Scalability and Portability of a Belief Network-based Dialog Model for Different Application Domains

Scalability and Portability of a Belief Network-based Dialog Model for Different Application Domains Carmen Wai The Chinese University of Hong Kong Shatin, N.T., Hong Kong SAR, China Tel: +852 2609 8327 cmwai@se.cuhk.edu.hk Helen M. Meng The Chinese University of Hong Kong Shatin, N.T., Hong Kong SAR, China Tel: +852 2609 8327 hmmeng@se.cuhk.edu.hk Roberto Pieraccini SpeechWorks International Ltd 17 State Street New York, NY 1004 Tel: +1.212.425.7200 roberto.pieraccini@speechworks.com ABSTRACT This paper describes the scalability and portability of a Belief Network (BN)-based mixed initiative dialog model across application domains. The Belief Networks (BNs) are used to automatically govern the transitions between a system-initiative and a user-initiative dialog model, in order to produce mixed- initiative interactions. We have migrated our dialog model from a simpler domain of foreign exchange to a more complex domain of air travel information service. The adapted processes include: (i) automatic selection of specified concepts in the user’s query, for the purpose of informational goal inference; (ii) automatic detection of missing / spurious concepts based on backward inference using the BN. We have also enhanced our dialog model with the capability of discourse context inheritance. To ease portability across domains, which often implies the lack of training data for the new domain, we have developed a set of principles for hand-assigning BN probabilities, based on the “degree of belief” in the relationships between concepts and goals. Application of our model to the ATIS data gave promising results. 1. INTRODUCTION Spoken dialog systems demonstrate a high degree of usability in many restricted domains, and dialog modeling in such systems plays an important role in assisting users to achieve their goals. The system-initiative dialog model assumes complete control in guiding the user through an interaction towards task completion. This model often attains high task completion rates, but the user is bound by many constraints throughout the interaction. Conversely, the user-initiative model offers maximum flexibility to the user in determining the preferred course of interaction. However this model often has lower task completion rates relative to the system-initiative model, especially when the user’s request falls beyond the system's competence level. To strike a balance between these two models, the mixed-initiative dialog model allows both the user and the system to influence the course of interaction. It is possible to handcraft a sophisticated mixed-initiative dialog flow, but the task is expensive, and may become intractable for complex application domains. We strive to reduce handcrafting in the design of mixed- initiative dialogs. We propose to use Belief Networks (BN) to automatically govern the transitions between a system-initiative and a user-initiative dialog model, in order to produce mixed- initiative interactions. Previous work includes the use of semantic interpretation rules for natural language understanding, where the rules are learnt by decision trees known as Semantic Classification Trees (SCTs) [6]. Moreover, there is also previous effort that explores the use of machine learning techniques to automatically determine the optimal dialog strategy. A dialog system can be described as a sequential decision process that has states and actions. …

Content truncated to 500 words