A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis

• URL: http://arxiv.org/abs/2208.11283v1
• Date: Wed, 24 Aug 2022 03:03:49 GMT
• Title: A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis
• Authors: Wei Chen, Jinglong Du, Zhao Zhang, Fuzhen Zhuang, Zhongshi He
• Abstract summary: We propose a hierarchical interactive network (HI-ASA) to model two-way interactions between two tasks appropriately. We use cross-stitch mechanism to combine the different task-specific features selectively as the input to ensure proper two-way interactions. Experiments on three real-world datasets demonstrate HI-ASA's superiority over baselines.
• Score: 34.1489054082536
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect boundaries and then classify the span-level sentiments (sentiment classification). However, most existing approaches either sequentially extract task-specific features, leading to insufficient feature interactions, or they encode aspect features and sentiment features in a parallel manner, implying that feature representation in each task is largely independent of each other except for input sharing. Both of them ignore the internal correlations between the aspect extraction and sentiment classification. To solve this problem, we novelly propose a hierarchical interactive network (HI-ASA) to model two-way interactions between two tasks appropriately, where the hierarchical interactions involve two steps: shallow-level interaction and deep-level interaction. First, we utilize cross-stitch mechanism to combine the different task-specific features selectively as the input to ensure proper two-way interactions. Second, the mutual information technique is applied to mutually constrain learning between two tasks in the output layer, thus the aspect input and the sentiment input are capable of encoding features of the other task via backpropagation. Extensive experiments on three real-world datasets demonstrate HI-ASA's superiority over baselines.

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