Distributional semantics for understanding spoken meal descriptions
This paper presents ongoing language understanding experiments conducted as part of a larger effort to create a nutrition dialogue system that automatically extracts food concepts from a user's spoken meal description. We first discuss the technical approaches to understanding, including three methods for incorporating word vector features into conditional random field (CRF) models for semantic tagging, as well as classifiers for directly associating foods with properties. We report experiments on both text and spoken data from an in-domain speech recognizer. On text data, we show that the addition of word vector features significantly improves performance, achieving an F1 test score of 90.8 for semantic tagging and 86.3 for food-property association. On speech, the best model achieves an F1 test score of 87.5 for semantic tagging and 86.0 for association. Finally, we conduct an end-to-end system evaluation through a user study with human ratings of 83% semantic tagging accuracy.