Proceedings

Progressive Open-Domain Response Generation with Multiple Controllable Attributes

Haiqin Yang, Xiaoyuan Yao, Yiqun Duan, Jianping Shen, Jie Zhong, Kun Zhang

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3279-3285.

https://doi.org/10.24963/ijcai.2021/451

Keywords

  • Machine Learning: Learning Generative Models
  • Natural Language Processing: Dialogue

Abstract

It is desirable to include more controllable attributes to enhance the diversity of generated responses in open-domain dialogue systems. However, existing methods can generate responses with only one controllable attribute or lack a flexible way to generate them with multiple controllable attributes. In this paper, we propose a Progressively trained Hierarchical Encoder-Decoder (PHED) to tackle this task. More specifically, PHED deploys Conditional Variational AutoEncoder (CVAE) on Transformer to include one aspect of attributes at one stage. A vital characteristic of the CVAE is to separate the latent variables at each stage into two types: a global variable capturing the common semantic features and a specific variable absorbing the attribute information at that stage. PHED then couples the CVAE latent variables with the Transformer encoder and is trained by minimizing a newly derived ELBO and controlled losses to produce the next stage's input and produce responses as required. Finally, we conduct extensive evaluations to show that PHED significantly outperforms the state-of-the-art neural generation models and produces more diverse responses as expected.

Haiqin Yang, Xiaoyuan Yao, Yiqun Duan, Jianping Shen, Jie Zhong, and Kun Zhang. 2021. Progressive Open-Domain Response Generation with Multiple Controllable Attributes. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3279–3285. https://doi.org/10.24963/ijcai.2021/451
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