@inproceedings{fe45231fe7b74339b7c5429746511ea3,
title = "Knowledge Memory Based LSTM Model for Answer Selection",
abstract = "Recurrent neural networks (RNN) have shown great success in answer selection task in recent years. Although the attention mechanism has been widely used to enhance the information interaction between questions and answers, knowledge is still the gap between their representations. In this paper, we propose a knowledge memory based RNN model, which incorporates the knowledge learned from the data sets into the question representations. Experiments on two benchmark data sets show the great advantages of our proposed model over that without the knowledge memory. Furthermore, our model outperforms most of the recent progress in question answering.",
keywords = "Answer selection, Deep learning, Knowledge memory",
author = "Weijie An and Qin Chen and Yan Yang and Liang He",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
year = "2017",
doi = "10.1007/978-3-319-70096-0\_4",
language = "英语",
isbn = "9783319700953",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "34--42",
editor = "Dongbin Zhao and El-Alfy, \{El-Sayed M.\} and Derong Liu and Shengli Xie and Yuanqing Li",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
address = "德国",
}