Search Results for author: Daryna Dementieva

Found 17 papers, 10 papers with code

Exploring Cross-lingual Text Detoxification with Large Multilingual Language Models.

1 code implementation ACL 2022 Daniil Moskovskiy, Daryna Dementieva, Alexander Panchenko

This work investigates multilingual and cross-lingual detoxification and the behavior of large multilingual models in this setting.

Style Transfer

ParaDetox: Detoxification with Parallel Data

1 code implementation ACL 2022 Varvara Logacheva, Daryna Dementieva, Sergey Ustyantsev, Daniil Moskovskiy, David Dale, Irina Krotova, Nikita Semenov, Alexander Panchenko

To the best of our knowledge, these are the first parallel datasets for this task. We describe our pipeline in detail to make it fast to set up for a new language or domain, thus contributing to faster and easier development of new parallel resources. We train several detoxification models on the collected data and compare them with several baselines and state-of-the-art unsupervised approaches.

Sentence

MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages

no code implementations2 Apr 2024 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.

Style Transfer

Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge Transfer Approaches

no code implementations2 Apr 2024 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.

Natural Language Inference text-classification +2

Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models

1 code implementation15 May 2023 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".

AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning

no code implementations15 May 2023 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.

Data Augmentation Ensemble Learning

IFAN: An Explainability-Focused Interaction Framework for Humans and NLP Models

no code implementations6 Mar 2023 Edoardo Mosca, Daryna Dementieva, Tohid Ebrahim Ajdari, Maximilian Kummeth, Kirill Gringauz, Yutong Zhou, Georg Groh

Interpretability and human oversight are fundamental pillars of deploying complex NLP models into real-world applications.

Multiverse: Multilingual Evidence for Fake News Detection

1 code implementation25 Nov 2022 Daryna Dementieva, Mikhail Kuimov, Alexander Panchenko

In this work, we propose Multiverse -- a new feature based on multilingual evidence that can be used for fake news detection and improve existing approaches.

Fake News Detection News Classification

Exploring Cross-lingual Textual Style Transfer with Large Multilingual Language Models

1 code implementation5 Jun 2022 Daniil Moskovskiy, Daryna Dementieva, Alexander Panchenko

However, models are not able to perform cross-lingual detoxification and direct fine-tuning on exact language is inevitable.

Style Transfer

Cross-lingual Evidence Improves Monolingual Fake News Detection

1 code implementation ACL 2021 Daryna Dementieva, Alexander Panchenko

Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases.

Fake News Detection News Classification

SkoltechNLP at SemEval-2020 Task 11: Exploring Unsupervised Text Augmentation for Propaganda Detection

no code implementations SEMEVAL 2020 Daryna Dementieva, Igor Markov, Alexander Panchenko

This paper presents a solution for the Span Identification (SI) task in the {``}Detection of Propaganda Techniques in News Articles{''} competition at SemEval-2020.

Propaganda detection Text Augmentation

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