Abstract
Knowledge-aware dialogue generation aims to generate informative and meaningful responses with external knowledge. Existing works are still insufficient to encode retrieved knowledge regardless of the dialogue context, which probably leads to the introduction of irrelevant information. In this paper, we propose a dialogue generation model named CKFS-DG, which filters out context-irrelevant and off-topic knowledge to reduce the influence of redundant knowledge. Specifically, we design a knowledge-enriched encoder and a topic fact predictor to improve the quality of fusion knowledge. For achieve the knowledge-enriched encoder, we put forward a context-knowledge attention mechanism to dynamically filter out irrelevant knowledge conditioned on context. For the topic fact predictor, we utilize the probability distribution on retrieved facts to retain on-topic knowledge. The experimental results on English Reddit and Chinese Weibo dataset demonstrate that CKFS-DG outperforms the state-of-the-art neural generative methods in knowledge utilization, and CKFS-DG could reduce the influence of irrelevant knowledge to generate reasonable responses.
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Acknowledgements
This work was supported by the National Key R &D Program of China under Grant No. 2019YFF0302601.
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Fu, D., Zhang, C., Yu, J., Sun, Q., Zhan, Z. (2022). Improving Dialogue Generation with Commonsense Knowledge Fusion and Selection. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13368. Springer, Cham. https://doi.org/10.1007/978-3-031-10983-6_8
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