@inproceedings{9252866e11ac480893ea06123b62dcb2,
title = "Learning to start for sequence to sequence based response generation",
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.",
keywords = "Learning to start, Response generation, Sequence to sequence",
author = "Qingfu Zhu and Weinan Zhang and Ting Liu",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 24th China Conference on Information Retrieval, CCIR 2018 ; Conference date: 27-09-2018 Through 29-09-2018",
year = "2018",
doi = "10.1007/978-3-030-01012-6\_22",
language = "英语",
isbn = "9783030010119",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "274--285",
editor = "Xianxian Li and Chenliang Li and Tie-Yan Liu and Jiafeng Guo and Shichao Zhang",
booktitle = "Information Retrieval - 24th China Conference, CCIR 2018, Proceedings",
address = "德国",
}