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AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog

Tomáš Nekvinda, Ondřej Dušek


Abstract
We introduce AARGH, an end-to-end task-oriented dialog system combining retrieval and generative approaches in a single model, aiming at improving dialog management and lexical diversity of outputs. The model features a new response selection method based on an action-aware training objective and a simplified single-encoder retrieval architecture which allow us to build an end-to-end retrieval-enhanced generation model where retrieval and generation share most of the parameters. On the MultiWOZ dataset, we show that our approach produces more diverse outputs while maintaining or improving state tracking and context-to-response generation performance, compared to state-of-the-art baselines.
Anthology ID:
2022.sigdial-1.29
Volume:
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:
September
Year:
2022
Address:
Edinburgh, UK
Editors:
Oliver Lemon, Dilek Hakkani-Tur, Junyi Jessy Li, Arash Ashrafzadeh, Daniel Hernández Garcia, Malihe Alikhani, David Vandyke, Ondřej Dušek
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
283–297
Language:
URL:
https://aclanthology.org/2022.sigdial-1.29
DOI:
10.18653/v1/2022.sigdial-1.29
Bibkey:
Cite (ACL):
Tomáš Nekvinda and Ondřej Dušek. 2022. AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 283–297, Edinburgh, UK. Association for Computational Linguistics.
Cite (Informal):
AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog (Nekvinda & Dušek, SIGDIAL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.sigdial-1.29.pdf
Video:
 https://youtu.be/o_-G6L9wL9U
Code
 tomiinek/aargh
Data
MultiWOZ