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
@inproceedings{wnut-ufal,
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
.
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