Entity-Relation Extraction as Multi-Turn Question Answering

TL;DR

This paper introduces a novel multi-turn question answering framework for entity-relation extraction, leveraging MRC models to improve accuracy and jointly model entities and relations, achieving state-of-the-art results on multiple datasets.

Contribution

The paper presents a new paradigm that reformulates entity-relation extraction as a multi-turn QA task, enabling better modeling and improved performance.

Findings

Achieved state-of-the-art results on ACE04, ACE05, and CoNLL04 datasets.

Significantly outperformed previous models in entity-relation extraction.

Demonstrated effectiveness on a newly developed Chinese dataset RESUME.

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.