Using Dempster-Shafer Evidence Theory for Dialog State Tracking

被引：0

作者：

Liu, Minglu ^{[2
]}

Li, Miao ^{[1
]}

Wu, Ji ^{[1
]}

Fu, Xiangling ^{[3
]}

Gao, Ji ^{[3
]}

机构：

[1] Tsinghua Univ, Beijing, Peoples R China

[2] Iflytek Co Ltd, Hefei, Anhui, Peoples R China

[3] Beijing Univ Posts & Telecommun, Beijing, Peoples R China

来源：

2018 11TH INTERNATIONAL SYMPOSIUM ON CHINESE SPOKEN LANGUAGE PROCESSING (ISCSLP) | 2018年

关键词：

target-based state tracking; D-S theory; task-oriented spoken dialog system;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In a task oriented spoken dialogue system, the automatic speech recognition (ASR) and spoken language understanding (SLU) modules usually provide multiple uncertain results, which may be related or divergent. Previously, researchers used classical probability theory based approach to solve this problem, but it is difficult to handle results combination and conflict. In this paper, we describe a novel target-based state tracking algorithm based on Dempster-Shafer(D-S) theory to deal with ASR and SLU uncertain issues. The n-best recognition results of different dialog turns are combined to update the current dialog state using evidence theory. Our method can be easily integrated into existing dialogue system. The effectiveness of our approach is demonstrated on a song-on-demand task, and performs better than traditional probability based methods.

引用

页码：285 / 289

页数：5