@inproceedings{e92e89a0d4c34b7b94c8c357aeca3247,
title = "An auto-encoder for learning conversation representation using LSTM",
abstract = "In this paper, an auto-encoder is proposed to learn conversation representation. First, the long short term memory (LSTM) neural network is used to encode the sequence of sentences in a conversation. The interactive context is encoded into a fixed-length vector. Then, through the LSTM-decoder, the learnt representation is used to reconstruct the sentence vectors of a conversation. To train our model, we construct one corpus with 32,881 conversations from the online shopping platform. Finally, experiments on topic recognition task demonstrate the effectiveness of the proposed auto-encoder on learning conversation representation, especially when training data of topic recognition is relatively small.",
keywords = "Auto-encoder, Conversation representation, LSTM",
author = "Xiaoqiang Zhou and Baotian Hu and Qingcai Chen and Xiaolong Wang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 22nd International Conference on Neural Information Processing, ICONIP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",
year = "2015",
doi = "10.1007/978-3-319-26532-2\_34",
language = "英语",
isbn = "9783319265315",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "310--317",
editor = "Lai, \{Weng Kin\} and Qingshan Liu and Tingwen Huang and Sabri Arik",
booktitle = "Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings",
address = "德国",
}