33 citations · 86 across the 13 of their papers we have counts for
13 papers
Text Simplification with Sentence Embeddings
Matthew Shardlow
Sentence embeddings can be decoded to give approximations of the original texts used to create them. We explore this effect in the context of text simplification, demonstrating tha…
Large Language Models for Biomedical Text Simplification: Promising But Not There Yet
Zihao Li, Samuel Belkadi, Nicolo Micheletti +3
In this system report, we describe the models and methods we used for our participation in the PLABA2023 task on biomedical abstract simplification, part of the TAC 2023 tracks. Th…
MultiLS: A Multi-task Lexical Simplification Framework
Kai North, Tharindu Ranasinghe, Matthew Shardlow +1
Lexical Simplification (LS) automatically replaces difficult to read words for easier alternatives while preserving a sentence's original meaning. LS is a precursor to Text Simplif…
BLESS: Benchmarking Large Language Models on Sentence Simplification
Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez +4
We present BLESS, a comprehensive performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS). We examine how wel…
Investigating Large Language Models and Control Mechanisms to Improve Text Readability of Biomedical Abstracts
Zihao Li, Samuel Belkadi, Nicolo Micheletti +3
Biomedical literature often uses complex language and inaccessible professional terminologies. That is why simplification plays an important role in improving public health literac…
Deep Learning Approaches to Lexical Simplification: A Survey
Kai North, Tharindu Ranasinghe, Matthew Shardlow +1
Lexical Simplification (LS) is the task of replacing complex for simpler words in a sentence whilst preserving the sentence's original meaning. LS is the lexical component of Text…
Lexical Complexity Prediction: An Overview
Kai North, Marcos Zampieri, Matthew Shardlow
The occurrence of unknown words in texts significantly hinders reading comprehension. To improve accessibility for specific target populations, computational modelling has been app…
Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification
Horacio Saggion, Sanja Štajner, Daniel Ferrés +4
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 TSA…
Lexical Simplification Benchmarks for English, Portuguese, and Spanish
Sanja Stajner, Daniel Ferres, Matthew Shardlow +3
Even in highly-developed countries, as many as 15-30\% of the population can only understand texts written using a basic vocabulary. Their understanding of everyday texts is limite…
SemEval-2021 Task 1: Lexical Complexity Prediction
Matthew Shardlow, Richard Evans, Gustavo Henrique Paetzold +1
This paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction. We provided participants with an augmented version of the CompLex Corpus (…
Predicting Lexical Complexity in English Texts: The Complex 2.0 Dataset
Matthew Shardlow, Richard Evans, Marcos Zampieri
Identifying words which may cause difficulty for a reader is an essential step in most lexical text simplification systems prior to lexical substitution and can also be used for as…
Detecting Multiword Expression Type Helps Lexical Complexity Assessment
Ekaterina Kochmar, Sian Gooding, Matthew Shardlow
Multiword expressions (MWEs) represent lexemes that should be treated as single lexical units due to their idiosyncratic nature. Multiple NLP applications have been shown to benefi…
CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Data
Matthew Shardlow, Michael Cooper, Marcos Zampieri
Predicting which words are considered hard to understand for a given target population is a vital step in many NLP applications such as text simplification. This task is commonly r…