Sentence Simplification Capabilities of Transfer-Based Models
| Autor: | Sanja Štajner, Kim Cheng Sheang, Horacio Saggion |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Proceedings of the AAAI Conference on Artificial Intelligence. 36:12172-12180 |
| ISSN: | 2374-3468 2159-5399 |
| DOI: | 10.1609/aaai.v36i11.21477 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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