Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response

Tatiana Anikina, Ivana Kruijff-Korbayová

In: 20th Annual SIGdial Meeting on Discourse and Dialogue - Proceedings of the Conference. Annual SIGdial Meeting on Discourse and Dialogue (SIGdial-2019) 20th September 11-13 Stockholm Sweden Seiten 399-410 ISBN 978-1-950737-61-1 The Association of Computational Linguistics 9/2019.


We present the results we obtained on the classification of dialogue acts in a corpus of human-human team communication in the domain of robot-assisted disaster response. We annotated dialogue acts according to the ISO 24617-2 standard scheme and carried out experiments using the FastText linear classifier as well as several neural architectures, including feed-forward, recurrent and convolutional neural models with different types of embed- dings, context and attention mechanism. The best performance was achieved with a "Divide & Merge" architecture presented in the paper, using trainable GloVe embeddings and a struc- tured dialogue history. This model learns from the current utterance and the preceding context separately and then combines the two gener- ated representations. Average accuracy of 10- fold cross-validation is 79.8%, F-score 71.8%


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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence