A Multi-task Learning Framework for Opinion Triplet Extraction

Chen Zhang,Qiuchi Li,Dawei Song,Benyou Wang

doi:10.18653/v1/2020.findings-emnlp.72

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.

Highlights

Aspect-based sentiment analysis (ABSA), termed as Target-based Sentiment Analysis in some literature (Liu, 2012), is a fine-grained sentiment analysis task
We present a novel view of Aspect-based Sentiment Analysis (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
It is usually formulated as detecting aspect terms and sentiments expressed in a sentence towards the aspects (Li et al, 2019; He et al, 2019; Luo et al, 2019; Hu et al, 2019). This type of formulation is referred to as aspect-sentiment pair extraction. There exists another type of approach to ABSA, referred to as aspectopinion co-extraction, which focuses on jointly deriving aspect terms (a.k.a. opinion targets) and

Summary

Introduction

Aspect-based sentiment analysis (ABSA), termed as Target-based Sentiment Analysis in some literature (Liu, 2012), is a fine-grained sentiment analysis task. It is usually formulated as detecting aspect terms and sentiments expressed in a sentence towards the aspects (Li et al, 2019; He et al, 2019; Luo et al, 2019; Hu et al, 2019). This type of formulation is referred to as aspect-sentiment pair extraction There exists another type of approach to ABSA, referred to as aspectopinion co-extraction, which focuses on jointly deriving aspect terms (a.k.a. opinion targets) and Example sentence: The atmosphere is attractive , but a little uncomfortable. The compelling performances of both directions illustrate a strong dependency between aspect terms, opinion terms and the expressed sentiments

Methods

Results

Conclusion