UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification
Laura Vásquez-Rodríguez, Nhung T. H. Nguyen, Matthew Shardlow, Sophia Ananiadou · 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 finetuned model.Among all TSAR-2022 Shared Task participants, our team was ranked second in Spanish and third in English.