A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)
This work addresses the challenge of diverse schemas in multi-domain task-oriented spoken dialogue systems, though it is incremental as it builds upon existing datasets and methods.
The paper tackles the problem of resolving referring expressions in spoken dialogue by modeling it as a user query reformulation task, resulting in the creation of the Contextual Query Rewrite (CQR) dataset and establishing initial baselines for this task.
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.