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Found 26 papers, 12 papers with code
ParaDetox: Detoxification with Parallel Data
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.
Exploring Cross-lingual Text Detoxification with Large Multilingual Language Models.
This work investigates multilingual and cross-lingual detoxification and the behavior of large multilingual models in this setting.
A Study on Manual and Automatic Evaluation for Text Style Transfer: The Case of Detoxification
It is often difficult to reliably evaluate models which generate text.
SkoltechNLP at SemEval-2022 Task 8: Multilingual News Article Similarity via Exploration of News Texts to Vector Representations
This paper describes our contribution to SemEval 2022 Task 8: Multilingual News Article Similarity.
EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian
While Ukrainian NLP has seen progress in many texts processing tasks, emotion classification remains an underexplored area with no publicly available benchmark to date.
NLP for Social Good: A Survey of Challenges, Opportunities, and Responsible Deployment
Recent advancements in large language models (LLMs) have unlocked unprecedented possibilities across a range of applications.
Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data
Notably, our approach is highly data-efficient, retrieving as small as 200 instances in some cases while maintaining superior performance.
Can Prompting LLMs Unlock Hate Speech Detection across Languages? A Zero-shot and Few-shot Study
Despite growing interest in automated hate speech detection, most existing approaches overlook the linguistic diversity of online content.
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages
In this paper, we present BRIGHTER-- a collection of multilabeled emotion-annotated datasets in 28 different languages.
Multilingual and Explainable Text Detoxification with Parallel Corpora
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 abusive speech remains a significant issue.
Crafting Tomorrow's Headlines: Neural News Generation and Detection in English, Turkish, Hungarian, and Persian
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 and societal well-being.
Demarked: A Strategy for Enhanced Abusive Speech Moderation through Counterspeech, Detoxification, and Message Management
Despite regulations imposed by nations and social media platforms, such as recent EU regulations targeting digital violence, abusive content persists as a significant challenge.
Toxicity Classification in Ukrainian
The task of toxicity detection is still a relevant task, especially in the context of safe and fair LMs development.
MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages
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.
Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge Transfer Approaches
Despite the extensive amount of labeled datasets in the NLP text classification field, the persistent imbalance in data availability across various languages remains evident.
Exploring Methods for Cross-lingual Text Style Transfer: The Case of Text Detoxification
Text detoxification is the task of transferring the style of text from toxic to neutral.
AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning
The Explainable Detection of Online Sexism task presents the problem of explainable sexism detection through fine-grained categorisation of sexist cases with three subtasks.
Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models
This paper presents the best-performing approach alias "Adam Smith" for the SemEval-2023 Task 4: "Identification of Human Values behind Arguments".
IFAN: An Explainability-Focused Interaction Framework for Humans and NLP Models
Interpretability and human oversight are fundamental pillars of deploying complex NLP models into real-world applications.
Multiverse: Multilingual Evidence for Fake News Detection
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.
Exploring Cross-lingual Textual Style Transfer with Large Multilingual Language Models
However, models are not able to perform cross-lingual detoxification and direct fine-tuning on exact language is inevitable.
Detecting Text Formality: A Study of Text Classification Approaches
Formality is one of the important characteristics of text documents.
Text Detoxification using Large Pre-trained Neural Models
We compare our models with a number of methods for style transfer.
Cross-lingual Evidence Improves Monolingual Fake News Detection
Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases.
Methods for Detoxification of Texts for the Russian Language
We introduce the first study of automatic detoxification of Russian texts to combat offensive language.
SkoltechNLP at SemEval-2020 Task 11: Exploring Unsupervised Text Augmentation for Propaganda Detection
This paper presents a solution for the Span Identification (SI) task in the {``}Detection of Propaganda Techniques in News Articles{''} competition at SemEval-2020.
