A Multi-task Learning Framework for Opinion Triplet Extraction

• URL: http://arxiv.org/abs/2010.01512v2
• Date: Sat, 31 Oct 2020 02:55:55 GMT
• Title: A Multi-task Learning Framework for Opinion Triplet Extraction
• Authors: Chen Zhang, Qiuchi Li, Dawei Song, Benyou Wang
• Abstract summary: We present a novel view of ABSA as an opinion triplet extraction task. We propose a multi-task learning framework to jointly extract aspect terms and opinion terms. We evaluate the proposed framework on four SemEval benchmarks for ASBA.
• Score: 24.983625011760328
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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