Papers by Marcos Zampieri
Countering Hateful and Offensive Speech Online - Open Challenges (2024.emnlp-tutorials)
Copied to clipboard
Leon Derczynski, Marco Guerini, Debora Nozza, Flor Miriam Plaza-del-Arco, Jeffrey Sorensen, Marcos Zampieri
| Challenge: | a comprehensive understanding of the field is needed to maintain respectful and inclusive online environments. |
| Approach: | This tutorial aims to provide attendees with a comprehensive understanding of the field by delving into essential dimensions such as multilingualism, counter-narrative generation, a hands-on session with one of the most popular APIs for detecting hate speech, fairness, and ethics in AI, and the use of recent advanced approaches. |
| Outcome: | This tutorial aims to provide attendees with a comprehensive understanding of the field by delving into essential dimensions such as multilingualism, counter-narrative generation, a hands-on session with one of the most popular APIs for detecting hate speech, fairness, and ethics in AI, and the use of recent advanced approaches. |
MUDES: Multilingual Detection of Offensive Spans (2021.naacl-demos)
Copied to clipboard
| 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)
Copied to clipboard
| 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 . |
Predicting the Type and Target of Offensive Posts in Social Media (N19-1)
Copied to clipboard
| Challenge: | Prior work focused on detecting specific types of offensive content, such as hate speech, cyberbullying, or cyber-aggression. |
| Approach: | They propose to use a dataset to identify offensive content in social media . they compare the performance of different machine learning models to OLID . |
| Outcome: | The proposed dataset contains tweets annotated for offensive content using a fine-grained three-layer annotation scheme. |
Target-Based Offensive Language Identification (2023.acl-short)
Copied to clipboard
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)
Copied to clipboard
| 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. |
CodeGuard: Improving LLM Guardrails in CS Education (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are increasingly embedded in Computer Science classrooms to automate code generation, feedback, and assessment. |
| Approach: | They propose a guardrail framework for educational AI systems that can handle unsafe and irrelevant prompts. |
| Outcome: | The proposed framework reduces potentially harmful or policy-violating code completions by 30-65% without degrading performance on legitimate educational tasks. |
Native Language Identification in Texts: A Survey (2024.naacl-long)
Copied to clipboard
| Challenge: | Native language identification is the task of automatically identifying an author’s native language (L1) based on their second language production. |
| Approach: | They present a survey of native language identification applied to texts . authors describe several text representations and computational techniques used in the task . |
| Outcome: | The proposed task has been widely studied for both text and speech, particularly for L2 English due to the availability of suitable corpora. |
Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive (2023.emnlp-main)
Copied to clipboard
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. |
TigerLLM - A Family of Bangla Large Language Models (2025.acl-short)
Copied to clipboard
| Challenge: | linguistic disparity is particularly evident for Bangla, the 5th most spoken language . open-source Bangla LLMs have limited reproducibility and performance gaps . |
| Approach: | They propose a family of Bangla LLMs that outperform open-source alternatives and benchmarks and establish a new benchmark for future Bangla language modeling. |
| Outcome: | The proposed models outperform existing models and outperformed proprietary models across six benchmarks. |
Transfer Learning Methods for Domain Adaptation in Technical Logbook Datasets (2022.lrec-1)
Copied to clipboard
| Challenge: | Technical logbook data typically has both a domain, the field it comes from, and an application, what it is used for. |
| Approach: | They propose to use domain-specific technical language to identify technical logbook entries by using transfer learning to learn from different domains and from different datasets. |
| Outcome: | The proposed approach improves performance in all cases but one of the three domains studied. |
Tracing L1 Interference in English Learner Writing: A Longitudinal Corpus with Error Annotations (2025.emnlp-main)
Copied to clipboard
Poorvi Acharya, J. Elizabeth Liebl, Dhiman Goswami, Kai North, Marcos Zampieri, Antonios Anastasopoulos
| Challenge: | high-quality learner corpora are rarely available for studies of second language acquisition and language transfer. |
| Approach: | They propose to curate a corpus of adult learners with longitudinal data that includes 15 different L1s. |
