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This paper reports an ongoing effort to derive linear discourse structures from a corpus of telephone conversations. First, we would like to determine how reliably human annotators can tag discourse segments in dialogues. Second, we begin to investigate how to build machine models for performing this annotation task. To carry out our research, we use a corpus of transcribed and annotated human-human dialogues in a specific information retrieval domain (Movie theater schedules). We conducted an experiment in which 25 different dialogues have each been antotated by at least seven different people. We found that the average precision and recall among annotators in placing segment boundaries is 84.3%, and in assigning segment purpose labels is 80.1%. A simple discourse segment parser based on finite state machines is able to cover 56% of the same dialogues. When the finite state grammar is able to analyse a dialogue, it agrees with human annotators in placing segment boundaries with 59.4% precision and 66.4% recall, and it agrees in segment label accuracy at the 59% level.
A0103.pdf
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In this paper we show how it is possible to design and implement a general architecture that is suitable for the rapid development of human/machine natural language, mixed initiative dialogue systems. The architecture proposed here relies on the assumption that a dialogue system can be modularized into different actions or functions that can be designed separately and implement basic aspects of the dialogue behavior, and a strategy that is fairly independent of the particular application.
A0329.pdf
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Spoken dialog systems interpret a user's request and engage in conversation if the need arises. It is the responsibility of the dialog manager to determine if this need is present and how to proceed. Our spoken dialog system is constructed to sufficiently understand a user's response to the open-ended prompt 'How may I help you?' in order to route a caller to an appropriate destination, with subsequent processing for information retrieval or call completion. In this paper we describe how to structure the relationships among the call types into an inheritance hierarchy. We then describe an algorithm which exploits this hierarchy and the output of a spoken language understanding module to generate a set of semantically consistent inputs.
A0346.pdf
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Recent progress in the field of spoken natural language understanding expanded the scope of spoken language systems to include mixed initiative dialogue. Currently there are no agreed upon theoretical foundations for the design of such systems. In this work we propose a stochastic model of computer-human interactions. This model can be used for learning and adaptation of the dialogue strategy and for objective evaluation.
A0474.pdf
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One of the most important causes of failure in spoken dialogue systems is usually neglected: the problem of words that are not covered by the system's vocabulary (out-of-vocabulary or OOVwords). In this paper a methodology is described for the detection, classification and processing of OOV words in an automatic train timetable information system [2]. The various extensions that had to be effected on the different modules of the system are reported, resulting in the design of appropriate dialogue strategies, as are encouraging evaluation results on the new versions of the word recogniser and the linguistic processor.
A0687.pdf
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In this paper we describe the possibility to carry out clarification dialogues in the framework of the face-to-face translation system verbmobil. We focus on a special subtype of clarification dialogues which occur when the system has insufficient information to continue processing. The clarification dialogues currently incorporated in our system concern three aspects: (i) phonological ambiguities, (ii) unknown words, and (iii) semantic inconsistencies. We describe each of these subdialogues in detail and discuss the extensions and changes that had to be made to the overall system in order to allow for clarification dialogues. An outlook on future developments concludes the paper.
A0903.pdf
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