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Fine-tuned ByT5-small for MultiLexNorm (Croatian version)

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This is the official release of the fine-tuned models for the winning entry to the W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm) shared task, which evaluates lexical-normalization systems on 12 social media datasets in 11 languages.

Our system is based on ByT5, which we first pre-train on synthetic data and then fine-tune on authentic normalization data. It achieves the best performance by a wide margin in intrinsic evaluation, and also the best performance in extrinsic evaluation through dependency parsing. In addition to these fine-tuned models, we also release the source files on GitHub and an interactive demo on Google Colab.

How to use

The model was not fine-tuned in a standard sentence-to-sentence setting – instead, it was tailored to the token-to-token definition of MultiLexNorm data. Please refer to the interactive demo on Colab notebook to learn how to use these models.

How to cite

  title= "{ÚFAL} at {MultiLexNorm} 2021: Improving Multilingual Lexical Normalization by Fine-tuning {ByT5}",
  author = "Samuel, David and Straka, Milan",
  booktitle = "Proceedings of the 7th Workshop on Noisy User-generated Text (W-NUT 2021)",
  year = "2021",
  publisher = "Association for Computational Linguistics",
  address = "Punta Cana, Dominican Republic"

ByT5 - Small

ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.

ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.

ByT5 works especially well on noisy text data,e.g., google/byt5-small significantly outperforms mt5-small on TweetQA.

Paper: ByT5: Towards a token-free future with pre-trained byte-to-byte models

Authors: Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel

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Datasets used to train ufal/byt5-small-multilexnorm2021-hr