Abstract
Continuous processing of input in incremental dialogue systems might result in the need of interrupting a user’s utterance when clarification or rapport is needed. Being able to predict the right time when to interrupt the utterance can be another step to a more human-like dialogue. On the other hand, annotation of corpora with different types of possible interruptions requires additional human resources. In this paper, we discuss how to process a corpus that does not have interruptions specifically annotated. We also present initial experiments on two corpora and show that it is possible to model the desired behaviour from these corpora.
This work was supported by the European Regional Development Fund under the project Robotics for Industry 4.0 \(\ \mathrm {(reg.\ no.\ CZ.02.1.01/0.0/0.0/15\_003/}\mathrm {0000470)}\) and by the grant of the University of West Bohemia, project No. SGS-2016-039. Access to computing and storage facilities owned by parties and projects co ntributing to the National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.
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Chýlek, A., Švec, J., Šmídl, L. (2018). Learning to Interrupt the User at the Right Time in Incremental Dialogue Systems. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2018. Lecture Notes in Computer Science(), vol 11107. Springer, Cham. https://doi.org/10.1007/978-3-030-00794-2_54
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DOI: https://doi.org/10.1007/978-3-030-00794-2_54
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