@inproceedings{bd34e726e7974b708c7766207b0673c9,
title = "Question rewrite based dialogue response generation",
abstract = "Dialogue response generation is a fundamental technique in natural language processing, which can be used in human-computer interaction. As the quick development in neural networks, the sequence to sequence (seq2seq) model which employed recurrent neural networks (RNN) encoder-decoder has archived great success in machine translation. Many researchers began to apply this model in dialogue response generation. However, the conventional seq2seq model counters several problems, e.g., grammatical mistake, safe response and etc. In this paper, motivated by the great success of generative adversarial networks (GANs) in generating images, we propose an improved seq2seq framework by employing GANs to rewrite questions in order to retrieve more information from the question. Afterwards we combine the original question and the rewritten question together to generate responses. The experiments on the public Yahoo! Answers dataset demonstrated the proposed framework{\textquoteright}s potential in dialogue response generation.",
keywords = "Dialogue generation, Generative adversarial networks, Question rewriting",
author = "Hengrui Liu and Wenge Rong and Libin Shi and Yuanxin Ouyang and Zhang Xiong",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 25th International Conference on Neural Information Processing, ICONIP 2018 ; Conference date: 13-12-2018 Through 16-12-2018",
year = "2018",
doi = "10.1007/978-3-030-04224-0\_15",
language = "英语",
isbn = "9783030042233",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "169--180",
editor = "Long Cheng and Leung, \{Andrew Chi Sing\} and Seiichi Ozawa",
booktitle = "Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings",
address = "德国",
}