User modeling in spoken dialogue systems for flexible guidance generation (original) (raw)

User Modeling in Spoken Dialogue Systems to Generate Flexible Guidance

User Modeling and User-Adapted Interaction, 2005

We address the issue of appropriate user modeling to generate cooperative responses to users in spoken dialogue systems. Unlike previous studies that have focused on a user's knowledge, we propose more generalized modeling. We specifically set up three dimensions for user models: the skill level in use of the system, the knowledge level about the target domain, and the degree of urgency. Moreover, the models are automatically derived by decision tree learning using actual dialogue data collected by the system. We obtained reasonable accuracy in classification for all dimensions. Dialogue strategies based on user modeling were implemented on the Kyoto City Bus Information System that was developed at our laboratory. Experimental evaluations revealed that the cooperative responses adapted to each subject type served as good guides for novices without increasing the duration dialogue lasted for skilled users.

Modeling spoken decision support dialogue and optimization of its dialogue strategy

ACM Transactions on Speech and Language Processing, 2011

This article presents a user model for user simulation and a system state representation in spoken decision support dialogue systems. When selecting from a group of alternatives, users apply different decision-making criteria with different priorities. At the beginning of the dialogue, however, users often do not have a definite goal or criteria in which they place value, thus they can learn about new features while interacting with the system and accordingly create new criteria. In this article, we present a user model and dialogue state representation that accommodate these patterns by considering the user's knowledge and preferences. To estimate the parameters used in the user model, we implemented a trial sightseeing guidance system, collected dialogue data, and trained a user simulator. Since the user parameters are not observable from the system, the dialogue is modeled as a partially observable Markov decision process (POMDP), and a dialogue state representation was intro...

User Modelling in Adaptive Dialogue Management

This paper describes an adaptive approach to dialogue management in spoken dialogue systems. The system maintains a user model, in which assumptions about the user's expectations of the system are recorded. Whenever errors occur, the dialogue manager drops some assumption from the user model, and adapts its behaviour accordingly. The system uses a hierarchical slot structure that allows generation and interpretation of utterances at various levels of generality. This is essential for exploiting mixed-initiative to optimise effectiveness. The hierarchical slot structure is also a source of variation. This is important because it is ineffective to repeat prompts in cases of speech recognition errors, for in such cases users are likely to repeat their responses also, and thus the same speech recognition problems too.

Data-driven strategies for an automated dialogue system

Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04, 2004

We present a prototype natural-language problem-solving application for a financial services call center, developed as part of the Amitiés multilingual human-computer dialogue project. Our automated dialogue system, based on empirical evidence from real call-center conversations, features a datadriven approach that allows for mixed system/customer initiative and spontaneous conversation. Preliminary evaluation results indicate efficient dialogues and high user satisfaction, with performance comparable to or better than that of current conversational travel information systems.

The Amitiés system: Data-driven techniques for automated dialogue

Speech Communication, 2006

We present a natural-language customer service application for a telephone banking call center, developed as part of the AMITIÉS dialogue project (Automated Multilingual Interaction with Information and Services). Our dialogue system, based on empirical data gathered from real call-center conversations, features data-driven techniques that allow for spoken language understanding despite speech recognition errors, as well as mixed system/customer initiative and spontaneous conversation.

Knowledge-Combining Methodology for Dialogue Design in Spoken Language Systems

Genetic Resources and Crop Evolution, 2005

In this paper, we propose a strategy for designing dialogue managers in spoken dialogue systems for a restricted domain. This strategy combines several information sources intuition, observation and simulation, in order to maximize the adaptation within the system capability and the expectation of the user. These sources are combined by an iterative process consisting of five steps, where different dialogue alternatives are proposed and evaluated sequentially. The evaluation process includes different measures depending on the information required. Several measures are proposed and analyzed in each step. We also describe a user-modeling technique and an approach for designing the confirmation sub-dialogues based on recognition confidence measures. The knowledge-combining methodology is described and applied to a railway information system. In a subjective evaluation, users from the university gave the system a 3.9 score on a 5-point scale with an average call duration of 205 seconds. The employers of the railway company were more critical of the system. They gave it a score of 2.1 even though the system resolved more than half of the calls (57.8%) within an average call duration of three minutes (185 seconds).

User modeling in dialog systems: Potentials and hazards

1990

In order to be capable of exhibiting a wide range of cooperative behavior, a computer-based dialog system must have available assumptions about the current user's goals, plans, background knowledge and (false) beliefs, ie, maintain a so-called “user model”. Apart from cooperativity aspects, such a model is also necessary for intelligent coherent dialog behavior in general.

Dialogue management in the Bell Labs communicator system

… International Conference on …, 2000

This paper describes a dialogue manager and its interaction with semantics and context tracking in a spoken dialogue system developed for general information retrieval and transaction applications. The dialogue system supports the following basic functionality: electronic form filling, database query, result navigation, attribute-value pair referencing, and value and reference resolution. General data structures and algorithms for representing and resolving ambiguity in a spoken dialogue system and a parsimonious parameterization for all application-dependent semantic and dialogue information are proposed. Dialogue management algorithms examine the semantics and dialogue state and adapt to the user's needs and task necessities. These algorithms are applied to a travel reservation application developed under the auspices of the DARPA Communicator project. The proposed algorithms are application-independent and facilitate ease of developing new spoken dialogue systems by changing only the semantics encoded in the prototype tree and the domain-dependent templates used by such components as the parser and the prompt generator.

Adaptive Information Presentation for Spoken Dialogue Systems: Evaluation with human subjects

We present evaluation results with human subjects for a novel data-driven approach to Natural Language Generation in spoken dialogue systems. We evaluate a trained Information Presentation (IP) strategy in a deployed tourist-information spoken dialogue system. The IP problem is formulated as statistical decision making under uncertainty using Reinforcement Learning, where both content planning and attribute selection are jointly optimised based on data collected in a Wizard-of-Oz study. After earlier work testing and training this model in simulation, we now present results from an extensive online user study, involving 131 users and more than 800 test dialogues, which explores its contribution to overall 'global' task success. We find that the trained Information Presentation strategy significantly improves dialogue task completion, with up to a 9.7% increase (30% relative) compared to the deployed dialogue system which uses conventional, hand-coded presentation prompts. We also present subjective evaluation results and discuss the implications of these results for future work in dialogue management and NLG.

Cooperative dialogue planning with user and situation models via example-based training

2004

Abstract To provide a high level of usability, spoken dialogue systems must generate cooperative responses for a wide variety of users and situations. We introduce a dialogue planning scheme which incorporates user and situation models, making such dialogue adaptation possible. Manually developing a set of dialogue rules to accommodate all possible model combinations is very difficult and obstructs system portability.