A Semi-Supervised Stable Variational Network for Promoting Replier-Consistency in Dialogue Generation

Jinxin Chang, Ruifang He, Longbiao Wang, Xiangyu Zhao, Ting Yang, Ruifang Wang

Abstract
Neural sequence-to-sequence models for dialog systems suffer from the problem of favoring uninformative and non replier-specific responses due to lack of the global and relevant information guidance. The existing methods model the generation process by leveraging the neural variational network with simple Gaussian. However, the sampled information from latent space usually becomes useless due to the KL divergence vanishing issue, and the highly abstractive global variables easily dilute the personal features of replier, leading to a non replier-specific response. Therefore, a novel Semi-Supervised Stable Variational Network (SSVN) is proposed to address these issues. We use a unit hypersperical distribution, namely the von Mises-Fisher (vMF), as the latent space of a semi-supervised model, which can obtain the stable KL performance by setting a fixed variance and hence enhance the global information representation. Meanwhile, an unsupervised extractor is introduced to automatically distill the replier-tailored feature which is then injected into a supervised generator to encourage the replier-consistency. Experimental results on two large conversation datasets show that our model outperforms the competitive baseline models significantly, and can generate diverse and replier-specific responses.
Anthology ID:
D19-1200
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1920–1930
Language:
URL:
https://aclanthology.org/D19-1200
DOI:
10.18653/v1/D19-1200
Bibkey:
Cite (ACL):
Jinxin Chang, Ruifang He, Longbiao Wang, Xiangyu Zhao, Ting Yang, and Ruifang Wang. 2019. A Semi-Supervised Stable Variational Network for Promoting Replier-Consistency in Dialogue Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1920–1930, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
A Semi-Supervised Stable Variational Network for Promoting Replier-Consistency in Dialogue Generation (Chang et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/D19-1200.pdf
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