Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications
Year: 2013
Type: conference-paper
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.
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
Funder: Office of Naval Research
OpenAlex ID: W1495448333