Improved semantic parsers for if-then statements

Abstract

Digital personal assistants are becoming both more common and more useful. The major NLP challenge for personal assistants is machine understanding: translating natural language user commands into an executable representation. This paper focuses on understanding rules written as If-Then statements, though the techniques should be portable to other semantic parsing tasks. We view understanding as structure prediction and show improved models using both conventional techniques and neural network models. We also discuss various ways to improve generalization and reduce overfitting: synthetic training data from paraphrase, grammar combinations, feature selection and ensembles of multiple systems. An ensemble of these techniques achieves a new state of the art result with 8% accuracy improvement.