Sentence Simplification Capabilities of Transfer-Based Models
Keywords:AI For Social Impact (AISI Track Papers Only)
AbstractAccording 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.
How to Cite
Štajner, S., Sheang, K. C., & Saggion, H. (2022). Sentence Simplification Capabilities of Transfer-Based Models. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12172-12180. https://doi.org/10.1609/aaai.v36i11.21477
AAAI Special Track on AI for Social Impact