Dialogue state tracking, or estimating and keeping tabs on a person's goals throughout a multiturn conversation, is one of the ways Alexa figures out what users want. By combining conversation history with the most recent command, Amazon's intelligent assistant can better map slot names -- the price of a hotel or its star rating, for example -- to slot values, or entities mentioned in a dialogue. Alexa already performs dialogue state tracking pretty effectively, but a team of scientists at Amazon's R&D division think there's room for improvement. In a new paper ("Dialog State Tracking: A Neural Reading Comprehension Approach") scheduled to be presented at the International Speech Communication Association's Special Interest Group on Discourse and Dialogue, they propose an AI system that formulates dialogue state tracking as a classic question-answering problem. In other words, their machine learning model decides on the slot value for each slot name after reading a conversational passage. The team reports that their technique yielded a 6.5% improvement in slot tracking accuracy over the previous state of the art in qualitative tests and that it had an accuracy of up to 96% per slot on a data set of development data.