Database Search Results Disambiguation for Task-Oriented Dialog Systems

Anonymous

Paper Link: https://openreview.net/forum?id=tym7kAeuj8

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract:

As task-oriented dialog systems are becoming increasingly popular in our lives, more realistic tasks have been proposed and explored. However, new practical challenges arise. For instance, current dialog systems cannot effectively handle multiple
search results when querying a database, due to the lack of such scenarios in
existing public datasets. In this paper, we propose Database Search Result (DSR) Disambiguation, a novel task that focuses on disambiguating database search results, which enhances user experience by allowing them to choose from multiple options instead of just one. To study this task, we augment the popular task-oriented dialog datasets (MultiWOZ and SGD) with turns that resolve ambiguities by (a) synthetically generating turns through a pre-defined grammar, and (b) collecting human paraphrases for a subset. We find that training on our augmented dialog data improves the model's ability to deal with ambiguous scenarios, without sacrificing performance on unmodified turns. Furthermore, pre-fine tuning and multi-task learning help our model to improve performance on DSR-disambiguation even in the absence of in-domain data, suggesting that it can be learned as a universal dialog skill.
Our data and code will be made publicly available.

Presentation Mode: This paper will be presented in person in Seattle

Copyright Consent Signature (type Name Or NA If Not Transferrable): Kun Qian

Copyright Consent Name And Address: Columbia University, 116th and Broadway, New York, NY 10027