1 citations · 2 across the 26 of their papers we have counts for
30 papers
Last Translation Benchmark
Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263
For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…
From Specialization to Generalization: Instruction-tuned LLMs for Robust Harmful Content Mitigation
Lukas Edman, Daryna Dementieva, Alexander Fraser
Large language models (LLMs) demonstrate impressive performance across a wide range of general NLP tasks; however, their effectiveness in sensitive domains, such as hate speech det…
PluraMath: Extending Mathematical Reasoning Evaluation Beyond High-Resource Languages
Daryna Dementieva, Nikolay Babakov, Kathy Hämmerl +14
Mathematical reasoning has become a central task for evaluating and tuning reasoning Large Language Models (LLMs), yet existing benchmarks remain heavily biased toward high-resourc…
Explainable Semantic Textual Similarity via Dissimilar Span Detection
Diego Miguel Lozano, Daryna Dementieva, Alexander Fraser
Semantic Textual Similarity (STS) is a crucial component of many Natural Language Processing (NLP) applications. However, existing approaches typically reduce semantic nuances to a…
Self-Explaining Hate Speech Detection with Moral Rationales
Francielle Vargas, Jackson Trager, Diego Alves +6
Existing hate speech detection models are often opaque and rely on surface-level lexical cues, which makes them vulnerable to spurious correlations and limits robustness, interpret…
Low-Resource, High-Impact: Building Corpora for Inclusive Language Technologies
Ekaterina Artemova, Laurie Burchell, Daryna Dementieva +3
This tutorial (https://tum-nlp.github.io/low-resource-tutorial) is designed for NLP practitioners, researchers, and developers working with multilingual and low-resource languages…
CrossNews-UA: A Cross-lingual News Semantic Similarity Benchmark for Ukrainian, Polish, Russian, and English
Daryna Dementieva, Evgeniya Sukhodolskaya, Alexander Fraser
In the era of social networks and rapid misinformation spread, news analysis remains a critical task. Detecting fake news across multiple languages, particularly beyond English, po…
HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation
Naquee Rizwan, Seid Muhie Yimam, Daryna Dementieva +11
Despite regulations imposed by nations and social media platforms, e.g. (Government of India, 2021; European Parliament and Council of the European Union, 2022), inter alia, hatefu…
Evaluating Text Style Transfer: A Nine-Language Benchmark for Text Detoxification
Vitaly Protasov, Nikolay Babakov, Daryna Dementieva +1
Despite notable advances in large language models (LLMs), reliable evaluation of text generation tasks such as text style transfer (TST) remains an open challenge. Existing researc…
Temperature Matters: Enhancing Watermark Robustness Against Paraphrasing Attacks
Badr Youbi Idrissi, Monica Millunzi, Amelia Sorrenti +2
In the present-day scenario, Large Language Models (LLMs) are establishing their presence as powerful instruments permeating various sectors of society. While their utility offers…
Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data
Faeze Ghorbanpour, Daryna Dementieva, Alexander Fraser
Considering the importance of detecting hateful language, labeled hate speech data is expensive and time-consuming to collect, particularly for low-resource languages. Prior work h…
Can Prompting LLMs Unlock Hate Speech Detection across Languages? A Zero-shot and Few-shot Study
Faeze Ghorbanpour, Daryna Dementieva, Alexander Fraser
Despite growing interest in automated hate speech detection, most existing approaches overlook the linguistic diversity of online content. Multilingual instruction-tuned large lang…
EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian
Daryna Dementieva, Nikolay Babakov, Alexander Fraser
While Ukrainian NLP has seen progress in many texts processing tasks, emotion classification remains an underexplored area with no publicly available benchmark to date. In this wor…
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities, and Responsible Deployment
Antonia Karamolegkou, Angana Borah, Eunjung Cho +30
Natural language processing (NLP) now shapes many aspects of our world, yet its potential for positive social impact is underexplored. This paper surveys work in ``NLP for Social G…
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages
Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin +45
People worldwide use language in subtle and complex ways to express emotions. Although emotion recognition--an umbrella term for several NLP tasks--impacts various applications wit…
Multilingual and Explainable Text Detoxification with Parallel Corpora
Daryna Dementieva, Nikolay Babakov, Amit Ronen +11
Even with various regulations in place across countries and social media platforms (Government of India, 2021; European Parliament and Council of the European Union, 2022, digital…
Crafting Tomorrow's Headlines: Neural News Generation and Detection in English, Turkish, Hungarian, and Persian
Cem Üyük, Danica Rovó, Shaghayegh Kolli +3
In the era dominated by information overload and its facilitation with Large Language Models (LLMs), the prevalence of misinformation poses a significant threat to public discourse…
Demarked: A Strategy for Enhanced Abusive Speech Moderation through Counterspeech, Detoxification, and Message Management
Seid Muhie Yimam, Daryna Dementieva, Tim Fischer +8
Despite regulations imposed by nations and social media platforms, such as recent EU regulations targeting digital violence, abusive content persists as a significant challenge. Ex…
Toxicity Classification in Ukrainian
Daryna Dementieva, Valeriia Khylenko, Nikolay Babakov +1
The task of toxicity detection is still a relevant task, especially in the context of safe and fair LMs development. Nevertheless, labeled binary toxicity classification corpora ar…
MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages
Daryna Dementieva, Nikolay Babakov, Alexander Panchenko
Text detoxification is a textual style transfer (TST) task where a text is paraphrased from a toxic surface form, e.g. featuring rude words, to the neutral register. Recently, text…
Cross-lingual Text Classification Transfer: The Case of Ukrainian
Daryna Dementieva, Valeriia Khylenko, Georg Groh
Despite the extensive amount of labeled datasets in the NLP text classification field, the persistent imbalance in data availability across various languages remains evident. To su…
Exploring Methods for Cross-lingual Text Style Transfer: The Case of Text Detoxification
Daryna Dementieva, Daniil Moskovskiy, David Dale +1
Text detoxification is the task of transferring the style of text from toxic to neutral. While here are approaches yielding promising results in monolingual setup, e.g., (Dale et a…
AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning
Adam Rydelek, Daryna Dementieva, Georg Groh
The Explainable Detection of Online Sexism task presents the problem of explainable sexism detection through fine-grained categorisation of sexist cases with three subtasks. Our te…
Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models
Daniel Schroter, Daryna Dementieva, Georg Groh
This paper presents the best-performing approach alias "Adam Smith" for the SemEval-2023 Task 4: "Identification of Human Values behind Arguments". The goal of the task was to crea…