Multi-grained Aspect Fusion for Review Response Generation
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2023, PT IX(2023)
摘要
Review response generation (RRG) aims to automatically generate responses to customer reviews. Responding to reviews in a right manner is important to online customer experience. However, most previous research on RRG focused on exploring coarse review information and ignored fine-grain aspects within reviews, especially those with negative sentiment. As a result, the generated responses are usually not targeted to users' real concerns in their reviews. To this end, we proposed a multi-grained aspect fusion model (MGAF) model to improve the targeting of generated responses. In particular, we first enhance the targeting ability by performing sentence-level aspect selection and response script learning. Then we integrate aspect-level keywords with sentiment information to further improve the diversity of generated responses. Experimental results on both Chinese and English datasets show that our proposed model outperforms the state-of-the-art models available, demonstrating the importance of fusing multi-grained aspect information for targeted response generation.
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关键词
response generation,aspect targeting,script learning
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