Abstract
The current study investigated novel techniques and methods for trainable approaches to data-to-text generation. Neural Machine Translation was explored for the conversion from data to text as well as the addition of extra templatization steps of the data input and text output in the conversion process. Evaluation using BLEU did not find the Neural Machine Translation technique to perform any better compared to rule-based or Statistical Machine Translation, and the templatization method seemed to perform similarly or sometimes worse compared to direct data-to-text conversion. However, the human evaluation metrics indicated that Neural Machine Translation yielded the highest quality output and that the templatization method was able to increase text quality in multiple situations.- Anthology ID:
- W18-6504
- 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:
- 35–45
- Language:
- URL:
- https://aclanthology.org/W18-6504
- DOI:
- 10.18653/v1/W18-6504
- Cite (ACL):
- Chris van der Lee, Emiel Krahmer, and Sander Wubben. 2018. Automated learning of templates for data-to-text generation: comparing rule-based, statistical and neural methods. In Proceedings of the 11th International Conference on Natural Language Generation, pages 35–45, Tilburg University, The Netherlands. Association for Computational Linguistics.
- Cite (Informal):
- Automated learning of templates for data-to-text generation: comparing rule-based, statistical and neural methods (van der Lee et al., INLG 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/W18-6504.pdf