In the development of dialog systems the discovery of the set of target intents to identify is a crucial first step that is often overlooked. Most intent detection works assume that a labelled dataset already exists, however creating these datasets is no trivial task and usually requires humans to manually analyse, decide on intent labels and tag accordingly. The field of Open Intent Discovery addresses this problem by automating the process of grouping utterances and providing the user with the discovered intents. Our Open Intent Discovery framework allows for the user to choose from a range of different techniques for each step in the discovery process, including the ability to extend previous works with a human-readable label generation stage. We also provide an analysis of the relationship between dataset features and optimal combination of techniques for each step to help others choose without having to explore every possible combination for their unlabelled data.

We investigate differences in user communication with live chat agents versus a commercial Intelligent Virtual Agent (IVA). This case study compares the two types of interactions in the same domain for the same company filling the same purposes. We compared 16,794 human-to-human conversations and 27,674 conversations with the IVA. Of those IVA conversations, 8,324 escalated to human live chat agents. We then investigated how human-to-human communication strategies change when users first communicate with an IVA in the same conversation thread. We measured quantity, quality, and diversity of language, and analyzed complexity using numerous features. We find that while the complexity of language did not significantly change between modes, the quantity and some quality metrics did vary significantly. This fair comparison provides unique insight into how humans interact with commercial IVAs and how IVA and chatbot designers might better curate training data when automating customer service tasks.