Combining semantic interpretation and statistical classification for improved explanation processing in a tutorial dialogue system

Abstract

We present an approach for combining symbolic interpretation and statistical classification in the natural language processing (NLP) component of a tutorial dialogue system. Symbolic NLP approaches support dynamic generation of context-adaptive natural language feedback, but lack robustness. In contrast, statistical classification approaches are robust to ill-formed input but provide less detail for context-specific feedback generation. We describe a system design that combines symbolic interpretation with statistical classification to support context-adaptive, dynamically generated natural language feedback, and show that the combined system significantly improves interpretation quality while retaining the adaptivity benefits of a symbolic interpreter. © 2013 Springer-Verlag Berlin Heidelberg.