@inproceedings{sheang-saggion-2021-controllable,
title = "Controllable Sentence Simplification with a Unified Text-to-Text Transfer Transformer",
author = "Sheang, Kim Cheng and
Saggion, Horacio",
editor = "Belz, Anya and
Fan, Angela and
Reiter, Ehud and
Sripada, Yaji",
booktitle = "Proceedings of the 14th International Conference on Natural Language Generation",
month = aug,
year = "2021",
address = "Aberdeen, Scotland, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.inlg-1.38/",
doi = "10.18653/v1/2021.inlg-1.38",
pages = "341--352",
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."
}
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%0 Conference Proceedings
%T Controllable Sentence Simplification with a Unified Text-to-Text Transfer Transformer
%A Sheang, Kim Cheng
%A Saggion, Horacio
%Y Belz, Anya
%Y Fan, Angela
%Y Reiter, Ehud
%Y Sripada, Yaji
%S Proceedings of the 14th International Conference on Natural Language Generation
%D 2021
%8 August
%I Association for Computational Linguistics
%C Aberdeen, Scotland, UK
%F sheang-saggion-2021-controllable
%X 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.
%R 10.18653/v1/2021.inlg-1.38
%U https://aclanthology.org/2021.inlg-1.38/
%U https://doi.org/10.18653/v1/2021.inlg-1.38
%P 341-352
Markdown (Informal)
[Controllable Sentence Simplification with a Unified Text-to-Text Transfer Transformer](https://aclanthology.org/2021.inlg-1.38/) (Sheang & Saggion, INLG 2021)
ACL