GMU-WLV at TSAR-2022 Shared Task: Evaluating Lexical Simplification Models

Kai North, Alphaeus Dmonte, Tharindu Ranasinghe, Marcos Zampieri


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.
Anthology ID:
2022.tsar-1.30
Volume:
Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Virtual)
Venue:
TSAR
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
264–270
Language:
URL:
https://aclanthology.org/2022.tsar-1.30
DOI:
Bibkey:
Cite (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.
Cite (Informal):
GMU-WLV at TSAR-2022 Shared Task: Evaluating Lexical Simplification Models (North et al., TSAR 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.tsar-1.30.pdf