Generating Challenge Datasets for Task-Oriented Conversational Agents through Self-Play

Sourabh Majumdar, Serra Sinem Tekiroglu, Marco Guerini


Abstract
End-to-end neural approaches are becoming increasingly common in conversational scenarios due to their promising performances when provided with sufficient amount of data. In this paper, we present a novel methodology to address the interpretability of neural approaches in such scenarios by creating challenge datasets using dialogue self-play over multiple tasks/intents. Dialogue self-play allows generating large amount of synthetic data; by taking advantage of the complete control over the generation process, we show how neural approaches can be evaluated in terms of unseen dialogue patterns. We propose several out-of-pattern test cases each of which introduces a natural and unexpected user utterance phenomenon. As a proof of concept, we built a single and a multiple memory network, and show that these two architectures have diverse performances depending on the peculiar dialogue patterns.
Anthology ID:
R19-1081
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
693–702
Language:
URL:
https://aclanthology.org/R19-1081
DOI:
10.26615/978-954-452-056-4_081
Bibkey:
Cite (ACL):
Sourabh Majumdar, Serra Sinem Tekiroglu, and Marco Guerini. 2019. Generating Challenge Datasets for Task-Oriented Conversational Agents through Self-Play. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 693–702, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Generating Challenge Datasets for Task-Oriented Conversational Agents through Self-Play (Majumdar et al., RANLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/R19-1081.pdf