based relation extraction tasks. |
| Approach: | They propose a syntax-aware seq2seq pre-trained model for relation extraction that incorporates dependency information into a seq2-trained language model by continual pre-training with a dependency parsing task. |
| Outcome: | The proposed model incorporates dependency information into a seq2seq pre-trained language model by continual pre-training with a generative sequence-to-sequence (sequ2sq)-based dependency parsing task. |
Advancing Cross-Lingual Entity Alignment with Large Language Models: Tailored Sample Segmentation and Zero-Shot Prompts (2024.findings-emnlp)
Copied to clipboard
| 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 **Seg**mentation, 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. |
CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction (2021.acl-long)
Copied to clipboard
| 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. |
RE2: Region-Aware Relation Extraction from Visually Rich Documents (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing studies on relation extraction from visually rich documents focus on layout structure and Optical Character Recognition (OCR) results. |
| Approach: | They propose a relation extraction tool that leverages layout structure among entity blocks to improve relation prediction. |
| Outcome: | The proposed model outperforms existing models on a wide range of domains and languages. |
SENT: Sentence-level Distant Relation Extraction via Negative Training (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for relation extraction use bag labels, which introduce noise, to train the model. |
| Approach: | They propose to use negative training to train a model using complementary labels to separate the noisy data from the training data. |
| Outcome: | The proposed method improves on previous methods on sentence-level evaluation and de-noise effect. |
HARE: an entity and relation centric evaluation framework for histopathology reports (2025.findings-emnlp)
Copied to clipboard
| 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. |
Effects of Human Adversarial and Affable Samples on BERT Generalization (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Limiting quantities of training data is considered a key impediment to achieving generalizability in machine learning. |
| Approach: | They examine the impact of training data quality, not quantity, on a model’s generalizability by comparing human-adversarial and human-affable training samples. |
| Outcome: | The proposed model performance improves with 10-30% h-adversarial instances in text classification and relation extraction tasks. |
Query-based Instance Discrimination Network for Relational Triple Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent approaches to extract relational triples from open domain texts suffer from error propagation, relation redundancy and lack of high-level connections. |
| Approach: | They propose a query-based approach to construct instance-level representations for relational triples . they use query embeddings and token embeddables to extract all types of triples in one step . |
| Outcome: | The proposed method achieves state-of-the-art on five widely used benchmarks. |
Span-Level Model for Relation Extraction (P19-1)
Copied to clipboard
| Challenge: | Recent approaches for this span-level task have inherent limitations. |
| Approach: | They propose a model which directly models all possible spans and performs joint entity mention detection and relation extraction. |
| Outcome: | The proposed model performs joint entity mention detection and relation extraction on the ACE2005 dataset. |
Building a Multimodal Entity Linking Dataset From Tweets (2020.lrec-1)
Copied to clipboard
| Challenge: | Entity linking is a task that aims at associating an entity mention with a unique entity in a knowledge base. |
| Approach: | They propose a method to quasi-automatically build annotated datasets to evaluate methods on the Entity Linking task. |
| Outcome: | The proposed method builds annotated datasets of tweets with ambiguous mentions and a Twitter KB defining the entities. |
Unexpected Phenomenon: LLMs’ Spurious Associations in Information Extraction (2024.findings-acl)
Copied to clipboard
Weiyan Zhang, Wanpeng Lu, Jiacheng Wang, Yating Wang, Lihan Chen, Haiyun Jiang, Jingping Liu, Tong Ruan
| Challenge: | Information extraction (IE) tasks require a limited number of example instructions to achieve effective performance. |
| Approach: | They propose two strategies to find spurious associations in large language models (LLMs) they use forward label extension and backward label validation to leverage extended labels to improve model performance. |
| Outcome: | The proposed methods improve performance on Chinese and English datasets and 9.55%, 11.42%, and 21.27% in F1 scores on SciERC, ACE05, and DuEE datasets. |
Regularized Attentive Capsule Network for Overlapped Relation Extraction (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods to extract relations from distant supervision contain low-quality instances with noisy words and overlapped relations. |
| Approach: | They propose a Regularized Attentive Capsule Network to better identify overlapped relations in informal sentences . they embed multi-head attention into the capsule network as the low-level capsules . |
