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We present the winning entry to the Multilingual Lexical Normalization(MultiLexNorm) shared task at W-NUT 2021 (van der Goot et al., 2021a), whichevaluates lexical-normalization systems on 12 social media datasets in 11languages. We base our solution on a pre-trained byte-level language model,ByT5 (Xue et al., 2021a), which we further pre-train on synthetic data and thenfine-tune on authentic normalization data. Our system achieves the bestperformance by a wide margin in intrinsic evaluation, and also the bestperformance in extrinsic evaluation through dependency parsing. The source codeis released at https://github.com/ufal/multilexnorm2021 and the fine-tunedmodels at https://huggingface.co/ufal.

\'UFAL at MultiLexNorm 2021: Improving Multilingual Lexical Normalization by Fine-tuning ByT5