Identifying Domain Independent Update Intents in Task Based Dialogs.

One important problem in task-based conversations is that of effectively updating the belief estimates of user-mentioned slot-value pairs. Given a user utterance, the intent of a slot-value pair is captured using dialog acts (DA) expressed in that utterance. However, in certain cases, DA's fail to capture the actual update intent of the user. In this paper, we describe such cases and propose a new type of semantic class for user intents. This new type, Update Intents (UI), is directly related to the type of update a user intends to perform for a slot-value pair. We define five types of UI's, which are independent of the domain of the conversation. We build a multi-class classification model using LSTM's to identify the type of UI in user utterances in the Restaurant and Shopping domains. Experimental results show that our models achieve strong classification performance in terms of F-1 score.