Discourse Structure in the TRAINS Project

DISCOURSE STRUCTURE IN THE TRAINS PROJECT James F. Allen Department of Computer Science University of Rochester Rochester, NY 14627 ABSTRACT In a natural dialog, a considerable proportion of the utterances actually relate to the maintenance of the dialog itself rather than to furthering the task or goals motivating the conversation. For example, many utterances serve to acknowledge, clarify, correct a previous utterance rather than pursue some goal in the domain. In addition, natural dialog is full of false starts, ungrammatical sentences and other complexities not found in in written language. This paper describes our recent efforts to define and construct a model of discourse interaction that handle dialogs that are rich in these natural dialog-related phenomena. INTRODUCTION The TRAINS project involves building an intelligent planning assistant that is eonversationally-proficit in natural language. The name of the project comes from the domain used to test and demonstrate the ideas: the system acts as an intelligent assistant to a person attempting to solve transportation problems involving freight trains and factories in a simulated world. The system assists in formulating plans, and monitors these plans as they are executed by the simulated agents in a simulated TRAINS world, providing updates and support to the human in replanning as necessary. The human should be able to communicate using unconstrained natural, spoken language. We have started to collect dialogs using a wizard scenario in the TRAINS domain and have built an initial prototype system. The current system uses keyboard input derived from the transcripts of actual spoken dialogs. The dialogs exhibit complex behavior as both the human and system take initiative at times in the dialog, and there are a large number of clarifications, corrections and acknowledgements. This short paper describes both our empirical work in analyzing the transcripts, and our theoretical work in defining a computational discourse model. THE DATA We decided to collect our own data rather than using an existing source, such as the ATIS corpus, for several reasons. First, the dialogs in ATIS are structured to emphasize question-answering rather than interactive problem solving. More importantly, the mixed modality interaction of the ATIS scenario inhibits most natural spoken dialog phenomena. In particular, the long pauses before responses and the table-based system output prevent natural follow-up, such as" acknowledgements, clarifications and confirmations that are common in spoken dioalog. Almost 50% of the soeech collected in our more natural setting was of these types. The TRAINS domain was carefully designed so that a significant part of it is within reach of current (or near future) capabilities of plan reasoning systems. Because of this, we should be able to fully specify and implement the reasoning underlying the "system" in the dialogs. If ATIS were extended to be a travel-planner rather than a database, the domains would be comparable. We have collected an initial corpus of natural spoken conversations between two people engaged in complex problem solving in the TRAINS world. One person (the "system") has most of the information and detail about the domain, but …

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