UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification

Laura Vásquez-Rodríguez, Nhung Nguyen, Matthew Shardlow, Sophia Ananiadou


Abstract
We present PromptLS, a method for fine-tuning large pre-trained Language Models (LM) to perform the task of Lexical Simplification. We use a predefined template to attain appropriate replacements for a term, and fine-tune a LM using this template on language specific datasets. We filter candidate lists in post-processing to improve accuracy. We demonstrate that our model can work in a) a zero shot setting (where we only require a pre-trained LM), b) a fine-tuned setting (where language-specific data is required), and c) a multilingual setting (where the model is pre-trained across multiple languages and fine-tuned in an specific language). Experimental results show that, although the zero-shot setting is competitive, its performance is still far from the fine-tuned setting. Also, the multilingual is unsurprisingly worse than the fine-tuned model. Among all TSAR-2022 Shared Task participants, our team was ranked second in Spanish and third in English.
Anthology ID:
2022.tsar-1.23
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:
218–224
Language:
URL:
https://aclanthology.org/2022.tsar-1.23
DOI:
Bibkey:
Cite (ACL):
Laura Vásquez-Rodríguez, Nhung Nguyen, Matthew Shardlow, and Sophia Ananiadou. 2022. UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification. In Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), pages 218–224, Abu Dhabi, United Arab Emirates (Virtual). Association for Computational Linguistics.
Cite (Informal):
UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification (Vásquez-Rodríguez et al., TSAR 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.tsar-1.23.pdf