Papers with Relation extraction
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. |
Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping (D19-1)
Copied to clipboard
| Challenge: | Relation extraction (RE) is an important information extraction task that seeks to detect and classify semantic relationships between entities. |
| Approach: | They propose a bilingual word embedding mapping approach for cross-lingual RE model transfer . they use a small bilingual dictionary with only 1K word pairs to embed word pairs . |
| Outcome: | The proposed approach achieves very good performance on target and target languages . it uses bilingual word embedding mapping to transfer a source-language model . |
Semi-supervised Relation Extraction via Data Augmentation and Consistency-training (2023.eacl-main)
Copied to clipboard
| Challenge: | Obtaining high-quality human labelled data is an expensive and noisy process. |
| Approach: | They propose to leverage unlabelled data to improve the sample efficiency of the models. |
| Outcome: | The proposed methods can be used to extract the Cause-Effect relation between a given head entity and tail entity based on context in the input sentence. |
Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing RE models are incapable of handling implicit expressions and long-tail relation types due to language complexity and data sparsity. |
| Approach: | They propose a method to enhance relation extraction using k nearest neighbors (kNN-RE) kNN is a nearest-neighbor search tool that allows the model to consult training relations at test time . |
| Outcome: | The proposed model outperforms the best model to date on ACE05, SciERC, and Wiki80 datasets and outperformed the best on i2b2 and Wik80 dataset. |
Relation Extraction using Explicit Context Conditioning (N19-1)
Copied to clipboard
| Challenge: | Existing methods for relation extraction fail to capture complex and long dependencies . end-to-end models that learn both NER and RE can solve this problem . |
| Approach: | They propose to use second-order relations to compute relation scores for relation extraction (RE) . they propose to combine second- and first-order relation scores to obtain final relation scores . |
| Outcome: | The proposed method leads to state-of-the-art performance over two biomedical datasets. |
Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing studies require modifications to existing baseline architectures to leverage syntactic information. |
| Approach: | They propose to leverage syntactic information to improve relation extraction by training a syntax-induced encoder on auto-parsed data through dependency masking. |
| Outcome: | The proposed approach outperforms baseline models and achieves state-of-the-art results on two English datasets. |
MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction (2022.coling-1)
Copied to clipboard
| Challenge: | Relation extraction in the biomedical domain is challenging due to the lack of labeled data and high annotation costs. |
| Approach: | They propose to use distant supervision to pair knowledge graph relationships with raw texts to tackle the scarcity of annotated data and to validate their results. |
| Outcome: | The proposed benchmarks are more accurate and consistent with existing benchmarks and show that there is no train-test leakage. |
MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset (2023.acl-long)
Copied to clipboard
| Challenge: | Relation extraction (RE) is a fundamental task in information extraction, but its extension to multilingual settings is hindered by the lack of supervised resources comparable in size to large English datasets. |
| Approach: | They propose a dataset to analyze relation extraction (RE) in multilingual settings . they find machine translation is a viable strategy to transfer RE instances . |
| Outcome: | The proposed dataset covers 12 typologically diverse languages from 9 language families and is compared with existing datasets. |
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. |
Knowledge-Driven Cross-Document Relation Extraction (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to extract relationships between entities are based on sentence-level tasks, but they do not consider domain knowledge, which are assumed to be known to the reader when documents are authored. |
| Approach: | They propose to embed domain knowledge of entities with input text for cross-document RE by embedding domain knowledge with the document. |
| Outcome: | The proposed framework offers interpretability by producing explanatory text for predicted relations between entities and improves performance over baseline methods. |
HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications (2021.findings-acl)
Copied to clipboard
Qiao Cheng, Juntao Liu, Xiaoye Qu, Jin Zhao, Jiaqing Liang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Yanghua Xiao
| Challenge: | Relation extraction (RE) is an essential topic in natural language processing and has attracted extensive attention. |
| Approach: | They propose a case-oriented construction framework to build a hard case relation extraction dataset with 65,225 relational facts annotated from 9,231 documents. |
| Outcome: | The proposed model achieves a high 96% F1 score on data quality and is far lower than humans. |
Global-to-Local Neural Networks for Document-Level Relation Extraction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Relation extraction (RE) aims to identify the semantic relations between named entities in text. |
| Approach: | They propose a novel relation extraction model that encodes document information in terms of entity global and local representations and context relation representations. |
