Classifying Dialog Acts in Human-Human and Human-Machine Spoken Conversations

摘要

Dialog acts represent the illocutionary aspect of the communication; depending on the nature of the dialog and its participants, different types of dialog act occur and an accurate classification of these is essential to support the understanding of human conversations. We learn effective discriminative dialog act classifiers by studying the most predictive classification features on Human-Human and Human-Machine corpora such as LUNA and SWITCHBOARD; additionally, we assess classifier robustness to speech errors. Our results exceed the state of the art on dialog act classification from reference transcriptions on SWITCHBOARD and allow us to reach a very satisfying performance on ASR transcriptions.