| Outcome: | Extensive experiments show that the proposed model improves relation extraction. |
Incorporating Global Contexts into Sentence Embedding for Relational Extraction at the Paragraph Level with Distant Supervision (L18-1)
Copied to clipboard
| 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. |
Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction (2020.coling-main)
Copied to clipboard
| Challenge: | Existing graph convolutional networks use pruned dependency trees to filter irrelevant nodes from sentence graphs. |
| Approach: | They propose to construct multiple sub-graphs from shortest dependency path and words linked to entities in the dependency parse to obtain more informative features useful for relation extraction. |
| Outcome: | The proposed method achieves state-of-the-art performance on a sentence-level relation extraction dataset and the SemEval 2010 Task 8 sentence- level relation extraction data. |
Revisiting Distant Supervision for Relation Extraction (L18-1)
Copied to clipboard
| 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 . |
Evaluating Generative Language Models in Information Extraction as Subjective Question Correction (2024.lrec-main)
Copied to clipboard
| Challenge: | Modern large language models (LLMs) perform poorly in elementary tasks like relation extraction and event extraction due to two issues in conventional evaluation methods. |
| Approach: | They propose a method to evaluate large language models by incorporating a human annotation schema. |
| Outcome: | The proposed evaluation method improves matching between model outputs and golden labels. |
Learning to Leverage High-Order Medical Knowledge Graph for Joint Entity and Relation Extraction (2023.findings-acl)
Copied to clipboard
| Challenge: | Medical terms are difficult to understand and relations between medical entities become complicated. |
| Approach: | They propose to leverage medical domain knowledge for extracting entities and relations for Chinese medical texts by building a heterogeneous graph based on medical knowledge graph. |
| Outcome: | The proposed method is more effective than state-of-the-art methods on real Chinese medical texts. |
Relation Extraction with Explanation (2020.acl-main)
Copied to clipboard
| Challenge: | Recent studies focus on improving relation extraction accuracy but little is known about their explanability. |
| Approach: | They propose to automatically generate "distractor" sentences to augment the bags and train the model to ignore the distractors. |
| Outcome: | The proposed model improves extraction accuracy while also explanability. |
Entity, Relation, and Event Extraction with Contextualized Span Representations (D19-1)
Copied to clipboard
| Challenge: | Existing frameworks for named entity recognition, relation extraction, and event extraction can be easily adapted for new tasks or datasets. |
| Approach: | They propose a framework that enumerates, refins, and scores text spans to capture local (within-sentence) and global (cross-sentent) context. |
| Outcome: | The proposed framework achieves state-of-the-art results on four datasets from a variety of domains. |
C-ICL: Contrastive In-context Learning for Information Extraction (2024.findings-emnlp)
Copied to clipboard
| 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. |
Enhanced Distant Supervision with State-Change Information for Relation Extraction (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for enhancing distant supervision with state-change information for relation extraction are limited. |
| Approach: | They propose a method for enhancing distant supervision with state-change information for relation extraction by adding temporal information to a curation dataset. |
| Outcome: | The proposed method reduces noise when used for static relation extraction and can be used to train a relation-extraction system that detects a change of state in relations. |
Distantly-Supervised Joint Extraction with Noise-Robust Learning (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to identifying entity pairs and relations with a single model are noisy . Existing methods only consider one source of noise or make decisions using external knowledge . |
| Approach: | They propose a framework that aligns entity mentions with corresponding tags for joint extraction . they propose DENRL, which employs a lightweight transformer backbone for joint tagging . |
| Outcome: | The proposed framework outperforms baseline models on two benchmark datasets with better interpretability. |
Federated Document-Level Biomedical Relation Extraction with Localized Context Contrast (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies on relation extraction focus on document-level training without sharing raw medical texts. |
| Approach: | They propose a federated framework for relation extraction that enables collaborative training without sharing raw medical texts. |
| Outcome: | The proposed framework extends document-level relation extraction to a federated environment. |
ITER: Iterative Transformer-based Entity Recognition and Relation Extraction (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in NLP generate structured information in an autoregressive manner, causing low throughput . authors propose an efficient encoder-based relation extraction model that performs the task in three parallelizable steps. |
