Mutually Guided Few-shot Learning for Relational Triple Extraction
Chengmei Yang Shuai Jiang Bowei He Chen Ma Lianghua He
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).