What's the trouble: automatically identifying problematic dialogues in DARPA communicator dialogue systems

Helen Wright Hastie, Rashmi Prasad, Marilyn Walker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Spoken dialogue systems promise efficient and natural access to information services from any phone. Recently, spoken dialogue systems for widely used applications such as email, travel information, and customer care have moved from research labs into commercial use. These applications can receive millions of calls a month. This huge amount of spoken dialogue data has led to a need for fully automatic methods for selecting a subset of caller dialogues that are most likely to be useful for further system improvement, to be stored, transcribed and further analyzed. This paper reports results on automatically training a Problematic Dialogue Identifier to classify problematic human-computer dialogues using a corpus of 1242 DARPA Communicator dialogues in the travel planning domain. We show that using fully automatic features we can identify classes of problematic dialogues with accuracies from 67% to 89%.
Original languageEnglish
Title of host publicationProceedings of the 40th Annual Meeting on Association for Computational Linguistics
PublisherAssociation for Computational Linguistics
Pages384-391
Number of pages8
ISBN (Print)1558608834
DOIs
Publication statusPublished - 6 Jul 2002
Event40th Annual Meeting of the Association for Computational Linguistics - Philadelphia, United States
Duration: 7 Jul 200212 Jul 2002
Conference number: 40

Conference

Conference40th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2002
Country/TerritoryUnited States
CityPhiladelphia
Period7/07/0212/07/02

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