Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning

Atsumoto Ohashi, Ryuichiro Higashinaka


Abstract
When a natural language generation (NLG) component is implemented in a real-world task-oriented dialogue system, it is necessary to generate not only natural utterances as learned on training data but also utterances adapted to the dialogue environment (e.g., noise from environmental sounds) and the user (e.g., users with low levels of understanding ability). Inspired by recent advances in reinforcement learning (RL) for language generation tasks, we propose ANTOR, a method for Adaptive Natural language generation for Task-Oriented dialogue via Reinforcement learning. In ANTOR, a natural language understanding (NLU) module, which corresponds to the user’s understanding of system utterances, is incorporated into the objective function of RL. If the NLG’s intentions are correctly conveyed to the NLU, which understands a system’s utterances, the NLG is given a positive reward. We conducted experiments on the MultiWOZ dataset, and we confirmed that ANTOR could generate adaptive utterances against speech recognition errors and the different vocabulary levels of users.
Anthology ID:
2022.coling-1.19
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
242–252
Language:
URL:
https://aclanthology.org/2022.coling-1.19
DOI:
Bibkey:
Cite (ACL):
Atsumoto Ohashi and Ryuichiro Higashinaka. 2022. Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 242–252, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning (Ohashi & Higashinaka, COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.19.pdf
Code
 nu-dialogue/antor
Data
MultiWOZ