E2E NLG Challenge: Neural Models vs. Templates

Yevgeniy Puzikov, Iryna Gurevych


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
Venues:
INLG | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
463–471
Language:
URL:
https://aclanthology.org/W18-6557
DOI:
10.18653/v1/W18-6557
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/W18-6557.pdf