| Approach: | They propose an efficient encoder-based relation extraction model that performs the task in three parallelizable steps. |
| Outcome: | The proposed model achieves state-of-the-art on two datasets and is faster than existing models. |
NCRE: A Benchmark for Document-level Nominal Compound Relation Extraction (2025.coling-main)
Copied to clipboard
| Challenge: | Existing work focuses on detecting specific relations between entities, often constrained to specific fields and lacking general applicability. |
| Approach: | They propose a novel task that concentrates on abstract relation extraction between noun phrases . they annotate a Chinese dataset and develop a model incorporating a rotary position-enhanced word pair detection schema. |
| Outcome: | The proposed task is more efficient than previous methods. |
Boosting Document-Level Relation Extraction by Mining and Injecting Logical Rules (2022.emnlp-main)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) aims to extract relations of all entity pairs in document. |
| Approach: | They propose a logic enhanced framework that boosts DocRE by mining and injecting logic rules. |
| Outcome: | The proposed framework outperforms LogiRE on two benchmarks. |
When Phrases Meet Probabilities: Enabling Open Relation Extraction with Cooperating Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing clustering-based open relation extraction methods use pre-trained language models . embeddings from language models are high-dimensional and anisotropic, so there is a gap . |
| Approach: | They propose a framework that makes two LLMs work collaboratively to achieve clustering. |
| Outcome: | The proposed framework outperforms existing methods by 1.4%3.13% on different datasets. |
CROSSAGENTIE: Cross-Type and Cross-Task Multi-Agent LLM Collaboration for Zero-Shot Information Extraction (2025.findings-acl)
Copied to clipboard
| 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 . |
TAGPRIME: A Unified Framework for Relational Structure Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models for natural language processing (NLP) do not address common tasks. |
| Approach: | They propose to take a unified view of all the tasks and introduce a model that appends priming words about the condition to the input text. |
| Outcome: | The proposed model is based on ten datasets across five different languages and covers ten tasks that cover ten languages. |
Towards relation extraction from speech (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for extracting relations from speech have been neglected due to the nature of speech. |
| Approach: | They propose a listening information extraction task that uses speech to extract relation extraction from speech . they use a text-to-speech system and crowd-sourced native English speakers to test the task . |
| Outcome: | The proposed task extracts semantic relationships from speech data using a new model . the proposed task is more challenging than the existing method due to the characteristics of speech . |
Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modelling (2025.emnlp-main)
Copied to clipboard
Sydney Anuyah, Mehedi Mahmud Kaushik, Sri Rama Krishna Reddy Dwarampudi, Rakesh Shiradkar, Arjan Durresi, Sunandan Chakraborty
| 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. |
CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models pre-trained on massive corpora have shown impressive few-shot learning ability on many NLP tasks. |
| Approach: | They propose to recast structured output in the form of code instead of natural language and use generative LLMs of code to perform IE tasks. |
| Outcome: | The proposed method outperforms fine-tuning moderate-size pre-trained models and prompting NL-LLMs under few-shot settings. |
LLMaAA: Making Large Language Models as Active Annotators (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing supervised learning methods in natural language processing require large amounts of data. |
| Approach: | They propose an active learning loop that takes LLMs as annotators and puts them into an active loop to determine what to annotate efficiently. |
| Outcome: | The proposed model outperforms existing models with few-shot performance in two NLP tasks. |
UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches for information extraction (IE) are limited by the number of subtasks and the isolation of the subtask. |
| Approach: | They propose a new paradigm for universal information extraction that is compatible with any schema format and applicable to a list of IE tasks. |
| Outcome: | The proposed framework outperforms generative universal IE models on 14 benchmarks with the supervised setting and the state-of-the-art performance in low-resource scenarios. |
Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding (2023.emnlp-main)
Copied to clipboard
| Challenge: | Chain-of-Thought (CoT) is a technique that guides large language models to decompose complex tasks into multi-step reasoning processes. |
| Approach: | They propose a two-step reasoning framework based on prompt tuning to implement step-by-step thinking for MLMs on NLU tasks. |
