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        <title>GMU-WLV at TSAR-2022 Shared Task: Evaluating Lexical Simplification Models</title>
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            <title>Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T GMU-WLV at TSAR-2022 Shared Task: Evaluating Lexical Simplification Models
%A North, Kai
%A Dmonte, Alphaeus
%A Ranasinghe, Tharindu
%A Zampieri, Marcos
%Y Štajner, Sanja
%Y Saggion, Horacio
%Y Ferrés, Daniel
%Y Shardlow, Matthew
%Y Sheang, Kim Cheng
%Y North, Kai
%Y Zampieri, Marcos
%Y Xu, Wei
%S Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Virtual)
%F north-etal-2022-gmu
%X 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.
%R 10.18653/v1/2022.tsar-1.30
%U https://aclanthology.org/2022.tsar-1.30
%U https://doi.org/10.18653/v1/2022.tsar-1.30
%P 264-270

Markdown (Informal)

[GMU-WLV at TSAR-2022 Shared Task: Evaluating Lexical Simplification Models](https://aclanthology.org/2022.tsar-1.30) (North et al., TSAR 2022)

• GMU-WLV at TSAR-2022 Shared Task: Evaluating Lexical Simplification Models (North et al., TSAR 2022)

ACL

• Kai North, Alphaeus Dmonte, Tharindu Ranasinghe, and Marcos Zampieri. 2022. GMU-WLV at TSAR-2022 Shared Task: Evaluating Lexical Simplification Models. In Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), pages 264–270, Abu Dhabi, United Arab Emirates (Virtual). Association for Computational Linguistics.