Automatic Segmentation of Multiparty Dialogue

Automatic Segmentation of Multiparty Dialogue Pei-Yun Hsueh School of Informatics University of Edinburgh Edinburgh, EH8 9LW, GB p.hsueh@ed.ac.uk Johanna D. Moore School of Informatics University of Edinburgh Edinburgh, EH8 9LW, GB J.Moore@ed.ac.uk Steve Renals School of Informatics University of Edinburgh Edinburgh, EH8 9LW, GB s.renals@ed.ac.uk Abstract In this paper, we investigate the prob- lem of automatically predicting segment boundaries in spoken multiparty dialogue. We extend prior work in two ways. We first apply approaches that have been pro- posed 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. Exam- ination 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 ma- chine learning approach that combines lexical-cohesion and conversational fea- tures 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 conver- sational features, but do not change the general preference of approach for the two tasks. 1 Introduction Text segmentation, i.e., determining the points at which the topic changes in a stream of text, plays an important role in applications such as topic detection and tracking, summarization, automatic genre detection and information retrieval and ex- traction (Pevzner and Hearst, 2002). In recent work, researchers have applied these techniques to corpora such as newswire feeds, transcripts of radio broadcasts, and spoken dialogues, in order to facilitate browsing, information retrieval, and topic detection (Allan et al., 1998; van Mulbregt et al., 1999; Shriberg et al., 2000; Dharanipragada et al., 2000; Blei and Moreno, 2001; Christensen et al., 2005). In this paper, we focus on segmenta- tion of multiparty dialogues, in particular record- ings of small group meetings. We compare mod- els based solely on lexical information, which are common in approaches to automatic segmentation of text, with models that combine lexical and con- versational features. Because tasks as diverse as browsing, on the one hand, and summarization, on the other, require different levels of granularity of segmentation, we explore the performance of our models for two tasks: hypothesizing where ma- jor topic changes occur and hypothesizing where more subtle nested topic shifts occur. In addition, because we do not wish to make the assumption that high quality transcripts of meet- ing records, such as those produced by human transcribers, will be commonly available, we re- quire algorithms that operate directly on automatic speech recognition (ASR) output. 2 Previous Work Prior research on segmentation of spoken “docu- ments” uses approaches that were developed for text segmentation, and that are based solely on textual cues. These include algorithms based on lexical cohesion (Galley et al., 2003; Stokes et al., 2004), as well as models …

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