UoM and MMU @ TSAR2022-ST — PromptLS: Prompt Learning for Lexical Simplification.
Published in In Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), Abu Dhabi, United Arab Emirates (Virtual)., 2022
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.
Recommended citation: Vásquez-Rodríguez, L., Nguyen, N., Shardlow, M. and Ananiadou, S.. 2022. UoM and MMU @ TSAR2022-ST — PromptLS: 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.
Recommended citation: Vásquez-Rodríguez, L., Nguyen, N., Shardlow, M. and Ananiadou, S.. 2022. UoM and MMU @ TSAR2022-ST — PromptLS: 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.
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