Mutually Guided Few-shot Learning for Relational Triple Extraction

• URL: http://arxiv.org/abs/2306.13310v1
• Date: Fri, 23 Jun 2023 06:15:54 GMT
• Title: Mutually Guided Few-shot Learning for Relational Triple Extraction
• Authors: Chengmei Yang, Shuai Jiang, Bowei He, Chen Ma, and Lianghua He
• Abstract summary: Mutually Guided Few-shot learning framework for Triple Extraction (MG-FTE) Our method consists of an entity-guided relation-decoder to classify relations and a proto-decoder to extract entities. Our method outperforms many state-of-the-art methods by 12.6 F1 score on FewRel 1.0 (single domain) and 20.5 F1 score on FewRel 2.0 (cross-domain)
• Score: 10.539566491939844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graphs (KGs), containing many entity-relation-entity triples, provide rich information for downstream applications. Although extracting triples from unstructured texts has been widely explored, most of them require a large number of labeled instances. The performance will drop dramatically when only few labeled data are available. To tackle this problem, we propose the Mutually Guided Few-shot learning framework for Relational Triple Extraction (MG-FTE). Specifically, our method consists of an entity-guided relation proto-decoder to classify the relations firstly and a relation-guided entity proto-decoder to extract entities based on the classified relations. To draw the connection between entity and relation, we design a proto-level fusion module to boost the performance of both entity extraction and relation classification. Moreover, a new cross-domain few-shot triple extraction task is introduced. Extensive experiments show that our method outperforms many state-of-the-art methods by 12.6 F1 score on FewRel 1.0 (single-domain) and 20.5 F1 score on FewRel 2.0 (cross-domain).

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