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This paper presents the first demonstration of a statistical spoken dialogue system that uses automatic belief compression to reason over complex user goal sets. Reasoning over the power set of possible user goals allows complex sets of user goals to be represented, which leads to more natural dialogues. The use of the power set results in a massive expansion in the number of belief states maintained by the Partially Observable Markov Decision Process (POMDP) spoken dialogue manager. A modified form of Value Directed Compression (VDC) is applied to the POMDP belief states producing a near-lossless compression which reduces the. | A Statistical Spoken Dialogue System using Complex User Goals and Value Directed Compression Paul A. Crook Zhuoran Wang Xingkun Liu and Oliver Lemon Interaction Lab School of Mathematical and Computer Sciences MACS Heriot-Watt University Edinburgh UK p.a.crook zhuoran.wang x.liu o.lemon @hw.ac.uk Abstract This paper presents the first demonstration of a statistical spoken dialogue system that uses automatic belief compression to reason over complex user goal sets. Reasoning over the power set of possible user goals allows complex sets of user goals to be represented which leads to more natural dialogues. The use of the power set results in a massive expansion in the number of belief states maintained by the Partially Observable Markov Decision Process POMDP spoken dialogue manager. A modified form of Value Directed Compression VDC is applied to the POMDP belief states producing a near-lossless compression which reduces the number of bases required to represent the belief distribution. 1 Introduction One of the main problems for a spoken dialogue system SDS is to determine the user s goal e.g. plan suitable meeting times or find a good Indian restaurant nearby under uncertainty and thereby to compute the optimal next system dialogue action e.g. offer a restaurant ask for clarification . Recent research in statistical SDSs has successfully addressed aspects of these problems through the application of Partially Observable Markov Decision Process POMDP approaches Thomson and Young 2010 Young et al. 2010 . However POMDP SDSs are currently limited by the representation of user goals adopted to make systems computationally tractable. Work in dialogue system evaluation e.g. Walker et al. 2004 and Lemon et al. 2006 shows that real user goals are generally sets of items rather than a single item. People like to explore possible trade offs between the attributes of items. Crook and Lemon 2010 identified this as a central challenge for the field of spoken dialogue systems .
