Joint dialog act segmentation and recognition in human conversations using attention to dialog context

摘要

A dialog act represents the communicative function of an utterance in a conversation, and thus provides informative cues for understanding, managing, and generating dialog. While most spoken dialog systems process user input and system output at the turn level, a single turn can consist of multiple dialog acts in human conversations. Therefore, segmenting turn-level tokens into a meaningful dialog act unit is just as important as recognizing the dialog act. Towards joint segmentation and recognition of dialog acts, we propose an encoder-decoder model featuring joint coding and incorporate contextual information by means of an attentional mechanism. The proposed encoder-decoder outperforms other models in segmentation, and the application of attentions significantly reduces recognition error rates. By combining the encoder-decoder model with contextual attention, we achieve state-of-the-art performance in the joint evaluation of dialog act segmentation and recognition. (C) 2019 Elsevier Ltd. All rights reserved.