Document Type
Conference Paper
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Computer Sciences
Abstract
Dialogue State Tracking is arguably one of the most challenging tasks among dialogue processing problems due to the uncertainties of language and complexity of dialogue contexts. We argue that this problem is made more challenging by variable dependencies in the dialogue states that must be accounted for in processing. In this paper we give details on our motivation for this argument through statistical tests on a number of dialogue datasets. We also propose a machine learning-based approach called energy-based learning that tackles variable dependencies while performing prediction on the dialogue state tracking tasks.
DOI
https://doi.org/10.21427/1csd-yv51
Recommended Citation
Trinh, A, & Ross, R.J. & Kelleher, J.D. (2019). Investigating Variable Dependencies in Dialogue States,The 23rd Workshop on the Semantics and Pragmatics of Dialogue, SEMDIAL 2019, London, September 4-6th, 2019. doi:10.21427/1csd-yv51
Funder
Science Foundation Ireland, European Regional Development Fund (ERDF)
Publication Details
The 23rd Workshop on the Semantics and Pragmatics of Dialogue, SEMDIAL 2019