A New Dataset for Natural Language Understanding of Exercise Logs in a Food and Fitness Spoken Dialogue System

Abstract

Health and fitness are becoming increasingly important in the United States, as illustrated by the 70% of adults in the U.S. that are classified as overweight or obese, as well as globally, where obesity nearly tripled since 1975. Prior work used convolutional neural networks (CNNs) to understand a spoken sentence describing one’s meal, in order to expedite the meal-logging process. However, the system lacked a complementary exercise-logging component. We have created a new dataset of 3,000 natural language exercise-logging sentences. Each token was tagged as an Exercise, Feeling, or Other, and mapped to the most relevant exercise, as well as a score of how they felt on a scale from 1 to 10. We demonstrate the following: for intent detection (i.e., logging a meal or exercise), logistic regression achieves over 99% accuracy on a held-out test set; for semantic tagging, contextual embedding models achieve 93% F1 score, outperforming conditional random field models (CRFs); and recurrent neural networks (RNNs) trained on a multiclass classification task successfully map tagged exercise and feeling segments to database matches. By connecting how the user felt while exercising to the food they ate, in the future we may provide personalized and dynamic diet recommendations.