Dialogue Breakdown Detection with Long Short Term Memory

Tittaya Mairittha, Tsuyoshi Okita, Sozo Inoue,
EAI International Conference on Mobile Computing, Applications and Services (MobiCASE) poster
(Not Available)
(Not Available)
245-250
2018-03-01
Osaka
http://mobicase.org/2018/show/home
This paper aims to detect the utterance which can be categorized as the breakdown of the dialogue flow. We propose a logistic regression-based and a Long Short-Term Memory (LSTM)-based methods. Using the input with utterance-response pairs the performance of the LSTM-based method is superior to that of the logistic regression-based method in 36% measured with F-measure. We also measured the performance using the performance with utterance-response pairs: the performance with the input only with responses is unexpectedly inferior to those with responses in 6% to 23% measured with F-measure.

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