Improving interpretation robustness in a tutorial dialogue system

Myrosia Dzikovska, Elaine Farrow, Johanna Moore

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

Abstract

We present an experiment aimed at improving interpretation robustness of a tutorial dialogue system that relies on detailed semantic interpretation and dynamic natural language feedback generation. We show that we can improve overall interpretation quality by combining the output of a semantic interpreter
with that of a statistical classifier trained on the subset of student utterances where semantic interpretation fails. This improves on a previous result which used a similar approach but trained the classifier on a substantially larger data set containing all student utterances. Finally, we discuss how the labels from the statistical classifier can be integrated effectively with the dialogue system’s existing error recovery policies.
Original languageEnglish
Title of host publicationProceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications Atlanta, Georgia, June 13 2013
PublisherASSOC COMPUTATIONAL LINGUISTICS-ACL
Pages293-299
Number of pages7
Publication statusPublished - 13 Jun 2013

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