Abstract
In Natural Language Generation (NLG), one important limitation is the lack of common benchmarks on which to train, evaluate and compare data-to-text generators. In this paper, we make one step in that direction and introduce a method for automatically creating an arbitrary large repertoire of data units that could serve as input for generation. Using both automated metrics and a human evaluation, we show that the data units produced by our method are both diverse and coherent.- Anthology ID:
- C16-1141
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 1493–1502
- Language:
- URL:
- https://aclanthology.org/C16-1141
- DOI:
- Cite (ACL):
- Laura Perez-Beltrachini, Rania Sayed, and Claire Gardent. 2016. Building RDF Content for Data-to-Text Generation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1493–1502, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Building RDF Content for Data-to-Text Generation (Perez-Beltrachini et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/C16-1141.pdf