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Found 32 papers, 12 papers with code
Using NLP to quantify the environmental cost and diversity benefits of in-person NLP conferences
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
This project investigates the capabilities of Machine Translation models for generating translations at varying levels of readability, focusing on texts related to COVID-19.
An Evaluation of Binary Comparative Lexical Complexity Models
Identifying complex words in texts is an important first step in text simplification (TS) systems.
Agree to Disagree: Exploring Subjectivity in Lexical Complexity
Subjective factors affect our familiarity with different words.
Simple TICO-19: A Dataset for Joint Translation and Simplification of COVID-19 Texts
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.
MultiLS: A Multi-task Lexical Simplification Framework
We present MultiLS, the first LS framework that allows for the creation of a multi-task LS dataset.
BLESS: Benchmarking Large Language Models on Sentence Simplification
Our performance benchmark will be available as a resource for the development of future TS methods and evaluation metrics.
Overview of the BioLaySumm 2023 Shared Task on Lay Summarization of Biomedical Research Articles
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.
Investigating Large Language Models and Control Mechanisms to Improve Text Readability of Biomedical Abstracts
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
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.
Natural language processing on customer note data
Automatic analysis of customer data for businesses is an area that is of interest to companies.
Lexical Complexity Prediction: An Overview
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.
Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification
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.
Deanthropomorphising NLP: Can a Language Model Be Conscious?
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.
Lexical Simplification Benchmarks for English, Portuguese, and Spanish
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.
Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification
We present two convolutional neural networks for predicting the complexity of words and phrases in context on a continuous scale.
Investigating Text Simplification Evaluation
Modern text simplification (TS) heavily relies on the availability of gold standard data to build machine learning models.
SemEval-2021 Task 1: Lexical Complexity Prediction
This paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction.
Predicting Lexical Complexity in English Texts: The Complex 2.0 Dataset
We develop a protocol for the annotation of lexical complexity and use this to annotate a new dataset, CompLex 2. 0.
Complex Word Identification
Lexical Complexity Prediction
+1
Multi-Word Lexical Simplification
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.
HTSS: A Novel Hybrid Text Summarisation and Simplification Architecture
Our results show that our proposed HTSS model outperforms neural text simplification (NTS) on SARI score and abstractive text summarisation (ATS) on the ROUGE score.
Ranked #2 on
Text Simplification
on EurekaAlert
Detecting Multiword Expression Type Helps Lexical Complexity Assessment
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.
CombiNMT: An Exploration into Neural Text Simplification Models
This work presents a replication study of Exploring Neural Text Simplification Models (Nisioi et al., 2017).
CompLex --- A New Corpus for Lexical Complexity Prediction from Likert Scale Data
Predicting which words are considered hard to understand for a given target population is a vital step in many NLP applications such astext simplification.
CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Data
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.
Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table
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).
Manchester Metropolitan at SemEval-2018 Task 2: Random Forest with an Ensemble of Features for Predicting Emoji in Tweets
We present our submission to the Semeval 2018 task on emoji prediction.
Out in the Open: Finding and Categorising Errors in the Lexical Simplification Pipeline
Lexical simplification is the task of automatically reducing the complexity of a text by identifying difficult words and replacing them with simpler alternatives.