| Outcome: | The proposed model achieves superior performance on two public datasets for document-level RE. |
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. |
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. |
Combinatory Grammar Tells Underlying Relevance among Entities (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches focus on dependencies among words while paying limited attention to other types of syntactic structure. |
| Approach: | They propose an alternative approach that takes advantage of combinatory categorial grammar to detect the relation between entities. |
| Outcome: | The proposed model performs state-of-the-art on two widely used English benchmark datasets. |
Revisiting Large Language Models as Zero-shot Relation Extractors (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies show that large language models (LLMs) transfer well to new tasks out-of-the-box . relationship extraction (RE) involves a certain degree of labeled or unlabeled data even under zero-shot setting. |
| Approach: | They propose a simple prompt recursively using LLMs to transform RE inputs to QA format . they propose qq prompting and qt prompting to improve their results . |
| Outcome: | The proposed method improves on different model sizes, benchmarks and settings. |
The State of Relation Extraction Data Quality: Is Bigger Always Better? (2024.findings-acl)
Copied to clipboard
| Challenge: | Relation extraction (RE) methods extract tuples of relationships from text . many datasets with frequent label errors have been used . |
| Approach: | They review recent surveys and a sample of recent RE methods papers . they find that real-time evaluations of RE methods are possible . |
| Outcome: | a sample of 38 datasets currently being used shows that many have frequent label errors . a small number of relations in specific domains can more realistically evaluate methods . |
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. |
Summarization as Indirect Supervision for Relation Extraction (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Relation extraction (RE) models rely on training data with expensive annotations . et al., 2018; Zhao e.t al, 2018) . |
| Approach: | They propose a method that converts RE into a summarization formulation by using constraint decoding techniques. |
| Outcome: | The proposed method improves relation extraction models with high-resource and high-contrast inferences. |
Reasoning Makes Good Annotators : An Automatic Task-specific Rules Distilling Framework for Low-resource Relation Extraction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to extract knowledge from unlabeled data generate noise labels. |
| Approach: | They propose an automatic task-specific rules distilling framework to generate a logic rule from unlabeled data. |
| Outcome: | The proposed framework could power the labeling ability by discovering reliable model-labeled data. |
Reward-based Input Construction for Cross-document Relation Extraction (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for relation extraction are limited to a single sentence or document, but cross-document RE has emerged to address relations across multiple documents. |
| Approach: | They propose a sentence selector that extracts sentences based on relational evidence and rewards it with RE prediction scores. |
| Outcome: | The proposed method outperforms heuristic methods for relation extraction in multiple document scenarios. |
Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing relation extraction models rely on supervised machine learning, but many datasets are incompletely annotated, causing false negatives and errors during inference stage. |
| Approach: | They propose a class-adaptive re-sampling self-training framework that favored the pseudo-labels of classes with high precision and low recall scores. |
| Outcome: | The proposed framework outperforms existing methods on the Re-DocRED and ChemDisgene datasets when the training data are incompletely annotated. |
Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing studies on relation extraction (RE) use labeled training data for relation extraction models but it is expensive and time-consuming. |
| Approach: | They propose a dual supervision framework which utilizes both types of data to train relation extraction models. |
| Outcome: | The proposed framework can predict labels by human annotation and distant supervision without labeling bias since it is expensive and time-consuming. |
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing work adopts data augmentation techniques to generate pseudo-annotated sentences . existing methods neither preserve semantic consistency of original sentences nor preserve syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations. |
| Approach: | They propose a dedicated augmentation technique for relational texts, named GDA, which uses two complementary modules to preserve both semantic consistency and syntax structures. |
| Outcome: | The proposed technique can bring 2.0% F1 improvements in three datasets under low-resource setting. |
Enhancing Relation Extraction via Adversarial Multi-task Learning (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing studies have focused on re-modeling the given NEs and thus lead to inferior results when NE is sometimes ambiguous. |
| Approach: | They propose a relation extraction model with two training stages that uses adversarial multi-task learning to recover the given NEs. |
| Outcome: | The proposed model improves on two English benchmark datasets and shows state-of-the-art performance. |
