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 stateof-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. 1