Entity-Relation Extraction as Multi-Turn Question Answering

Abstract

In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and relations is transformed to the task of identifying answer spans from the context. This multi-turn QA formalization comes with several key advantages: firstly, the question query encodes important information for the entity/relation class we want to identify; secondly, QA provides a natural way of jointly modeling entity and relation; and thirdly, it allows us to exploit the well developed machine reading comprehension (MRC) models. Experiments on the ACE and the CoNLL04 corpora demonstrate that the proposed paradigm significantly outperforms previous best models. We are able to obtain the state-of-the-art results on all of the ACE04, ACE05 and CoNLL04 datasets, increasing the SOTA results on the three datasets to 49.4 (+1.0), 60.2 (+0.6) and 68.9 (+2.1), respectively. Additionally, we construct a newly developed dataset RESUME in Chinese, which requires multi-step reasoning to construct entity dependencies, as opposed to the single-step dependency extraction in the triplet exaction in previous datasets. The proposed multi-turn QA model also achieves the best performance on the RESUME dataset.

to appear at ACL2019

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Cited by in corpus (11)

• Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey
• Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy
• Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond
• Description Based Text Classification with Reinforcement Learning
• End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models
• Deeper Task-Specificity Improves Joint Entity and Relation Extraction
• A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land?
• BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction
• Deep Neural Networks for Relation Extraction
• A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction
• CalibreNet: Calibration Networks for Multilingual Sequence Labeling