105 citations · 509 across the 48 of their papers we have counts for
59 papers
Findings of the VarDial Evaluation Campaign 2023
Noëmi Aepli, Çağrı Çöltekin, Rob Van Der Goot +7
This report presents the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2023. The campaign is part of the tenth workshop on Natural Language Proce…
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…
Language Variety Identification with True Labels
Marcos Zampieri, Kai North, Tommi Jauhiainen +4
Language identification is an important first step in many IR and NLP applications. Most publicly available language identification datasets, however, are compiled under the assump…
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…
SOLD: Sinhala Offensive Language Dataset
Tharindu Ranasinghe, Isuri Anuradha, Damith Premasiri +4
The widespread of offensive content online, such as hate speech and cyber-bullying, is a global phenomenon. This has sparked interest in the artificial intelligence (AI) and natura…
Predicting the Type and Target of Offensive Social Media Posts in Marathi
Marcos Zampieri, Tharindu Ranasinghe, Mrinal Chaudhari +4
The presence of offensive language on social media is very common motivating platforms to invest in strategies to make communities safer. This includes developing robust machine le…
Overview of the HASOC Subtrack at FIRE 2022: Offensive Language Identification in Marathi
Tharindu Ranasinghe, Kai North, Damith Premasiri +1
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 conte…
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…
ALEXSIS-PT: A New Resource for Portuguese Lexical Simplification
Kai North, Marcos Zampieri, Tharindu Ranasinghe
Lexical simplification (LS) is the task of automatically replacing complex words for easier ones making texts more accessible to various target populations (e.g. individuals with l…
Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages
Thomas Mandl, Sandip Modha, Gautam Kishore Shahi +8
The widespread of offensive content online such as hate speech poses a growing societal problem. AI tools are necessary for supporting the moderation process at online platforms. F…
FBERT: A Neural Transformer for Identifying Offensive Content
Diptanu Sarkar, Marcos Zampieri, Tharindu Ranasinghe +1
Transformer-based models such as BERT, XLNET, and XLM-R have achieved state-of-the-art performance across various NLP tasks including the identification of offensive language and h…
Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi
Saurabh Gaikwad, Tharindu Ranasinghe, Marcos Zampieri +1
The widespread presence of offensive language on social media motivated the development of systems capable of recognizing such content automatically. Apart from a few notable excep…
An Ensemble Approach for Annotating Source Code Identifiers with Part-of-speech Tags
Christian D. Newman, Michael J. Decker, Reem S. AlSuhaibani +7
This paper presents an ensemble part-of-speech tagging approach for source code identifiers. Ensemble tagging is a technique that uses machine-learning and the output from multiple…
WLV-RIT at GermEval 2021: Multitask Learning with Transformers to Detect Toxic, Engaging, and Fact-Claiming Comments
Skye Morgan, Tharindu Ranasinghe, Marcos Zampieri
This paper addresses the identification of toxic, engaging, and fact-claiming comments on social media. We used the dataset made available by the organizers of the GermEval-2021 sh…
An Exploratory Analysis of the Relation Between Offensive Language and Mental Health
Ana-Maria Bucur, Marcos Zampieri, Liviu P. Dinu
In this paper, we analyze the interplay between the use of offensive language and mental health. We acquired publicly available datasets created for offensive language identificati…
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 (…
WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans
Tharindu Ranasinghe, Diptanu Sarkar, Marcos Zampieri +1
In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms. In response, social media platforms…
Multilingual Offensive Language Identification for Low-resource Languages
Tharindu Ranasinghe, Marcos Zampieri
Offensive content is pervasive in social media and a reason for concern to companies and government organizations. Several studies have been recently published investigating method…
LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Prediction
Abhinandan Desai, Kai North, Marcos Zampieri +1
This paper describes team LCP-RIT's submission to the SemEval-2021 Task 1: Lexical Complexity Prediction (LCP). The task organizers provided participants with an augmented version…
Domain-specific MT for Low-resource Languages: The case of Bambara-French
Allahsera Auguste Tapo, Michael Leventhal, Sarah Luger +2
Translating to and from low-resource languages is a challenge for machine translation (MT) systems due to a lack of parallel data. In this paper we address the issue of domain-spec…
Comparing Approaches to Dravidian Language Identification
Tommi Jauhiainen, Tharindu Ranasinghe, Marcos Zampieri
This paper describes the submissions by team HWR to the Dravidian Language Identification (DLI) shared task organized at VarDial 2021 workshop. The DLI training set includes 16,674…
MUDES: Multilingual Detection of Offensive Spans
Tharindu Ranasinghe, Marcos Zampieri
The interest in offensive content identification in social media has grown substantially in recent years. Previous work has dealt mostly with post level annotations. However, ident…
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…