| Outcome: | The proposed framework outperforms baselines and achieves state-of-the-art performance on two NLU tasks. |
Guideline Learning for In-Context Information Extraction (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models can perform a task by conditioning on task instructions and a few input-output examples without optimizing any parameters. |
| Approach: | They propose a guideline learning framework for In-context IE which reflectively learns and follows guidelines. |
| Outcome: | The proposed framework improves the performance of in-context IE by synthesizing and following guidelines. |
MaintIE: A Fine-Grained Annotation Schema and Benchmark for Information Extraction from Maintenance Short Texts (2024.lrec-main)
Copied to clipboard
| Challenge: | Maintenance short texts (MSTs) provide crucial insights into the state and maintenance activities of machines, infrastructure, and other engineered assets. |
| Approach: | They propose a multi-level fine-grained annotation scheme for entity recognition and relation extraction that includes 5 top-level classes and 6 relations tailored to MSTs. |
| Outcome: | The proposed scheme provides high-quality, fine-grained annotations and a coarse-grain corpus of 7,000 texts. |
Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Standard English and Malaysian English exhibit significant differences in morphosyntactic variations . existing datasets are not sufficient to enhance NLP tasks in Malaysian english . |
| Approach: | They propose to use a Malaysian English news article dataset to refine NER models for Malaysian english. |
| Outcome: | The proposed dataset can improve the performance of NER on Malaysian English. |
Context-faithful Prompting for Large Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models encode parametric knowledge about world facts but overly rely on it can cause incorrect predictions in context-sensitive NLP tasks. |
| Approach: | They propose to use opinion-based prompts and counterfactual demonstrations to improve LLM faithfulness to contexts. |
| Outcome: | The proposed methods improve faithfulness to contexts using opinion-based prompts and counterfactual demonstrations. |
LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases (2026.acl-long)
Copied to clipboard
Yida Cai, Ranjuexiao Hu, Huiyuan Xie, Chenyang Li, Yun Liu, Yuxiao Ye, Zhenghao Liu, Weixing Shen, Zhiyuan Liu
| Challenge: | Legal relations are an important analytical framework for dispute resolution in civil cases. |
| Approach: | They propose a comprehensive schema for legal relations in civil cases with hierarchical taxonomy and definitions of arguments. |
| Outcome: | The proposed schema shows that existing LLMs lack the ability to identify civil legal relations and performance improves on downstream tasks. |
MixRED: A Mix-lingual Relation Extraction Dataset (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing research focuses on monolingual relation extraction, but there is a significant gap in understanding relation extraction in the mix-lingual scenario. |
| Approach: | They propose a task of considering relation extraction in the mix-lingual scenario . they construct a human-annotated dataset to support the task . |
| Outcome: | The proposed task evaluates state-of-the-art supervised models and large language models on the human-annotated dataset MixRED. |
A Causal View of Entity Bias in (Large) Language Models (2023.findings-emnlp)
Copied to clipboard
| 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. |
Closed Boundary Learning for Classification Tasks with the Universum Class (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods treat the Universum class equally with the classes of interest, leading to problems such as overfitting, misclassification, and diminished model robustness. |
| Approach: | They propose a closed boundary learning method that applies closed decision boundaries to classes of interest and designates the area outside all closed boundaries as the Universum class. |
| Outcome: | The proposed method improves accuracy and robustness of classification models on six state-of-the-art tasks. |
Pointing Out the Shortcomings of Relation Extraction Models with Semantically Motivated Adversarials (2024.lrec-main)
Copied to clipboard
| 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. |
PolyNERE: A Novel Ontology and Corpus for Named Entity Recognition and Relation Extraction in Polymer Science Domain (2024.lrec-main)
Copied to clipboard
| 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 . |
Synergetic Interaction Network with Cross-task Attention for Joint Relational Triple Extraction (2024.lrec-main)
Copied to clipboard
| 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. |
Dependency Parsing-Based Syntactic Enhancement of Relation Extraction in Scientific Texts (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a pipeline approach to extract entities and relations from scientific text is challenging due to long sentences with densely packed entities. |
| Approach: | They propose a syntactic filtering method that prunes unlikely entity pairs before relation prediction. |
| Outcome: | The proposed method improves Rel+ F1 scores on SciERC, SciER, and ACE05 datasets. |