A Multi-task Learning Framework for Opinion Triplet Extraction

Code

• github.com/GeneZC/OTE-MTL
  OfficialIn paperpytorch★ 51
• github.com/QCOIN-LAB/OTE-MTL
  pytorch★ 2
• github.com/l294265421/OTE-MTL-ASOTE
  pytorch★ 2

Abstract

The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms. However, the extraction of aspect-sentiment pairs lacks opinion terms as a reference, while co-extraction of aspect and opinion terms would not lead to meaningful pairs without determining their sentiment dependencies. To address the issue, we present a novel view of ABSA as an opinion triplet extraction task, and propose a multi-task learning framework to jointly extract aspect terms and opinion terms, and simultaneously parses sentiment dependencies between them with a biaffine scorer. At inference phase, the extraction of triplets is facilitated by a triplet decoding method based on the above outputs. We evaluate the proposed framework on four SemEval benchmarks for ASBA. The results demonstrate that our approach significantly outperforms a range of strong baselines and state-of-the-art approaches.

Tasks

Reproductions