DAFA: Dialog System Domain Adaptation With a Filter and an Amplifier

IEEE Access

Liang He¹  Jianfeng Yu¹  Yan Yang¹  Zhou Yu²  Chengcai Chen³ 

[1] Department of Computer Science, East China Normal University, Shanghai, China;Department of Computer Science, University of California at Davis, Davis, CA, USA;Xiaoi Research, Shanghai, China;

关键词: Neural network;    dialogue system;    domain adaptation;    encoder-decoder model;   

DOI  :  10.1109/ACCESS.2020.2976816

来源: DOAJ

【 摘 要 】

End-to-end task-oriented dialog systems have attracted vast amounts of attention in recent years, mainly because of their ease of training. However, such an end-to-end model requires a large number of labeled dialogs to train. Labeled dialogs are always difficult to obtain in real-world settings. We propose a domain adaptive end-to-end task-oriented dialog model that transfers knowledge in source domains to a target domain with limited training samples. Specifically, we design a domain adaptive filter in the encoder-decoder model to reduce useless features in source domains and preserve common features. A domain adaptive amplifier is designed to enhance the target domain impact. We evaluate our method on both synthetic dialog and human-human dialog datasets and achieve state-of-the-art results.

【 授权许可】

Unknown