Alphaeus Dmonte
2022
GMU-WLV at TSAR-2022 Shared Task: Evaluating Lexical Simplification Models
Kai North
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Alphaeus Dmonte
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Tharindu Ranasinghe
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Marcos Zampieri
Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)
This paper describes team GMU-WLV submission to the TSAR shared-task on multilingual lexical simplification. The goal of the task is to automatically provide a set of candidate substitutions for complex words in context. The organizers provided participants with ALEXSIS a manually annotated dataset with instances split between a small trial set with a dozen instances in each of the three languages of the competition (English, Portuguese, Spanish) and a test set with over 300 instances in the three aforementioned languages. To cope with the lack of training data, participants had to either use alternative data sources or pre-trained language models. We experimented with monolingual models: BERTimbau, ELECTRA, and RoBERTA-largeBNE. Our best system achieved 1st place out of sixteen systems for Portuguese, 8th out of thirty-three systems for English, and 6th out of twelve systems for Spanish.