Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response
Tatiana Anikina, Ivana Kruijff‐Korbayová · 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 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 10fold cross-validation is 79.8%, F-score 71.8%.