1 code implementation • INLG (ACL) 2021 • Kim Cheng SHEANG, Horacio Saggion
Recently, a large pre-trained language model called T5 (A Unified Text-to-Text Transfer Transformer) has achieved state-of-the-art performance in many NLP tasks.
no code implementations • JEP/TALN/RECITAL 2022 • Kim Cheng SHEANG, Anaïs Koptient, Natalia Grabar, Horacio Saggion
Nous proposons de travail sur l’identification de mots et passages complexes dans les documents biomédicaux en français.
1 code implementation • 5 Jul 2023 • Kim Cheng SHEANG, Horacio Saggion
Moreover, further evaluation of our approach on the part of the recent TSAR-2022 multilingual LS shared-task dataset shows that our model performs competitively when compared with the participating systems for English LS and even outperforms the GPT-3 model on several metrics.
1 code implementation • 6 Feb 2023 • Kim Cheng SHEANG, Daniel Ferrés, Horacio Saggion
Fine-tuning Transformer-based approaches have recently shown exciting results on sentence simplification task.
no code implementations • 6 Feb 2023 • Horacio Saggion, Sanja Štajner, Daniel Ferrés, Kim Cheng SHEANG, Matthew Shardlow, Kai North, Marcos Zampieri
We report findings of the TSAR-2022 shared task on multilingual lexical simplification, organized as part of the Workshop on Text Simplification, Accessibility, and Readability TSAR-2022 held in conjunction with EMNLP 2022.
no code implementations • RANLP 2019 • Kim Cheng SHEANG
The paper is about our experiments with Complex Word Identification system using deep learning approach with word embeddings and engineered features.