Unsupervised Topic Modelling for Multi-Party Spoken Discourse

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 17–24, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Unsupervised Topic Modelling for Multi-Party Spoken Discourse Matthew Purver CSLI Stanford University Stanford, CA 94305, USA mpurver@stanford.edu Konrad P. K¨ording Dept. of Brain & Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02139, USA kording@mit.edu Thomas L. Griffiths Dept. of Cognitive & Linguistic Sciences Brown University Providence, RI 02912, USA tom griffiths@brown.edu Joshua B. Tenenbaum Dept. of Brain & Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02139, USA jbt@mit.edu Abstract We present a method for unsupervised topic modelling which adapts methods used in document classification (Blei et al., 2003; Griffiths and Steyvers, 2004) to unsegmented multi-party discourse tran- scripts. We show how Bayesian infer- ence in this generative model can be used to simultaneously address the prob- lems of topic segmentation and topic identification: automatically segmenting multi-party meetings into topically co- herent segments with performance which compares well with previous unsuper- vised segmentation-only methods (Galley et al., 2003) while simultaneously extract- ing topics which rate highly when assessed for coherence by human judges. We also show that this method appears robust in the face of off-topic dialogue and speech recognition errors. 1 Introduction Topic segmentation – division of a text or dis- course into topically coherent segments – and topic identification – classification of those seg- ments by subject matter – are joint problems. Both are necessary steps in automatic indexing, retrieval and summarization from large datasets, whether spoken or written. Both have received significant attention in the past (see Section 2), but most ap- proaches have been targeted at either text or mono- logue, and most address only one of the two issues (usually for the very good reason that the dataset itself provides the other, for example by the ex- plicit separation of individual documents or news stories in a collection). Spoken multi-party meet- ings pose a difficult problem: firstly, neither the segmentation nor the discussed topics can be taken as given; secondly, the discourse is by nature less tidily structured and less restricted in domain; and thirdly, speech recognition results have unavoid- ably high levels of error due to the noisy multi- speaker environment. In this paper we present a method for unsuper- vised topic modelling which allows us to approach both problems simultaneously, inferring a set of topics while providing a segmentation into topi- cally coherent segments. We show that this model can address these problems over multi-party dis- course transcripts, providing good segmentation performance on a corpus of meetings (compara- ble to the best previous unsupervised method that we are aware of (Galley et al., 2003)), while also inferring a set of topics rated as semantically co- herent by human judges. We then show that its segmentation performance appears relatively ro- bust to speech recognition errors, giving us con- fidence that it can be successfully applied in a real speech-processing system. The plan of the paper is as …

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