3 citations · 9 across the 13 of their papers we have counts for
14 papers
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…
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…
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…
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…
Explaining Bayesian Networks in Natural Language using Factor Arguments. Evaluation in the medical domain
Jaime Sevilla, Nikolay Babakov, Ehud Reiter +1
In this paper, we propose a model for building natural language explanations for Bayesian Network Reasoning in terms of factor arguments, which are argumentation graphs of flowing…
Scalability of Bayesian Network Structure Elicitation with Large Language Models: a Novel Methodology and Comparative Analysis
Nikolay Babakov, Ehud Reiter, Alberto Bugarin
In this work, we propose a novel method for Bayesian Networks (BNs) structure elicitation that is based on the initialization of several LLMs with different experiences, independen…
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…
Don't lose the message while paraphrasing: A study on content preserving style transfer
Nikolay Babakov, David Dale, Ilya Gusev +2
Text style transfer techniques are gaining popularity in natural language processing allowing paraphrasing text in the required form: from toxic to neural, from formal to informal,…
Error syntax aware augmentation of feedback comment generation dataset
Nikolay Babakov, Maria Lysyuk, Alexander Shvets +2
This paper presents a solution to the GenChal 2022 shared task dedicated to feedback comment generation for writing learning. In terms of this task given a text with an error and a…
Studying the role of named entities for content preservation in text style transfer
Nikolay Babakov, David Dale, Varvara Logacheva +2
Text style transfer techniques are gaining popularity in Natural Language Processing, finding various applications such as text detoxification, sentiment, or formality transfer. Ho…
Beyond Plain Toxic: Detection of Inappropriate Statements on Flammable Topics for the Russian Language
Nikolay Babakov, Varvara Logacheva, Alexander Panchenko
Toxicity on the Internet, such as hate speech, offenses towards particular users or groups of people, or the use of obscene words, is an acknowledged problem. However, there also e…
Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company's Reputation
Nikolay Babakov, Varvara Logacheva, Olga Kozlova +2
Not all topics are equally "flammable" in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or…