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arXiv · 2407.05449

SmurfCat at PAN 2024 TextDetox: Alignment of Multilingual Transformers for Text Detoxification

Elisei Rykov·Konstantin Zaytsev·Ivan Anisimov·Alexandr Voronin
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Abstract

This paper presents a solution for the Multilingual Text Detoxification task in the PAN-2024 competition of the SmurfCat team. Using data augmentation through machine translation and a special filtering procedure, we collected an additional multilingual parallel dataset for text detoxification. Using the obtained data, we fine-tuned several multilingual sequence-to-sequence models, such as mT0 and Aya, on a text detoxification task. We applied the ORPO alignment technique to the final model. Our final model has only 3.7 billion parameters and achieves state-of-the-art results for the Ukrainian language and near state-of-the-art results for other languages. In the competition, our team achieved first place in the automated evaluation with a score of 0.52 and second place in the final human evaluation with a score of 0.74.

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Elisei Rykov, Konstantin Zaytsev, Ivan Anisimov, Alexandr Voronin. 2024-07-07. SmurfCat at PAN 2024 TextDetox: Alignment of Multilingual Transformers for Text Detoxification. https://arxiv.org/abs/2407.05449

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