Search Results for author:
Found 17 papers, 10 papers with code
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.
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.
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.
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.
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.
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.
