Abstract
Knowledge selection is the key component in knowledge-ground dialogues, which aims to choice correct knowledge based on external knowledge for dialogue generation. The quality of knowledge selection depend on knowledge representation methods. However, the knowledge representation exploration is still challenging. We propose a knowledge-grounded dialogue model, which incorporates a knowledge-grounded module and a dialogue generation module, aiming to choose the most appropriate knowledge and fuse it into response generation. In addition, supervised contrastive knowledge representation signal is designed to obtain knowledge representation dynamically. Experiments on FoCus dataset show that our model outperforms the baseline models. The ablation study further demonstrates the effectiveness of each sub-modules.
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Acknowledgment
This work was supported in part Key R & D project of Shandong Province 2019JZZY010129, and in part by the Shandong Provincial Social Science Planning Project under Award 19BJCJ51, Award 18CXWJ01, and Award 18BJYJ04.
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Wang, B., Xu, F., Zhu, Z., Liu, P. (2023). Knowledge-Grounded Dialogue Generation with Contrastive Knowledge Selection. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_55
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DOI: https://doi.org/10.1007/978-981-99-7254-8_55
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