Papers by Tharindu Ranasinghe
MUDES: Multilingual Detection of Offensive Spans (2021.naacl-demos)
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| Challenge: | Identifying offensive spans in texts is the goal of the SemEval-2021 Task 5: Toxic Spans Detection . previous work focused on post level annotations, but identifying offensive span is useful in many ways. |
| Approach: | They propose a Python-based system to detect offensive spans in texts with pre-trained models and a user-friendly web-based interface. |
| Outcome: | The proposed system is based on a Python-based framework and a user-friendly web-based interface. |
Offensive Language Identification in Greek (2020.lrec-1)
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| Challenge: | a gap in the literature on offensive language has been addressed with studies on Spanish, Hindi, and German. |
| Approach: | They present a Greek annotated dataset for offensive language identification . it contains 4,779 tweets annotating offensive and not offensive posts from Twitter . they evaluate several computational models trained and tested on the dataset . |
| Outcome: | The proposed dataset contains 4,779 tweets annotated as offensive and not offensive . the authors show that the proposed dataset is similar to the OLID dataset for English . |
Target-Based Offensive Language Identification (2023.acl-short)
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Marcos Zampieri, Skye Morgan, Kai North, Tharindu Ranasinghe, Austin Simmmons, Paridhi Khandelwal, Sara Rosenthal, Preslav Nakov
| Challenge: | Popular social media annotation taxonomies focus on the post level and token-level annotations are not available. |
| Approach: | They propose a new dataset for Target-based Offensive language identification that uses post-level and token-level annotations to identify offensive language on Twitter. |
| Outcome: | The proposed taxonomy can be used to annotate offensive language on English Twitter posts. |
Multilingual Offensive Language Identification with Cross-lingual Embeddings (2020.emnlp-main)
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| Challenge: | Several studies investigating methods to detect offensive content in social media use English data. |
| Approach: | They apply cross-lingual contextual embeddings and transfer learning to make predictions in languages with less resources. |
| Outcome: | The proposed method compares favorably to the best systems submitted to recent shared tasks on Bengali, Hindi, and Spanish. |
Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive (2023.emnlp-main)
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Tharindu Weerasooriya, Sujan Dutta, Tharindu Ranasinghe, Marcos Zampieri, Christopher Homan, Ashiqur KhudaBukhsh
| Challenge: | a paper examines how machine and human moderators disagree on offensive speech . offensive speech detection is a key component of content moderation . |
| Approach: | They propose a large-scale noise audit and a vicarious offense dataset to investigate disagreement on social web political discourse. |
| Outcome: | The proposed dataset reveals that moderation outcomes vary wildly across different machine moderators. |
What do Large Language Models Need for Machine Translation Evaluation? (2024.emnlp-main)
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Shenbin Qian, Archchana Sindhujan, Minnie Kabra, Diptesh Kanojia, Constantin Orasan, Tharindu Ranasinghe, Fred Blain
| Challenge: | Existing research shows that large language models can perform better in machine translation tasks. |
| Approach: | They propose to use large language models for machine translation evaluations . authors explore what translation information is needed for LLMs to evaluate MT quality . |
| Outcome: | The proposed model performs comparable to fine-tuned multilingual pre-trained models. |
NSina: A News Corpus for Sinhala (2024.lrec-main)
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Hansi Hettiarachchi, Damith Premasiri, Lasitha Randunu Chandrakantha Uyangodage, Tharindu Ranasinghe
| Challenge: | introducing large language models (LLMs) has advanced natural language processing (NLP), but their effectiveness is largely dependent on pre-training resources. |
| Approach: | They propose a large news corpus for Sinhala with a set of NLP tasks for the language . NSina is the largest news corpuse for Sinha, available up to date . |
| Outcome: | The proposed model outperforms existing models in many benchmarks and outperformed previous models in high-resource languages. |
Teacher and Student Models of Offensive Language in Social Media (2023.findings-acl)
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| Challenge: | Existing approaches to identify offensive language online use large pre-trained transformer models. however, the inference time, disk, and memory requirements of these models are prohibitively large. |
| Approach: | They propose to transfer knowledge from large transformer models to much smaller neural models to make predictions at the token- and post-level. |
