Abstract
This paper describes the system submitted by NUIG-DSI to the WebNLG+ challenge 2020 in the RDF-to-text generation task for the English language. For this challenge, we leverage transfer learning by adopting the T5 model architecture for our submission and fine-tune the model on the WebNLG+ corpus. Our submission ranks among the top five systems for most of the automatic evaluation metrics achieving a BLEU score of 51.74 over all categories with scores of 58.23 and 45.57 across seen and unseen categories respectively.- Anthology ID:
- 2020.webnlg-1.15
- Volume:
- Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
- Month:
- 12
- Year:
- 2020
- Address:
- Dublin, Ireland (Virtual)
- Editors:
- Thiago Castro Ferreira, Claire Gardent, Nikolai Ilinykh, Chris van der Lee, Simon Mille, Diego Moussallem, Anastasia Shimorina
- Venue:
- WebNLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 137–143
- Language:
- URL:
- https://aclanthology.org/2020.webnlg-1.15
- DOI:
- Cite (ACL):
- Nivranshu Pasricha, Mihael Arcan, and Paul Buitelaar. 2020. NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation. In Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), pages 137–143, Dublin, Ireland (Virtual). Association for Computational Linguistics.
- Cite (Informal):
- NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation (Pasricha et al., WebNLG 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.webnlg-1.15.pdf
- Data
- DBpedia