Question Detection in Spoken Conversations Using Textual Conversations

Anna Margolis and Mari Ostendorf
University of Washington


Abstract

We investigate the use of textual Internet conversations for detecting questions in spoken conversations. We compare the text-trained model with models trained on manually-labeled, domain-matched spoken utterances with and without prosodic features. Overall, the text-trained model achieves over 90% of the performance (measured in Area Under the Curve) of the domain-matched model including prosodic features, but does especially poorly on declarative questions. We describe efforts to utilize unlabeled spoken utterances and prosodic features via domain adaptation.




Full paper: http://www.aclweb.org/anthology/P/P11/P11-2021.pdf