| Outcome: | The proposed model performs 100 times better than transformer models but with 100 times less parameters and much less memory usage. |
Sinhala Encoder-only Language Models and Evaluation (2025.acl-long)
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Tharindu Ranasinghe, Hansi Hettiarachchi, Nadeesha Chathurangi Naradde Vidana Pathirana, Damith Premasiri, Lasitha Uyangodage, Isuri Nanomi Arachchige, Alistair Plum, Paul Rayson, Ruslan Mitkov
| Challenge: | Recent advances in language models (LMs) have produced excellent results in many NLP tasks, but their effectiveness is highly dependent on available pre-training resources. |
| Approach: | They propose to collect the largest monolingual corpus for Sinhala and compile a benchmark and evaluate LMs on it. |
| Outcome: | The proposed language models outperform the popular multilingual LMs in downstream NLP tasks. |
Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language (2024.lrec-main)
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| Challenge: | Existing methods to extract relationships are limited to English and require annotating datasets in order to be expensive and time-consuming. |
| Approach: | They apply guided distant supervision to create a large biographical relationship extraction dataset for German using 80,000 instances for nine relationship types. |
| Outcome: | The proposed dataset is the largest biographical German relationship extraction dataset. |
MUSTS: MUltilingual Semantic Textual Similarity Benchmark (2025.acl-short)
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| Challenge: | Existing benchmarks for semantic textual similarity (STS) are limited to high-resource languages and do not include datasets annotated focusing on relatedness instead of similarity. |
| Approach: | They propose to evaluate multilingual semantic textual similarity benchmarks which span 13 languages and annotated datasets to evaluate and compare them. |
| Outcome: | The proposed method is the most comprehensive benchmark of multilingual STS methods. |
ltzGLUE: Luxembourgish General Language Understanding Evaluation (2026.findings-acl)
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Alistair Plum, Felicia Körner, Anne-Marie Lutgen, Laura Bernardy, Fred Philippy, Emilia Milano, Nils Rehlinger, Cedric Lothritz, Tharindu Ranasinghe, Barbara Plank, Christoph Purschke
| Challenge: | ltzGLUE is the first official NLU benchmark for Luxembourgish (LTZ) based on the popular GLUE benchmark for English. |
| Approach: | They propose a new natural language understanding (NLU) benchmark for Luxembourgish based on the popular GLUE benchmark for English. |
| Outcome: | The proposed model performs well across many languages and is based on the GLUE benchmark for English. |
An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers (2021.acl-short)
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| Challenge: | Existing word-level quality estimation models require labelled data for each language pair and expensive maintenance. |
| Approach: | They propose to use multilingual QE models to generalise across languages . they propose to train models on other language pairs to predict word-level quality . |
| Outcome: | The proposed models generalise well across languages, making them more useful in real-world scenarios. |
DORE: A Dataset for Portuguese Definition Generation (2024.lrec-main)
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| Challenge: | Definition modelling (DM) is the task of automatically generating a dictionary definition of a specific word. |
| Approach: | They propose to create a dataset for definition modelling for Portuguese with 100,000 definitions and evaluate several deep learning based DM models on the dataset. |
| Outcome: | The proposed dataset will facilitate research and study of Portuguese in wider contexts. |
TransQuest: Translation Quality Estimation with Cross-lingual Transformers (2020.coling-main)
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| Challenge: | Recent advances in the field of sentence-level quality estimation (QE) are based on neural-based architectures that require resourceintensive training. |
| Approach: | They propose a framework for sentence-level quality estimation based on cross-lingual transformers and use it to implement and evaluate two different neural architectures. |
| Outcome: | The proposed framework outperforms open-source QE frameworks when trained on WMT datasets and is very competitive in transfer learning settings. |
SmartMatch: Real-Time Semantic Retrieval for Translation Memory Systems (2026.eacl-demo)
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Ernesto L. Estevanell-Valladares, Salima Lamsiyah, Alicia Picazo-Izquierdo, Tharindu Ranasinghe, Ruslan Mitkov, Rafael Muñoz
| Challenge: | Translation Memory (TM) systems are core components of computer-aided translation tools . however, they fail to retrieve semantically relevant content when surface similarity is low. |
