Document Type

Conference Paper


This item is available under a Creative Commons License for non-commercial use only


Computer Sciences

Publication Details

The 23rd Workshop on the Semantics and Pragmatics of Dialogue, SEMDIAL 2019


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.