Abstract
E2E NLG Challenge is a shared task on generating restaurant descriptions from sets of key-value pairs. This paper describes the results of our participation in the challenge. We develop a simple, yet effective neural encoder-decoder model which produces fluent restaurant descriptions and outperforms a strong baseline. We further analyze the data provided by the organizers and conclude that the task can also be approached with a template-based model developed in just a few hours.- Anthology ID:
- W18-6557
- Volume:
- Proceedings of the 11th International Conference on Natural Language Generation
- Month:
- November
- Year:
- 2018
- Address:
- Tilburg University, The Netherlands
- Editors:
- Emiel Krahmer, Albert Gatt, Martijn Goudbeek
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 463–471
- Language:
- URL:
- https://aclanthology.org/W18-6557
- DOI:
- 10.18653/v1/W18-6557
- Cite (ACL):
- Yevgeniy Puzikov and Iryna Gurevych. 2018. E2E NLG Challenge: Neural Models vs. Templates. In Proceedings of the 11th International Conference on Natural Language Generation, pages 463–471, Tilburg University, The Netherlands. Association for Computational Linguistics.
- Cite (Informal):
- E2E NLG Challenge: Neural Models vs. Templates (Puzikov & Gurevych, INLG 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/W18-6557.pdf
- Data
- E2E