Abstract
Learning and utilizing personas in open-domain dialogue have become a hotspot in recent years. The existing methods that only use predefined explicit personas enhance the personality to some extent, however, they cannot easily avoid persona inconsistency and weak diversity responses. To address these problems, this paper proposes an effective model called Exploiting Explicit and Inferred Implicit Personas for Multi-turn Dialogue Generation (EIPD). Specifically, 1) an explicit persona extractor is designed to improve persona consistency; 2) Taking advantage of the von Mises-Fisher (vMF) distribution in modeling directional data (e.g., the different persona state), we introduce the implicit persona inference to increase diversity; 3) during the generation, the persona response generator fuses the explicit and implicit personas in the response. The experimental results on the ConvAI2 persona-chat dataset demonstrate that our model performs better than commonly used baselines. Further analysis of the ablation experiments shows that EIPD can generate more persona-consistent and diverse responses.
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Acknowledgement
This work was supported by the National Key RD Program of China under Grant 2018YFB1305200, the National Natural Science Foundation of China under Grant (61771333, 61976154), the Tianjin Municipal Science and Technology Project under Grant 18ZXZNGX00330, and the State Key Laboratory of Communication Content Cognition, People’s Daily Online (No. A32003).
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Wang, R. et al. (2021). Exploiting Explicit and Inferred Implicit Personas for Multi-turn Dialogue Generation. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13028. Springer, Cham. https://doi.org/10.1007/978-3-030-88480-2_39
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DOI: https://doi.org/10.1007/978-3-030-88480-2_39
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