Dzikovska, Myroslava

Farrow, Elaine

Moore, Johanna

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

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Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications

https://www.pure.ed.ac.uk/ws/files/14978590/W13_1738.pdf

Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications

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