Abstract
Pre-trained models with latent variables have been proved to be an effective method in the diverse dialogue generation. However, the latent variables in current models are finite and uninformative, making the generated responses lack diversity and informativeness. In order to address this problem, we propose an exemplar guided latent pre-trained dialogue generation model to sample the latent variables from a continuous sentence embedding space, which can be controlled by the exemplar sentences. The proposed model contains two parts: exemplar seeking and response generation. First, the exemplar seeking builds a sentence graph based on the given dataset and seeks an enlightened exemplar from the graph. Next, the response generation constructs informative latent variables based on the exemplar and generates diverse responses with latent variables. Experiments show that the model can effectively improve the propriety and diversity of responses and achieve state-of-the-art performance.
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Acknowledgment
This work was supported by National Natural Science Foundation of China (No. 61906187, No. 61976207, No. 61902394).
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Li, M., Fu, P., Lin, Z., Wang, W., Zang, W. (2021). Exemplar Guided Latent Pre-trained Dialogue Generation. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12743. Springer, Cham. https://doi.org/10.1007/978-3-030-77964-1_10
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DOI: https://doi.org/10.1007/978-3-030-77964-1_10
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