Papers › Entity-Relation Extraction as Multi-Turn Question Answering
Entity-Relation Extraction as Multi-Turn Question Answering
Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, Jiwei Li
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Relation Extraction | ACE 2004 | Multi-turn QA | Cross Sentence | No | #5 of 11 | Archive leaderboard | report |
| Relation Extraction | ACE 2004 | Multi-turn QA | NER Micro F1 | 83.6 | #5 of 11 | Archive leaderboard | report |
| Relation Extraction | ACE 2004 | Multi-turn QA | RE+ Micro F1 | 49.4 | #5 of 11 | Archive leaderboard | report |
| Relation Extraction | ACE 2005 | Multi-turn QA | Cross Sentence | No | #17 of 30 | Archive leaderboard | report |
| Relation Extraction | ACE 2005 | Multi-turn QA | NER Micro F1 | 84.8 | #17 of 30 | Archive leaderboard | report |
| Relation Extraction | ACE 2005 | Multi-turn QA | RE+ Micro F1 | 60.2 | #17 of 30 | Archive leaderboard | report |
| Relation Extraction | ACE 2005 | Multi-turn QA | Sentence Encoder | BERT base | #17 of 30 | Archive leaderboard | report |
| Relation Extraction | CoNLL04 | Multi-turn QA | NER Micro F1 | 87.8 | #14 of 16 | Archive leaderboard | report |
| Relation Extraction | CoNLL04 | Multi-turn QA | RE+ Micro F1 | 68.9 | #14 of 16 | Archive leaderboard | report |
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