Search Results for author: Matthew Shardlow

Found 31 papers, 12 papers with code

Simple TICO-19: A Dataset for Joint Translation and Simplification of COVID-19 Texts

1 code implementation LREC 2022 Matthew Shardlow, Fernando Alva-Manchego

Specialist high-quality information is typically first available in English, and it is written in a language that may be difficult to understand by most readers.

Machine Translation Translation

Using NLP to quantify the environmental cost and diversity benefits of in-person NLP conferences

1 code implementation Findings (ACL) 2022 Piotr Przybyła, Matthew Shardlow

The environmental costs of research are progressively important to the NLP community and their associated challenges are increasingly debated.

Towards Readability-Controlled Machine Translation of COVID-19 Texts

no code implementations EAMT 2022 Fernando Alva-Manchego, Matthew Shardlow

This project investigates the capabilities of Machine Translation models for generating translations at varying levels of readability, focusing on texts related to COVID-19.

Machine Translation Text Simplification +1

Overview of the BioLaySumm 2023 Shared Task on Lay Summarization of Biomedical Research Articles

no code implementations29 Sep 2023 Tomas Goldsack, Zheheng Luo, Qianqian Xie, Carolina Scarton, Matthew Shardlow, Sophia Ananiadou, Chenghua Lin

This paper presents the results of the shared task on Lay Summarisation of Biomedical Research Articles (BioLaySumm), hosted at the BioNLP Workshop at ACL 2023.

Lay Summarization

Large Language Models and Control Mechanisms Improve Text Readability of Biomedical Abstracts

1 code implementation22 Sep 2023 Zihao Li, Samuel Belkadi, Nicolo Micheletti, Lifeng Han, Matthew Shardlow, Goran Nenadic

In this work, we investigate the ability of state-of-the-art large language models (LLMs) on the task of biomedical abstract simplification, using the publicly available dataset for plain language adaptation of biomedical abstracts (\textbf{PLABA}).

Deep Learning Approaches to Lexical Simplification: A Survey

no code implementations19 May 2023 Kai North, Tharindu Ranasinghe, Matthew Shardlow, Marcos Zampieri

To reflect these recent advances, we present a comprehensive survey of papers published between 2017 and 2023 on LS and its sub-tasks with a special focus on deep learning.

Lexical Simplification Text Simplification

Lexical Complexity Prediction: An Overview

no code implementations8 Mar 2023 Kai North, Marcos Zampieri, Matthew Shardlow

Finally, we include brief sections on applications of lexical complexity prediction, such as readability and text simplification, together with related studies on languages other than English.

Lexical Complexity Prediction Reading Comprehension +1

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

Deanthropomorphising NLP: Can a Language Model Be Conscious?

no code implementations21 Nov 2022 Matthew Shardlow, Piotr Przybyła

However, here we take the position that such a large language model cannot be sentient, or conscious, and that LaMDA in particular exhibits no advances over other similar models that would qualify it.

Language Modelling Large Language Model

Lexical Simplification Benchmarks for English, Portuguese, and Spanish

2 code implementations12 Sep 2022 Sanja Stajner, Daniel Ferres, Matthew Shardlow, Kai North, Marcos Zampieri, Horacio Saggion

To showcase the usability of the dataset, we adapt two state-of-the-art lexical simplification systems with differing architectures (neural vs.\ non-neural) to all three languages (English, Spanish, and Brazilian Portuguese) and evaluate their performances on our new dataset.

Lexical Simplification

Investigating Text Simplification Evaluation

1 code implementation Findings (ACL) 2021 Laura Vásquez-Rodríguez, Matthew Shardlow, Piotr Przybyła, Sophia Ananiadou

Modern text simplification (TS) heavily relies on the availability of gold standard data to build machine learning models.

Text Simplification

Multi-Word Lexical Simplification

3 code implementations COLING 2020 Piotr Przyby{\l}a, Matthew Shardlow

In this work we propose the task of multi-word lexical simplification, in which a sentence in natural language is made easier to understand by replacing its fragment with a simpler alternative, both of which can consist of many words.

Language Modelling Lexical Simplification

Detecting Multiword Expression Type Helps Lexical Complexity Assessment

1 code implementation LREC 2020 Ekaterina Kochmar, Sian Gooding, Matthew Shardlow

In this work, we re-annotate the Complex Word Identification Shared Task 2018 dataset of Yimam et al. (2017), which provides complexity scores for a range of lexemes, with the types of MWEs.

Complex Word Identification Text Simplification +1

CombiNMT: An Exploration into Neural Text Simplification Models

no code implementations LREC 2020 Michael Cooper, Matthew Shardlow

This work presents a replication study of Exploring Neural Text Simplification Models (Nisioi et al., 2017).

Text Simplification

CompLex --- A New Corpus for Lexical Complexity Prediction from Likert Scale Data

no code implementations LREC 2020 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 astext simplification.

Binary Classification Complex Word Identification +1

CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Data

1 code implementation16 Mar 2020 Matthew Shardlow, Michael Cooper, Marcos Zampieri

With a few exceptions, previous studies have approached the task as a binary classification task in which systems predict a complexity value (complex vs. non-complex) for a set of target words in a text.

Binary Classification Complex Word Identification +2

Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table

1 code implementation ACL 2019 Matthew Shardlow, Raheel Nawaz

We also show improvement against baselines including the original text (2. 79) and using the phrase table without the neural text simplification software (2. 94).

Text Simplification

Out in the Open: Finding and Categorising Errors in the Lexical Simplification Pipeline

no code implementations LREC 2014 Matthew Shardlow

Lexical simplification is the task of automatically reducing the complexity of a text by identifying difficult words and replacing them with simpler alternatives.

Lexical Simplification Text Generation +2

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