@inproceedings{47661b770b1b4d33bbe937400f058dd2,
title = "Memory-based model with multiple attentions for multi-turn response selection",
abstract = "In this paper, we study the task of multi-turn response selection in retrieval-based dialogue systems. Previous approaches focus on matching response with utterances in the context to distill important matching information, and modeling sequential relationship among utterances. This kind of approaches do not take into account the position relationship and inner semantic relevance between utterances and query (i.e., the last utterance). We propose a memory-based network (MBN) to build the effective memory integrating position relationship and inner semantic relevance between utterances and query. Then we adopt multiple attentions on the memory to learn representations of context with multiple levels, which is similar to the behavior of human that repetitively think before response. Experimental results on a public data set for multi-turn response selection show the effectiveness of our MBN model.",
keywords = "Memory network, Multi-turn conversation, Neural networks, Response selection",
author = "Xingwu Lu and Man Lan and Yuanbin Wu",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 25th International Conference on Neural Information Processing, ICONIP 2018 ; Conference date: 13-12-2018 Through 16-12-2018",
year = "2018",
doi = "10.1007/978-3-030-04179-3\_26",
language = "英语",
isbn = "9783030041786",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "296--307",
editor = "Leung, \{Andrew Chi Sing\} and Seiichi Ozawa and Long Cheng",
booktitle = "Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings",
address = "德国",
}