Search Results for author: Kim Cheng SHEANG

Found 6 papers, 3 papers with code

Identification of complex words and passages in medical documents in French

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.

Controllable Sentence Simplification with a Unified Text-to-Text Transfer Transformer

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.

Language Modelling Sentence +1

Multilingual Controllable Transformer-Based Lexical Simplification

1 code implementation5 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.

Lexical Simplification Reading Comprehension

Controllable Lexical Simplification for English

1 code implementation6 Feb 2023 Kim Cheng SHEANG, Daniel Ferrés, Horacio Saggion

Fine-tuning Transformer-based approaches have recently shown exciting results on sentence simplification task.

Lexical Simplification Sentence

Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification

no code implementations6 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.

Lexical Simplification Text Simplification

Multilingual Complex Word Identification: Convolutional Neural Networks with Morphological and Linguistic Features

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.

Complex Word Identification Word Embeddings

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