Abstract
Response Generation which is a crucial component of a dialogue system can be modeled using the Sequence to Sequence (Seq2Seq) architecture. However, this kind of method suffers from vague responses of little meaningful content. One possible reason for generating vague responses is the different distribution of the first word between the generated responses and human responses. In fact, the Seq2Seq based method tends to generate high-frequency words in the beginning, which influences the following prediction resulting in vague responses. In this paper, we proposed a novel approach, namely learning to start (LTS), to learn how to generate the first word in the sequence to sequence architecture for response generation. Experimental results show that the proposed LTS model can enhance the performance of the start-of-the-art Seq2Seq model as well as other Seq2Seq models for response generation of short text conversation.
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All annotators are well-educated students and have a Bachelor or higher degree.
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Zhu, Q., Zhang, W., Liu, T. (2018). Learning to Start for Sequence to Sequence Based Response Generation. In: Zhang, S., Liu, TY., Li, X., Guo, J., Li, C. (eds) Information Retrieval. CCIR 2018. Lecture Notes in Computer Science(), vol 11168. Springer, Cham. https://doi.org/10.1007/978-3-030-01012-6_22
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