| Outcome: | The proposed corpus contains 687 texts written by adult learners in the USA . authors show that the corpus can be used to explore language learning trajectories over time. |
Language Variety Identification with True Labels (2024.lrec-main)
Copied to clipboard
Marcos Zampieri, Kai North, Tommi Jauhiainen, Mariano Felice, Neha Kumari, Nishant Nair, Yash Mahesh Bangera
| Challenge: | Language identification datasets are compiled with the assumption that the gold label of each instance is determined by where texts are retrieved from. |
| Approach: | They present a human-annotated multilingual dataset for language variety identification . they use a model to train multiple models to discriminate between different languages . |
| Outcome: | The proposed dataset provides a reliable benchmark toward robust and fairer language variety identification systems. |
SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification (2021.findings-acl)
Copied to clipboard
| Challenge: | toxicity, hate speech, cyberbullying, and cyber-aggression are common themes in social media . authors present a dataset that is limited in size and biased towards offensive language . |
| Approach: | They present an expanded dataset that uses a taxonomy for offensive language identification . they show that using SOLID and OLID yields sizable performance gains . |
| Outcome: | The proposed dataset shows that it performs better than the OLID dataset for two different models. |
Subasa - Adapting Language Models for Low-resourced Offensive Language Detection in Sinhala (2025.naacl-srw)
Copied to clipboard
Shanilka Haturusinghe, Tharindu Cyril Weerasooriya, Christopher M Homan, Marcos Zampieri, Sidath Ravindra Liyanage
| Challenge: | A major challenge in the field of NLP are the disparities between high- and low-resource languages. |
| Approach: | They propose fine-tuning strategies that have not been previously explored for Sinhala in the downstream task of offensive language detection. |
| Outcome: | The proposed models outperform baseline models on the Sinhala offensive language detection task. |
Teacher and Student Models of Offensive Language in Social Media (2023.findings-acl)
Copied to clipboard
| 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. |
MojoBench: Language Modeling and Benchmarks for Mojo (2025.findings-naacl)
Copied to clipboard
| Challenge: | Mojo is a programming language that has been praised for its speed and performance over Python. |
| Approach: | They propose a framework for Mojo code generation that evaluates code Large Language Models (LLMs) they propose 'mojo-Coder' which is the first LLM pretrained and fine-tuned for MoJO code generation . |
| Outcome: | MojoBench is the first framework for mojo code generation . it achieves a 30-35% performance improvement over leading models like GPT-4o and Claude-3.5-Sonnet . |
Handling Extreme Class Imbalance in Technical Logbook Datasets (2021.acl-long)
Copied to clipboard
| Challenge: | Technical logbooks are a challenging and under-explored text type in automated event identification. |
| Approach: | They propose a feedback strategy that resamples the training data based on its error in the prediction process. |
| Outcome: | The proposed approach provides the best results for four different neural network models trained across a suite of technical logbook datasets from distinct technical domains. |
NLP Tools for Predictive Maintenance Records in MaintNet (2020.aacl-demo)
Copied to clipboard
| Challenge: | Maintenance logbooks often contain free text fields with domain specific terms, abbreviations, and non-standard spelling . most standard NLP pipelines for pre-processing and annotation are trained on standard contemporary corpora. |
| Approach: | They propose to create an open-source library and data repository for predictive maintenance language datasets and to evaluate the tools available at MaintNet. |
| Outcome: | The proposed tools improve the performance of existing pipelines and improve the quality of the existing ones. |
MaintNet: A Collaborative Open-Source Library for Predictive Maintenance Language Resources (2020.coling-demos)
Copied to clipboard
| Challenge: | Maintenance record logbooks are an emerging text type in NLP. maintenance record logbook data is often written in non-standard language with many domain specific technical terms, abbreviations, and non-standardized spelling and grammar. |
| Approach: | They propose to create a collaborative open-source library of technical and domain-specific language resources for maintenance record logbooks. |
| Outcome: | The proposed library provides tools to aid in their (pre-)processing and clustering. |
Claim Verification in the Age of Large Language Models: A Survey (2026.acl-srw)
Copied to clipboard
| Challenge: | Recent election cycles have seen a large number of false information spread across social media and news platforms. |
| Approach: | They propose a framework for automated claim verification using Large Language Models and Retrieval Augmented Generation. |
