Attention Based Dialogue Context Selection Model

被引：0
|
作者：
Xu, Weidi [1 ,2 ]
Ren, Yong [3 ]
Tan, Ying [1 ,2 ]
机构：
[1] Peking Univ, Key Lab Machine Percept, Minist Educ, Beijing 100871, Peoples R China
[2] Peking Univ, Dept Machine Intelligence, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China
[3] Tsinghua Univ, Dept Elect Engn, Complex Engn Syst Lab CESL, Beijing 100084, Peoples R China
来源：
NEURAL INFORMATION PROCESSING (ICONIP 2018), PT II | 2018年 / 11302卷
基金：
北京市自然科学基金;
关键词：
D O I：
10.1007/978-3-030-04179-3_25
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The particular phenomena of Information Overload and Conversational Dependency in multi-turn dialogues have brought massive noise for feature learning in existing deep learning models. To solve the problem, the Attention Based Dialogue Context Selection Model (ABDCS) is proposed in this paper. This model uses attention mechanism to extract the relationship between current response utterance and previous utterances. Qualitative and quantitative analysis show that ABDCS is able to choose the semantically related utterances in its dialogue history as context and be robust against the noise.
引用
收藏
页码：286 / 295
页数：10
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