@inproceedings{anikina-kruijff-korbayova-2019-dialogue,
title = "Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response",
author = "Anikina, Tatiana and
Kruijff-Korbayova, Ivana",
booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
month = sep,
year = "2019",
address = "Stockholm, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5946",
doi = "10.18653/v1/W19-5946",
pages = "399--410",
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{\%}.",
}
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%0 Conference Proceedings
%T Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response
%A Anikina, Tatiana
%A Kruijff-Korbayova, Ivana
%S Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
%D 2019
%8 sep
%I Association for Computational Linguistics
%C Stockholm, Sweden
%F anikina-kruijff-korbayova-2019-dialogue
%X 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%.
%R 10.18653/v1/W19-5946
%U https://aclanthology.org/W19-5946
%U https://doi.org/10.18653/v1/W19-5946
%P 399-410
Markdown (Informal)
[Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response](https://aclanthology.org/W19-5946) (Anikina & Kruijff-Korbayova, 2019)
ACL