Automatic Segmentation of Multiparty Dialogue

Pei-Yun Hsueh, Johanna Moore, Steve Renals

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we investigate the problem of automatically predicting segment boundaries in spoken multiparty dialogue. We extend prior work in two ways. We first apply approaches that have been proposed for predicting top-level topic shifts to the problem of identifying subtopic boundaries. We then explore the impact on performance of using ASR output as opposed to human transcription. Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task. We also find that the transcription errors inevitable in ASR output have a negative impact on models
that combine lexical-cohesion and conversational features, but do not change the general preference of approach for the two tasks.
Original languageEnglish
Title of host publication11th Conference of the European Chapter of the Association for Computational Linguistics
PublisherAssociation for Computational Linguistics
Number of pages8
ISBN (Print)1-932432-59-0
Publication statusPublished - 2006

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