Question Detection in Spoken Conversations Using Textual Conversations

We investigate the use of textual Internet conversations for detecting questions in spoken conversations. We compare the text-trained model with models trained on manuallylabeled, 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.