Query-based Instance Discrimination Network for Relational Triple Extraction
This addresses error propagation and relation redundancy in information extraction for NLP applications, though it appears incremental as it builds on existing query-based methods.
The paper tackles the problem of joint entity and relation extraction by proposing a query-based approach to construct instance-level representations for relational triples, eliminating error propagation and achieving state-of-the-art results on five benchmarks.
Joint entity and relation extraction has been a core task in the field of information extraction. Recent approaches usually consider the extraction of relational triples from a stereoscopic perspective, either learning a relation-specific tagger or separate classifiers for each relation type. However, they still suffer from error propagation, relation redundancy and lack of high-level connections between triples. To address these issues, we propose a novel query-based approach to construct instance-level representations for relational triples. By metric-based comparison between query embeddings and token embeddings, we can extract all types of triples in one step, thus eliminating the error propagation problem. In addition, we learn the instance-level representation of relational triples via contrastive learning. In this way, relational triples can not only enclose rich class-level semantics but also access to high-order global connections. Experimental results show that our proposed method achieves the state of the art on five widely used benchmarks.