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Found 13 papers, 2 papers with code
An Evaluation of Binary Comparative Lexical Complexity Models
Identifying complex words in texts is an important first step in text simplification (TS) systems.
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.
Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement Learning
Our innovative RLHF reward function surpassed existing RL text simplification reward functions in effectiveness.
Offensive Language Identification in Transliterated and Code-Mixed Bangla
Identifying offensive content in social media is vital for creating safe online communities.
Findings of the VarDial Evaluation Campaign 2023
This report presents the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2023.
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.
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.
Language Variety Identification with True Labels
Research has shown that this is a problematic assumption, particularly in the case of very similar languages (e. g., Croatian and Serbian) and national language varieties (e. g., Brazilian and European Portuguese), where texts may contain no distinctive marker of the particular language or variety.
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.
Overview of the HASOC Subtrack at FIRE 2022: Offensive Language Identification in Marathi
The widespread of offensive content online has become a reason for great concern in recent years, motivating researchers to develop robust systems capable of identifying such content automatically.
ALEXSIS-PT: A New Resource for Portuguese Lexical Simplification
To continue improving the performance of LS systems we introduce ALEXSIS-PT, a novel multi-candidate dataset for Brazilian Portuguese LS containing 9, 605 candidate substitutions for 387 complex words.
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.
LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Prediction
This paper describes team LCP-RIT's submission to the SemEval-2021 Task 1: Lexical Complexity Prediction (LCP).
