5 accepted papers
In this paper, we present multi-modal approaches to diet tracking. As health and well-being become increasingly important, mobile applications for diet tracking attract much interest. However, these applications often require users to log their meals based on relatively unreliable memory recall, the…
In this paper, we present our novel approach to the 6th Dialogue State Tracking Challenge (DSTC6) track for end-to-end goal-oriented dialogue, in which the goal is to select the best system response from among a list of candidates in a restaurant booking conversation. Our model uses a convolutional…
In this work, we investigate mapping both natural language food and quantity descriptions to matching USDA database entries. We demonstrate that a convolutional neural network (CNN) model with a softmax layer on top to directly predict the most likely database matches outperforms our previous state-…
Natural language processing research has made major advances with the concept of representing words, sentences, paragraphs, and even documents by embedded vector representations. We apply this idea to the problem of relating foods, as expressed in natural language meal descriptions, to corresponding…
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 metho…