This paper presents initial results in the application of Value Directed Compression (VDC) to spoken dialogue management belief states for reasoning about complex user goals. On a small but realistic SDS problem VDC generates a lossless compression which achieves a 6-fold reduction in the number of dialogue states required by a Partially Observable Markov Decision Process (POMDP) dialogue manag...

Dialogue systems are one of the most interesting applications of speech and language technologies. There have recently been some attempts to build dialogue systems in Spanish, and some corpora have been acquired and annotated. Using these corpora, statistical machine learning methods can be applied to try to solve problems in spoken dialogue systems. In this paper, two statistical models based ...

This paper details a software architecture for discourse processing in spoken dialogue systems, where the three component tasks of discourse processing are (1) Dialogue Management, (2) Context Tracking, and (3) Pragmatic Adaptation. We define these three component tasks and describe their roles in a complex, near-future scenario in which multiple humans interact with each other and with compute...

Context is of crucial importance to language understanding in general and plays a central role in spoken language dialogue systems design. Context, however, is hard to define. In this paper context is viewed as denoting a collection of aspects or contextual elements each of which may be defined and analysed with respect to its specific contribution to dialogue understanding. Massive exploitatio...

We compare the relative utility of different automatically computable linguistic feature sets for modeling student learning in computer dialogue tutoring. We use the PARADISE framework (multiple linear regression) to build a learning model from each of 6 linguistic feature sets: 1) surface features, 2) semantic features, 3) pragmatic features, 4) discourse structure features, 5) local dialogue ...

This paper argues for the use of more natural and flexible clarification questions in spoken dialogue systems. Several forms and aspects of clarification questions are discussed and exemplified by examples from human-human dialogue corpora. We show how clarification questions fit into the broader context of miscommunication and describe the types of information spoken dialogue systems need in o...

Viewing dialogue management as a reinforcement learning task enables a system to learn to act optimally by maximising a reward function. This reward function is designed to induce the system behaviour required for the target application and for goal-oriented applications, this usually means fulfilling the user’s goal as efficiently as possible. However, in real-world spoken dialogue system appl...

Our research goal is to investigate whether previous findings and methods in the area of tutorial dialogue can be generalized across dialogue corpora that differ in domain (mechanics versus electricity in physics), modality (spoken versus typed), and tutor type (computer versus human). We first present methods for unifying our prior coding and analysis methods. We then show that many of our pri...

This paper presents construction of a spoken dialogue system using a large-scale spoken dialogue corpus with intention tags. In this system, all of main components, such as speech understanding, dialogue management, and response generation, are constructed with corpus-based methods. An evaluation experiment using a test set has shown that the performance of the corpus-based dialogue system is i...

The provision of automatic systems that can provide conversational practice for beginners would make a valuable addition to existing aids for foreign language teaching. To achieve this goal, the SCILL (Spoken Conversational Interaction for Language Learning) project is developing a spoken dialogue system that is capable of maintaining interactive dialogues with non-native students in the target...