Document Type
Conference Paper
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Computer Sciences
Abstract
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.
DOI
https://doi.org/10.21427/cvkg-0p89
Recommended Citation
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
Funder
ADAPT Centre, Science Foundation Ireland
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Included in
Artificial Intelligence and Robotics Commons, Computational Engineering Commons, Numerical Analysis and Scientific Computing Commons, Other Computer Sciences Commons, Theory and Algorithms Commons
Publication Details
The 22nd workshop on the Semantics and Pragmatics of Dialogue, SEMDIAL 2018