Abstract
Recently, a large pre-trained language model called T5 (A Unified Text-to-Text Transfer Transformer) has achieved state-of-the-art performance in many NLP tasks. However, no study has been found using this pre-trained model on Text Simplification. Therefore in this paper, we explore the use of T5 fine-tuning on Text Simplification combining with a controllable mechanism to regulate the system outputs that can help generate adapted text for different target audiences. Our experiments show that our model achieves remarkable results with gains of between +0.69 and +1.41 over the current state-of-the-art (BART+ACCESS). We argue that using a pre-trained model such as T5, trained on several tasks with large amounts of data, can help improve Text Simplification.- Anthology ID:
- 2021.inlg-1.38
- Volume:
- Proceedings of the 14th International Conference on Natural Language Generation
- Month:
- August
- Year:
- 2021
- Address:
- Aberdeen, Scotland, UK
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 341–352
- Language:
- URL:
- https://aclanthology.org/2021.inlg-1.38
- DOI:
- Cite (ACL):
- Kim Cheng Sheang and Horacio Saggion. 2021. Controllable Sentence Simplification with a Unified Text-to-Text Transfer Transformer. In Proceedings of the 14th International Conference on Natural Language Generation, pages 341–352, Aberdeen, Scotland, UK. Association for Computational Linguistics.
- Cite (Informal):
- Controllable Sentence Simplification with a Unified Text-to-Text Transfer Transformer (Sheang & Saggion, INLG 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.inlg-1.38.pdf
- Code
- kimchengsheang/ts_t5
- Data
- ASSET, TurkCorpus, WikiLarge