Corpus and Annotation Towards NLU for Customer Ordering Dialogs

Ordering products and services through virtual agents is possible but suffers limitations on the kind of ordering that is possible or on the naturalness of the conversation. We address these limitations by collecting a corpus of human-human dialogs in the food ordering domain. We create a food focused annotation scheme that is tailored for this corpus but customizable for other applications. After annotating the corpus, we find corpus characteristics that may make it more natural, such as complexity of food item mentions and use of multiple intent utterances. Furthermore, we train and evaluate preliminary statistical item and intent models using the annotated corpus.