We present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this pipeline can be used to distill a large existing corpus of paraphrases to get toxic-neutral sentence pairs. We release two parallel corpora which can be used for the training of detoxification models. 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. We conduct both automatic and manual evaluations. All models trained on parallel data outperform the state-of-the-art unsupervised models by a large margin. This suggests that our novel datasets can boost the performance of detoxification systems.

Detoxification is a task of generating text in polite style while preserving meaning and fluency of the original toxic text. Existing detoxification methods are monolingual i.e. designed to work in one exact language. This work investigates multilingual and cross-lingual detoxification and the behavior of large multilingual models in this setting. Unlike previous works we aim to make large language models able to perform detoxification without direct fine-tuning in a given language. Experiments show that multilingual models are capable of performing multilingual style transfer. However, tested state-of-the-art models are not able to perform cross-lingual detoxification and direct fine-tuning on exact language is currently inevitable and motivating the need of further research in this direction.

This paper describes our contribution to SemEval 2022 Task 8: Multilingual News Article Similarity. The aim was to test completely different approaches and distinguish the best performing. That is why we’ve considered systems based on Transformer-based encoders, NER-based, and NLI-based methods (and their combination with SVO dependency triplets representation). The results prove that Transformer models produce the best scores. However, there is space for research and approaches that give not yet comparable but more interpretable results.

It is often difficult to reliably evaluate models which generate text. Among them, text style transfer is a particularly difficult to evaluate, because its success depends on a number of parameters.We conduct an evaluation of a large number of models on a detoxification task. We explore the relations between the manual and automatic metrics and find that there is only weak correlation between them, which is dependent on the type of model which generated text. Automatic metrics tend to be less reliable for better-performing models. However, our findings suggest that, ChrF and BertScore metrics can be used as a proxy for human evaluation of text detoxification to some extent.

We present two novel unsupervised methods for eliminating toxicity in text. Our first method combines two recent ideas: (1) guidance of the generation process with small style-conditional language models and (2) use of paraphrasing models to perform style transfer. We use a well-performing paraphraser guided by style-trained language models to keep the text content and remove toxicity. Our second method uses BERT to replace toxic words with their non-offensive synonyms. We make the method more flexible by enabling BERT to replace mask tokens with a variable number of words. Finally, we present the first large-scale comparative study of style transfer models on the task of toxicity removal. We compare our models with a number of methods for style transfer. The models are evaluated in a reference-free way using a combination of unsupervised style transfer metrics. Both methods we suggest yield new SOTA results.

Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases. Therefore, it is becoming essential to develop fake news detection technologies. While substantial work has been done in this direction, one of the limitations of the current approaches is that these models are focused only on one language and do not use multilingual information. In this work, we propose a new technique based on cross-lingual evidence (CE) that can be used for fake news detection and improve existing approaches. The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed, firstly, by manual experiment based on a set of known true and fake news. Besides, we compared our fake news classification system based on the proposed feature with several strong baselines on two multi-domain datasets of general-topic news and one newly fake COVID-19 news dataset showing that combining cross-lingual evidence with strong baselines such as RoBERTa yields significant improvements in fake news 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. The goal of the SI task is to identify specific fragments of each article which contain the use of at least one propaganda technique. This is a binary sequence tagging task. We tested several approaches finally selecting a fine-tuned BERT model as our baseline model. Our main contribution is an investigation of several unsupervised data augmentation techniques based on distributional semantics expanding the original small training dataset as applied to this BERT-based sequence tagger. We explore various expansion strategies and show that they can substantially shift the balance between precision and recall, while maintaining comparable levels of the F1 score.