Abstract
Neural Natural Language Generation (NLG) systems are well known for their unreliability. To overcome this issue, we propose a data augmentation approach which allows us to restrict the output of a network and guarantee reliability. While this restriction means generation will be less diverse than if randomly sampled, we include experiments that demonstrate the tendency of existing neural generation approaches to produce dull and repetitive text, and we argue that reliability is more important than diversity for this task. The system trained using this approach scored 100% in semantic accuracy on the E2E NLG Challenge dataset, the same as a template system.- Anthology ID:
- 2020.emnlp-main.230
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2877–2888
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.230
- DOI:
- 10.18653/v1/2020.emnlp-main.230
- Cite (ACL):
- Henry Elder, Alexander O’Connor, and Jennifer Foster. 2020. How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2877–2888, Online. Association for Computational Linguistics.
- Cite (Informal):
- How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue (Elder et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2020.emnlp-main.230.pdf