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Incrementality is a fundamental feature of language in real world use. To this point, however, the vast majority of work in automated dialogue processing has focused on language as turn based. In this paper we explore the challenge of incremental dialogue state tracking through the development and analysis of a multi-task approach to incremental dialogue state tracking. We present the design of our incremental dialogue state tracker in detail and provide evaluation against the well known Dialogue State Tracking Challenge 2 (DSTC2) dataset. In addition to a standard evaluation of the tracker, we also provide an analysis of the Incrementality phenomenon in our model’s performance by analyzing how early our models can produce correct predictions and how stable those predictions are. We find that the Multi-Task Learning-based model achieves state-of-the-art results for incremental processing.
Trinh, A., Ross, R. & Kelleher, J. (2018). A multi-task approach to incremental dialogue state tracking. SEMDIAL 2018 (AixDial): the 22nd workshop on the Semantics and Pragmatics of Dialogue, Aix-en-Provence, France, 8-10 November, 2018. doi:10.21427/cvkg-0p89