The belief that humans will be able to interact with computers in conversational speech has long been a favorite subject in science fiction, reflecting the persistent belief that spoken dialogue would be the most natural and powerful user interface to computers. With recent improvements in computer technology and in speech and language processing, such systems are starting to appear feasible. There are significant technical problems that still need to be solved before speech-driven interfaces become truly conversational. This article describes the results of a 10-year effort building robust spoken dialogue systems at the University of Rochester.

The belief that humans will be able to interact with computers in conversational speech has long been a favorite subject in science fiction, reflecting the persistent belief that spoken dialogue would be the most natural and powerful user interface to computers. With recent improvements in computer technology and in speech and language processing, such systems are starting to appear feasible. There are significant technical problems that still need to be solved before speech-driven interfaces become truly conversational. This article describes the results of a 10-year effort building robust spoken dialogue systems at the University of Rochester.

Language processing has a large practical potential in intelligent interfaces if we take into account multiple modalities of communication. Multi-modality refers to the perception of different coordinated media used in delivering a message as well as the combination of various attitudes in relation to communication. In particular, the integration of natural language processing and hypermedia allows each modality to overcome the constraints of the other, resulting in a novel class of integrated environments for complex exploration and information access. Information presentation is a key element of such environments; generation techniques can contribute to their quality by producing texts ex novo or flexibly adapting existing material to the current situation. A great opportunity arises for intelligent interfaces and language technology of this kind to play an important role for individual-oriented cultural tourism. In the article, reference is made to some prototypes developed at IRST that were conceived for this specific area. A recent project concentrated on the combination of two forms of navigation taking place at the same time -- one in information space, the other in physical space. Collaboration, an important topic for intelligent interfaces, is also discussed.

Recently, a number of authors have proposed treating dialogue systems as Markov decision processes (MDPs). However, the practical application ofMDP algorithms to dialogue systems faces a number of severe technical challenges. We have built a general software tool (RLDS, for Reinforcement Learning for Dialogue Systems) based on the MDP framework, and have applied it to dialogue corpora gathered from two dialogue systems built at AT&T Labs. Our experiments demonstrate that RLDS holds promise as a tool for "browsing" and understanding correlations in complex, temporally dependent dialogue corpora.

Recently, a number of authors have proposed treating dialogue systems as Markov decision processes (MDPs). However, the practical application ofMDP algorithms to dialogue systems faces a number of severe technical challenges. We have built a general software tool (RLDS, for Reinforcement Learning for Dialogue Systems) based on the MDP framework, and have applied it to dialogue corpora gathered from two dialogue systems built at AT&T Labs. Our experiments demonstrate that RLDS holds promise as a tool for "browsing" and understanding correlations in complex, temporally dependent dialogue corpora.

Recently,a number of authorshave proposedtreating dialogue systems as Markov decision processes(MDPs). However,the practicalapplicationofMDP algorithms to dialogue systems faces a numberof severe technicalchallenges.We have built a general software tool (RLDS, for ReinforcementLearning for Dialogue Systems) on the MDP framework, and have applied it to dialogue corpora gatheredbased from two dialoguesystemsbuilt at AT&T Labs. Our experimentsdemonstratethat RLDS holds promise as a tool for "browsing" and understandingcorrelationsin complex, temporallydependentdialogue corpora.

This paper describes a novel method by which a spoken dialogue system can learn to choose an optimal dialogue strategy from its experience interacting with human users. The method is based on a combination of reinforcement learning and performance modeling of spoken dialogue systems. The reinforcement learning component applies Q-learning (Watkins, 1989), while the performance modeling component applies the PARADISE evaluation framework (Walker et al., 1997) to learn the performance function (reward) used in reinforcement learning. We illustrate the method with a spoken dialogue system named ELVIS (EmaiL Voice Interactive System), that supports access to email over the phone. We conduct a set of experiments for training an optimal dialogue strategy on a corpus of 219 dialogues in which human users interact with ELVIS over the phone. We then test that strategy on a corpus of 18 dialogues. We show that ELVIS can learn to optimize its strategy selection for agent initiative, for reading messages, and for summarizing email folders.

It is a northern European city of 100,000, almost on the border between the Republic of Ireland and Northern Ireland. The local press (The Derry Journal north Derry coast, with beautiful meetings enjoyed themselves and & Belfast Telegraph) and radio (BBC beaches at Benone and Castlenock expressed their congratulations on Northern Ireland) ran a number of and then through Coleraine to the the program and organization. Also, articles leading up to and during the seaside resorts of Portstewart and for the first time, AICS attracted a conference. All plenary invited speaker Portrush. A few kilometers further large number of delegates and papers talks and the panel session went out along the north Antrim coast, we from abroad, including many from on streaming video and audio, stored arrive at the Giants' Causeway and the United Kingdom, Europe, and Sauce!); Gweedore, home of the Clannad and live with the possibility of phonein for Pattern Recognition (IAPR), the More details on all the data mining and knowledge discovery, the CSSI, was run as "MIND-II: Computational events are available at www.infm.ulst. Project, multimedia, and distributed are particularly welcome! which integrates speech and language object computing (www.infc.ulst.ac. Ever since George Boolean processing as applied to a spoken dialogue uk/informatics/). Knowledge Engineering Laboratory (see Dennett's Joycean Okada focused on a similar theme to (NIKEL), a joint venture with machine), Claude Shannon Von Hahn with his paper " US, 1956) we have been generation system for integrating into artificial intelligence.

Cue phrases may be used in a discourse sense to explicitly signal discourse structure, but also in a sentential sense to convey semantic rather than structural information. Correctly classifying cue phrases as discourse or sentential is critical in natural language processing systems that exploit discourse structure, e.g., for performing tasks such as anaphora resolution and plan recognition. This paper explores the use of machine learning for classifying cue phrases as discourse or sentential. Two machine learning programs (Cgrendel and C4.5) are used to induce classification models from sets of pre-classified cue phrases and their features in text and speech. Machine learning is shown to be an effective technique for not only automating the generation of classification models, but also for improving upon previous results. When compared to manually derived classification models already in the literature, the learned models often perform with higher accuracy and contain new linguistic insights into the data. In addition, the ability to automatically construct classification models makes it easier to comparatively analyze the utility of alternative feature representations of the data. Finally, the ease of retraining makes the learning approach more scalable and flexible than manual methods.

Artificial Intelligence 8:155-173