Evaluation of Different Segmentation Techniques for Dialogue Turns

Abstract

In dialogue systems, it is necessary to decode the user input into semantically meaningful units. These semantical units, usually Dialogue Acts (DA), are used by the system to produce the most appropriate response. The user turns can be segmented into utterances, which are meaningful segments from the dialogue viewpoint. In this case, a single DA is associated to each utterance. Many previous works have used DA assignation models on segmented dialogue corpora, but only a few have tried to perform the segmentation and assignation at the same time. The knowledge of the segmentation of turns into utterances is not common in dialogue corpora, and knowing the quality of the segmentations provided by the models that simultaneously perform segmentation and assignation would be interesting. In this work, we evaluate the accuracy of the segmentation offered by this type of model. The evaluation is done on a Spanish dialogue system on a railway information task. The results reveal that one of these techniques provides a high quality segmentation for this corpus.

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