Papers with relation extraction tasks
Automatic Rule Induction for Efficient Semi-Supervised Learning (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to generalize from labeled and unlabeled data are difficult to explain and behave unreliably. |
| Approach: | They propose a framework for automatic discovery and integration of symbolic rules into pretrained transformer models by using an attention mechanism. |
| Outcome: | The proposed framework can improve state-of-the-art methods with no manual effort and minimal computational overhead. |
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. |
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. |
Self-Attention Enhanced CNNs and Collaborative Curriculum Learning for Distantly Supervised Relation Extraction (D19-1)
Copied to clipboard
| Challenge: | Distantly Supervised Relation Extraction (DSRE) suffers from mislabelled data . human annotation on large datasets is costly and often impossible . |
| Approach: | They propose a model that employs a collaborative curriculum learning framework to reduce mislabelled data. |
| Outcome: | The proposed model outperforms baselines including state-of-the-art in terms of P@N and PR curve metrics on a widely-used public dataset. |
Joint Constrained Learning with Boundary-adjusting for Emotion-Cause Pair Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Emotion-Cause Pair Extraction (ECPE) aims to identify the document’s emotion clauses and corresponding cause clauses. |
| Approach: | They propose a constrained learning framework with boundary-adjusting for Emotion-Cause Pair Extraction that summarizes prior rules and forces the model to take them into consideration in optimization. |
| Outcome: | The proposed framework achieves competitive results compared with state-of-the-art methods on unbalanced data and proves robustness on unbalancing data. |
Principles from Clinical Research for NLP Model Generalization (2024.naacl-long)
Copied to clipboard
| Challenge: | In clinical research, generalizability depends on (a) internal validity of experiments and (b) external validity or transportability of the results to the wider population. |
| Approach: | They propose to ensure internal validity when building machine learning models in NLP by incorporating learning spurious correlations into their models. |
| Outcome: | The proposed model can perform well on data unseen during training, but drawn from the same distribution or population. |
CoVariance-based Causal Debiasing for Entity and Relation Extraction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Named Entity Recognition and Relation Extraction are key tasks of Information Extraction. |
| Approach: | They propose a causal framework called c ovariance and variance optimization framework (OVO) to optimize feature representations and conduct general debiasing. |
| Outcome: | The proposed framework minimizes characterizing features’ covariance for alleviating selection and distribution bias and enhances feature representation in the feature space. |
Training Classifiers with Natural Language Explanations (P18-1)
Copied to clipboard
| Challenge: | a semantic parser converts explanations into programmatic labeling functions . a standard protocol for obtaining a labeled dataset provides only one bit of information per example . |
| Approach: | They propose a framework where an annotator provides an explanation for each labeling decision . they use a semantic parser to convert these explanations into programmatic labeling functions . |
| Outcome: | The proposed framework trains classifiers faster by providing explanations instead of labels . the proposed framework is based on a rule-based semantic parser . |
ExpBERT: Representation Engineering with Natural Language Explanations (2020.acl-main)
Copied to clipboard
| Challenge: | Existing models with explanations use semantic parsers to interpret explanations, but they can only parse low-level statements like "'wife' appears between o 1 and 2 " |
| Approach: | They propose to use natural language explanations to specify inductive biases . they use BERT fine-tuned on MultiNLI to "interpret" the explanations with respect to the input sentence . |
| Outcome: | The proposed method matches a baseline with less labeled data and improves on the baseline with the same amount of labele. |
Document-Level Zero-Shot Relation Extraction with Entity Side Information (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing approaches rely on Large Language Models (LLMs) to generate synthetic data for unseen labels. |
| Approach: | They propose a document-level zero-shot relation extraction framework with Entity Side Information to solve existing problems. |
| Outcome: | The proposed approach achieves an average improvement of 11.6% in the macro F1-Score compared to baseline models and existing benchmarks. |
Entity Relation Extraction as Dependency Parsing in Visually Rich Documents (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on key information extraction from visually rich documents focus on labeling the text within bounding boxes, while relations between words are unexplored. |
| Approach: | They propose to use a dependency parsing model to extract semantic entities from visually rich documents by combining entity labeling and relation extraction tasks. |
| Outcome: | The proposed model achieves 65.96% F1 score on the FUNSD dataset. |
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. |
Information Extraction from Legal Wills: How Well Does GPT-4 Do? (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Using information extraction from legal wills is an important application of artificial intelligence (AI) |
| Approach: | They propose a manually annotated dataset for Information Extraction (IE) from legal wills . they also use it to evaluate the performance of large language models (LLMs) |
| Outcome: | The proposed dataset can be used to evaluate large language models on IE from legal wills . it shows that the model performs reasonably well, but inconsistent outputs and overgeneralization are observed . |
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. |
Rethinking the Role of LLMs for Document-level Relation Extraction: a Refiner with Task Distribution and Probability Fusion (2025.naacl-long)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) provides a broad context for extracting relations for entities. |
| Approach: | They propose a method that utilizes LLMs as a refiner and task distribution and probability fusion to refine LLM-based relation extraction methods. |
| Outcome: | The proposed method outperforms existing LLM-based methods without fine-tuning by 25.2% F1. |
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. |
Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for information extraction from biomedical texts do not utilize external knowledge . despite the exponential growth of biomedically published articles, many existing methods fall behind . |
| Approach: | They propose a framework that utilizes external knowledge for entity and relation extraction . KECI uses an initial span graph to construct a knowledge graph containing relevant background knowledge . |
| Outcome: | The proposed framework achieves state-of-the-art results in two biomedical datasets . it achieves 4.59% and 4.91% improvement in F1 scores over the state- of-the art methods . |
Conservative Bias in Large Language Models: Measuring Relation Predictions (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) exhibit pronounced conservative bias in relation extraction tasks, often defaulting to no_relation label when an appropriate option is unavailable. |
| Approach: | They systematically evaluate the trade-off between conservative bias and hallucination in relation extraction tasks by using SBERT and LLM prompts to quantify this effect. |
| Outcome: | The proposed model defaults to no_relation label twice as often as hallucination, resulting in significant information loss when reasoning is not explicitly included in the output. |