Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response

2019-09-01 · WS 2019 9 · Tatiana Anikina, Ivana Kruijff-Korbayova

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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Tasks

Dialogue Act ClassificationDisaster ResponseGeneral Classification

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