Easy contextual intent prediction and slot detection

Abstract

Spoken language understanding (SLU) is one of the main tasks of a dialog system, aiming to identify semantic components in user utterances. In this paper, we investigate the incorporation of context into the SLU tasks of intent prediction and slot detection. Using a corpus that contains session-level information, including the start and end of a session and the sequence of utterances within it, we experiment with the incorporation of information from previous intra-session utterances into the SLU tasks on a given utterance. For slot detection, we find that including features indicating the slots appearing in the previous utterances gives no significant increase in performance. In contrast, for intent prediction we find that a similar approach that incorporates the intent of the previous utterance as a feature yields relative error rate reductions of 6.7% on transcribed data and 8.7% on automatically-recognized data. We also find similar gains when treating intent prediction of utterance sequences as a sequential tagging problem via SVM-HMMs.