A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods do not consider qualifier attributes for each relation triplet, such as time, quantity or location. |
| Approach: | They propose a hyper-relational extraction task to extract more specific facts from text using qualifiers. |
| Outcome: | The proposed model outperforms baselines and reveal possible directions for future research. |
Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to extract relationships are limited to English and require annotating datasets in order to be expensive and time-consuming. |
| Approach: | They apply guided distant supervision to create a large biographical relationship extraction dataset for German using 80,000 instances for nine relationship types. |
| Outcome: | The proposed dataset is the largest biographical German relationship extraction dataset. |
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 . |
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Relation extraction (RE) has been challenging in low-resource domains and with limited resources. |
| Approach: | They propose to pretrain and finetune the RE model using consistent objectives of contrastive learning. |
| Outcome: | The proposed method outperforms PLM-based RE classifier on two document-level RE datasets. |
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing Relation extraction models require extensive annotated training data, which is costly and labor-intensive to collect. |
| Approach: | They propose a new zero-shot RE task where only relation definitions are provided instead of seen-unseen relation instances. |
| Outcome: | The proposed task significantly improves cost-effective zero-shot performance by large margins. |
Revisiting Relation Extraction in the era of Large Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Standard supervised approaches to RE learn to tag tokens comprising entity spans and then predict the relationship between them. |
| Approach: | They propose to use large language models for RE to evaluate their performance . they use GPT-3 and Flan-T5 large to train RE . |
| Outcome: | The proposed model outperforms existing models on a sequence-to-sequence task under varying levels of supervision. |
EDeR: Towards Understanding Dependency Relations Between Events (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on event relation extraction focuses on hierarchical, temporal and causal relations but ignores the interdependence between events. |
| Approach: | They propose to use a human-annotated Event Dependency Relation dataset to identify event dependency relations between two events. |
| Outcome: | The proposed dataset integrates existing annotations with the OntoNotes dataset and shows that recognizing such event dependency relations can further benefit critical NLP tasks, including semantic role labelling and co-reference resolution. |
Retrieval over Classification: Integrating Relation Semantics for Multimodal Relation Extraction (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to multimodal relation extraction ignore structural constraints and lack semantic expressiveness for fine-grained relation understanding. |
| Approach: | They propose a framework that reformulates multimodal relation extraction as a retrieval task driven by relation semantics. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the benchmark datasets MNRE and MORE and exhibits stronger robustness and interpretability. |
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. |
R1-RE: Cross-Domain Relation Extraction with RLVR (2026.acl-long)
Copied to clipboard
| Challenge: | Relation extraction (RE) is a core task in natural language processing. |
| Approach: | They propose a supervised learning task for relation extraction (RE) based on annotation guidelines. |
| Outcome: | The proposed model achieves an average OOD accuracy of 70%, on par with leading proprietary models such as GPT-4o. |
WojoodRelations: Arabic Relation Extraction Corpus and Modeling (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on Arabic RE remains limited due to the language’s rich morphology and syntactic complexity, and the lack of large, high-quality datasets. |
| Approach: | They propose to use WojoodRelations to extract relation relationships from Arabic textual data using relation-aware templates and GPT-Joint to perform relation-based retrieval. |
| Outcome: | The proposed method achieves a Cohen’s of 0.92, indicating high reliability, and supervised models achieve 92.89% F1 for RE, while LLMs obtain 72.73% F1 . |
On the Role of Discriminative Models in Generative Relation Extraction (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for relation extraction (RE) are discriminative and generative . previous studies show that discriminative models can support generative RE . |
| Approach: | They propose a framework that leverages discriminative models to produce a top-k set of candidate relations and integrates this knowledge into generative models via in-context or prompt learning. |
| Outcome: | The proposed framework achieves state-of-the-art on five widely used RE benchmarks. |
Prompt Optimization for Relation Extraction using Reinforcement Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing prompt-based methods rely heavily on large-scale annotated datasets limiting their applicability in domain-specific and low-resource scenarios. |
| Approach: | They propose a reinforcement learning-based automated prompt optimization framework for domain relation extraction that optimizes prompt quality through interaction with a black-box LLM. |
| Outcome: | The proposed framework outperforms existing prompt-based methods and supervised baselines on multiple extraction datasets across medical, financial, legal, and news domains. |