A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)
Published in
arXiv.org(2019)External Links:
TL
TL;DR
This paper describes the methodology for creating the query reformulation extension to the dialog corpus, and presents an initial set of experiments to establish a baseline for the CQR task.
Abstract
We present Contextual Query Rewrite (CQR) a dataset for multi-domain task-oriented spoken dialogue systems that is an extension of the Stanford dialog corpus (Eric et al., 2017a). While previous approaches have addressed the issue of diverse schemas by learning candidate transformations (Naik et al., 2018), we instead model the reference resolution task as a user query reformulation task, where the dialog state is serialized into a natural language query that can be executed by the downstream spoken language understanding system. In this paper, we describe our methodology for creating the query reformulation extension to the dialog corpus, and present an initial set of experiments to establish a baseline for the CQR task. We have released the corpus to the public [1] to support further research in this area.
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