| Outcome: | The proposed frameworks are based on large-scale models and new methods such as Retrieval Augmented Generation (RAG). |
Bayelemabaga: Creating Resources for Bambara NLP (2025.naacl-long)
Copied to clipboard
| Challenge: | a lack of well-structured multilingual datasets remains a challenge for machine translation in under-resource languages. |
| Approach: | They propose to create a multilingual dataset for machine translation in the Bambara language, the vehicular language of Mali. |
| Outcome: | The proposed dataset is the most extensive curated multilingual dataset for machine translation in the Bambara language, the vehicular language of Mali. |
LIdioms: A Multilingual Linked Idioms Data Set (L18-1)
Copied to clipboard
| Challenge: | Recent studies have focused on linguistic data sets that are bilingual on the Linguistic Linked Open Data (LLOD) 1 . |
| Approach: | They describe a multilingual RDF representation of idioms currently containing five languages . they use a model to structure the data and a method to link the data to well-known multilingual data sets such as BabelNet. |
| Outcome: | The proposed model complies with best practices according to Linguistic Linked Open Data Community. |
A Computational Exploration of Pejorative Language in Social Media (2021.findings-emnlp)
Copied to clipboard
| Challenge: | In this paper, we examine the problem of pejorative language, an under-explored topic in computational linguistics. |
| Approach: | They propose to automatically disambiguate pejorative usage in social media . they leverage online dictionaries to build a multilingual lexicon of pejorativ terms . |
| Outcome: | The proposed model can automatically disambiguate pejorative usage in social media posts . the proposed model is based on dictionaries and tweets . |
fBERT: A Neural Transformer for Identifying Offensive Content (2021.findings-emnlp)
Copied to clipboard
| 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. |
RDF2PT: Generating Brazilian Portuguese Texts from RDF Data (L18-1)
Copied to clipboard
Diego Moussallem, Thiago Ferreira, Marcos Zampieri, Maria Claudia Cavalcanti, Geraldo Xexéo, Mariana Neves, Axel-Cyrille Ngonga Ngomo
| Challenge: | Existing approaches to generate natural language from RDF data have been proposed to generate texts in Brazilian Portuguese. |
| Approach: | They propose a rule-based approach to verbalize RDF data to Brazilian Portuguese language. |
| Outcome: | The proposed approach generates text similar to that generated by humans and can hence be easily understood. |
An Exploratory Analysis of the Relation between Offensive Language and Mental Health (2021.findings-acl)
Copied to clipboard
| Challenge: | Using computational models, the use of offensive language is pervasive in social media . a popular line of research is the study of machine learning classifiers to identify offensive content online . |
| Approach: | They analyze social media posts written by individuals with depression and those without . they train computational models to compare use of offensive language with depression detection . |
| Outcome: | The proposed models show that offensive language is more frequently used in the samples written by individuals with depression and those showing signs of depression. |
mHumanEval - A Multilingual Benchmark to Evaluate Large Language Models for Code Generation (2025.naacl-long)
Copied to clipboard
| Challenge: | Current evaluations focus on English-to-Python conversion tasks with limited test cases . code generation from low-resource language prompts remains largely unexplored . |
| Approach: | They propose a benchmark that supports prompts in over 200 natural languages . they provide expert human translations for 15 diverse natural languages (NLs) |
| Outcome: | The HumanEval Benchmark is the most widely used code generation benchmark . it provides expert human translations for 15 diverse natural languages . |
A Survey on Multilingual Mental Disorders Detection from Social Media Data (2026.eacl-long)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
Multilingual Native Language Identification with Large Language Models (2025.naacl-srw)
Copied to clipboard
| Challenge: | Native Language Identification (NLI) is the task of automatically identifying the native language (L1) of individuals based on their second language production. |
| Approach: | They evaluated the performance of several LLMs on non-English NLI corpora compared to traditional statistical machine learning models and language-specific BERT-based models. |
| Outcome: | The proposed models outperform statistical models and language-specific BERT-based models on English, Italian, Norwegian, and Portuguese. |
Rater Cohesion and Quality from a Vicarious Perspective (2024.findings-emnlp)
Copied to clipboard
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)
Copied to clipboard
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 . |