Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification

• DOI: 10.18653/v1/2022.tsar-1.31
• Publication Date: 2023-08-31T11:27:26Z
• Authors: Horacio Saggion; Sanja Štajner; Daniel Ferrés; Kim Cheng Sheang; Matthew Shardlow; Kai North; Marcos Zampieri
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Computation and Language; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Computation and Language (cs.CL); Machine Learning (cs.LG)

We report findings of the TSAR-2022 shared task on multilingual lexical simplification, organized as part of the Workshop on Text Simplification, Accessibility, and Readability TSAR-2022 held in conjunction with EMNLP 2022. The task called the Natural Language Processing research community to contribute with methods to advance the state of the art in multilingual lexical simplification for English, Portuguese, and Spanish. A total of 14 teams submitted the results of their lexical simplification systems for the provided test data. Results of the shared task indicate new benchmarks in Lexical Simplification with English lexical simplification quantitative results noticeably higher than those obtained for Spanish and (Brazilian) Portuguese.