Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response

Tatiana Anikina, Ivana Kruijff-Korbayova


Abstract
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 embeddings, context and attention mechanism. The best performance was achieved with a ”Divide & Merge” architecture presented in the paper, using trainable GloVe embeddings and a structured dialogue history. This model learns from the current utterance and the preceding context separately and then combines the two generated representations. Average accuracy of 10-fold cross-validation is 79.8%, F-score 71.8%.
Anthology ID:
W19-5946
Volume:
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
Month:
September
Year:
2019
Address:
Stockholm, Sweden
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
399–410
Language:
URL:
https://aclanthology.org/W19-5946
DOI:
10.18653/v1/W19-5946
Bibkey:
Cite (ACL):
Tatiana Anikina and Ivana Kruijff-Korbayova. 2019. Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 399–410, Stockholm, Sweden. Association for Computational Linguistics.
Cite (Informal):
Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response (Anikina & Kruijff-Korbayova, SIGDIAL 2019)
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PDF:
https://preview.aclanthology.org/author-url/W19-5946.pdf