Why Do Neural Dialog Systems Generate Short And Meaningless Replies? A Comparison Between Dialog And Translation
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2019)
摘要
This paper addresses the question: In neural dialog systems, why do sequence-to-sequence (Seq2Seq) neural networks generate short and meaningless replies for open-domain response generation? We conjecture that in a dialog system, due to the randomness of spoken language, there may be multiple equally plausible replies for one utterance, causing the deficiency of a Seq2Seq model. To evaluate our conjecture, we propose a systematic way to mimic the dialog scenario in machine translation systems with both real datasets and toy datasets generated elaborately. Experimental results show that we manage to reproduce the phenomenon of generating short and meaningless sentences in the translation setting.
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关键词
Dialog system,machine translation,sequence-to-sequence,short replies
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