| Approach: | They propose an open-source demo and evaluation toolkit for TM retrieval that connects modern sentence encoders and strong lexical/fuzzy baselines with a vector database. |
| Outcome: | The proposed toolkit exposes the end-to-end retrieval pipeline through a web-based UI for qualitative inspection and preference logging. |
Quality Estimation-Assisted Automatic Post-Editing (2023.findings-emnlp)
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| Challenge: | Existing APE and QE combination strategies have not shown significant performance gains in the field of automatic post-editing (APE). |
| Approach: | They propose to train a model on APE and QE tasks to improve the APE performance by using a multi-task learning methodology that treats both tasks as a 'bargaining game' they also investigate various existing combination strategies and show that their approach achieves state-of-the-art performance for a ‘distant’ language pair, viz., English-Marathi. |
| Outcome: | The proposed model improves on two different language pairs, viz., English-Marathi and English-German. |
fBERT: A Neural Transformer for Identifying Offensive Content (2021.findings-emnlp)
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| Challenge: | Existing models such as BERT, XLNET, and XLM-R have outperformed other neural architectures and statistical learning methods in the identification of offensive language and hate speech. |
| Approach: | They present a BERT model retrained on SOLID, the largest English offensive language identification corpus available with over 1.4 million offensive instances. |
| Outcome: | The proposed model outperforms models trained on SOLID, the largest English offensive language identification corpus available with over 1.4 million offensive instances. |
A Multi-task Learning Framework for Quality Estimation (2023.findings-acl)
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Sourabh Deoghare, Paramveer Choudhary, Diptesh Kanojia, Tharindu Ranasinghe, Pushpak Bhattacharyya, Constantin Orăsan
| Challenge: | Conventional approaches to QE involve training separate models at different levels of granularity viz., word-level, sentence-level and document-level . |
| Approach: | They propose to train a single model for sentence-level and word-level QE tasks in a multi-task learning framework and compare them to baseline models. |
| Outcome: | The proposed model improves on the single-pair, multi-patch, and zero-shot settings. |
A Survey on Multilingual Mental Disorders Detection from Social Media Data (2026.eacl-long)
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| Challenge: | Existing studies on mental disorders focus on English data, overlooking critical signals that may be present in non-English texts. |
| Approach: | They present a list of 108 social media datasets that can be used to train NLP models for mental health screening in 25 languages. |
| Outcome: | The proposed datasets cover 25 languages and can be used to train models for mental health screening. |
ALEXSIS-PT: A New Resource for Portuguese Lexical Simplification (2022.coling-1)
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| Challenge: | Lexical simplification (LS) is the task of replacing complex words with simpler alternatives to make texts more accessible to various target populations. |
| Approach: | They propose to use a Brazilian Portuguese multi-candidate dataset to test LS systems. |
| Outcome: | The proposed model outperforms existing models on Brazilian Portuguese and Brazilian newspaper articles. |
Rater Cohesion and Quality from a Vicarious Perspective (2024.findings-emnlp)
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Deepak Pandita, Tharindu Cyril Weerasooriya, Sujan Dutta, Sarah Luger, Tharindu Ranasinghe, Ashiqur KhudaBukhsh, Marcos Zampieri, Christopher Homan
| Challenge: | Recent work in reinforcement learning with human feedback (RLHF) highlights the gains in model performance from aligning them to human values. |
| Approach: | They propose to use vicarious annotation to break down disagreement by asking raters how they think others would annotate the data. |
| Outcome: | The proposed method breaks down disagreements by asking raters how they think others would annotate the data. |
MentalHelp: A Multi-Task Dataset for Mental Health in Social Media (2024.lrec-main)
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Nishat Raihan, Sadiya Sayara Chowdhury Puspo, Shafkat Farabi, Ana-Maria Bucur, Tharindu Ranasinghe, Marcos Zampieri
| Challenge: | Annotating social media data for mental health disorders is expensive and time-consuming, limiting their size and scope. |
| Approach: | They present a large-scale semi-supervised mental disorder detection dataset containing 14 million instances from Reddit and an ensemble of three separate models. |
| Outcome: | The proposed dataset contains 14 million instances of mental disorders . it was collected from reddit and labeled in a semi-supervised way . |