Collective prompt tuning with relation inference for document-level relation extraction

Publication Type

Journal Article

Publication Date

9-2023

Abstract

Document-level relation extraction (RE) aims to extract the relation of entities that may be across sentences. Existing methods mainly rely on two types of techniques: Pre-trained language models (PLMs) and reasoning skills. Although various reasoning methods have been proposed, how to elicit learnt factual knowledge from PLMs for better reasoning ability has not yet been explored. In this paper, we propose a novel Collective Prompt Tuning with Relation Inference (CPT-RI) for Document-level RE, that improves upon existing models from two aspects. First, considering the long input and various templates, we adopt a collective prompt tuning method, which is an update-and-reuse strategy. A generic prompt is first encoded and then updated with exact entity pairs for relation-specific prompts. Second, we introduce a relation inference module to conduct global reasoning overall relation prompts via constrained semantic segmentation. Extensive experiments on two publicly available benchmark datasets demonstrate the effectiveness of our proposed CPT-RI as compared to the baseline model (ATLOP (Zhou et al., 2021)), which improve the 0.57% on the DocRED dataset, 2.20% on the CDR dataset, and 2.30 on the GDA dataset in the F1 score. In addition, further ablation studies also verify the effects of the collective prompt tuning and relation inference.

Keywords

Natural language processing, Document-level relation extraction, Prompt-tuning, Various templates, Global reasoning

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing

Publication

Information Processing and Management

Volume

60

Issue

5

ISSN

0306-4573

Identifier

10.1016/j.ipm.2023.103451

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.ipm.2023.103451

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