Research Article
Dialogue Breakdown Detection with Long Short Term Memory
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@INPROCEEDINGS{10.1007/978-3-319-90740-6_18, author={Tittaya Mairittha and Tsuyoshi Okita and Sozo Inoue}, title={Dialogue Breakdown Detection with Long Short Term Memory}, proceedings={Mobile Computing, Applications, and Services. 9th International Conference, MobiCASE 2018, Osaka, Japan, February 28 -- March 2, 2018, Proceedings}, proceedings_a={MOBICASE}, year={2018}, month={5}, keywords={Dialogue breakdown Dialogue systems Text classification}, doi={10.1007/978-3-319-90740-6_18} }- Tittaya Mairittha
Tsuyoshi Okita
Sozo Inoue
Year: 2018
Dialogue Breakdown Detection with Long Short Term Memory
MOBICASE
Springer
DOI: 10.1007/978-3-319-90740-6_18
Abstract
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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