Abstract
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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Notes
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Response script refers to the language skills or templates used by customer service when replying to user reviews.
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Acknowledgments
We thank the reviewers for their valuable comments. This work was supported by the National Natural Science Foundation of China (No. 62076173), the High-level Entrepreneurship and Innovation Plan of Jiangsu Province (No. JSSCRC2021524), and the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Yuan, Y., Gong, C., Kong, D., Yu, N., Fu, G. (2023). Multi-grained Aspect Fusion for Review Response Generation. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14262. Springer, Cham. https://doi.org/10.1007/978-3-031-44201-8_3
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