Sentence Simplification Capabilities of Transfer-Based Models

Abstract

According to the official adult literacy report conducted in 24 highly-developed countries, more than 50% adults, on average, can only understand basic vocabulary, short sentences, and basic syntactic constructions. Everyday information found in news articles is thus inaccessible to many people, impeding their social inclusion and informed decision-making. Systems for automatic sentence simplification aim to provide scalable solution to this problem. In this paper, we propose new state-of-the-art sentence simplification systems for English and Spanish, and specifications for expert evaluation that are in accordance with well-established easy-to-read guidelines. We conduct expert evaluation of our new systems and the previous state-of-the-art systems for English and Spanish, and discuss strengths and weaknesses of each of them. Finally, we draw conclusions about the capabilities of the state-of-the-art sentence simplification systems and give some directions for future research.

Cite

Text

Stajner et al. "Sentence Simplification Capabilities of Transfer-Based Models." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21477

Markdown

[Stajner et al. "Sentence Simplification Capabilities of Transfer-Based Models." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/stajner2022aaai-sentence/) doi:10.1609/AAAI.V36I11.21477

BibTeX

@inproceedings{stajner2022aaai-sentence,
  title     = {{Sentence Simplification Capabilities of Transfer-Based Models}},
  author    = {Stajner, Sanja and Sheang, Kim Cheng and Saggion, Horacio},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {12172-12180},
  doi       = {10.1609/AAAI.V36I11.21477},
  url       = {https://mlanthology.org/aaai/2022/stajner2022aaai-sentence/}
}