@inproceedings{scherrer-ljubesic-2021-sesame,
title = "Sesame Street to Mount Sinai: {BERT}-constrained character-level {M}oses models for multilingual lexical normalization",
author = "Scherrer, Yves and
Ljube{\v{s}}i{\'c}, Nikola",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wnut-1.52",
doi = "10.18653/v1/2021.wnut-1.52",
pages = "465--472",
abstract = "This paper describes the HEL-LJU submissions to the MultiLexNorm shared task on multilingual lexical normalization. Our system is based on a BERT token classification preprocessing step, where for each token the type of the necessary transformation is predicted (none, uppercase, lowercase, capitalize, modify), and a character-level SMT step where the text is translated from original to normalized given the BERT-predicted transformation constraints. For some languages, depending on the results on development data, the training data was extended by back-translating OpenSubtitles data. In the final ordering of the ten participating teams, the HEL-LJU team has taken the second place, scoring better than the previous state-of-the-art.",
}
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%0 Conference Proceedings
%T Sesame Street to Mount Sinai: BERT-constrained character-level Moses models for multilingual lexical normalization
%A Scherrer, Yves
%A Ljubešić, Nikola
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F scherrer-ljubesic-2021-sesame
%X This paper describes the HEL-LJU submissions to the MultiLexNorm shared task on multilingual lexical normalization. Our system is based on a BERT token classification preprocessing step, where for each token the type of the necessary transformation is predicted (none, uppercase, lowercase, capitalize, modify), and a character-level SMT step where the text is translated from original to normalized given the BERT-predicted transformation constraints. For some languages, depending on the results on development data, the training data was extended by back-translating OpenSubtitles data. In the final ordering of the ten participating teams, the HEL-LJU team has taken the second place, scoring better than the previous state-of-the-art.
%R 10.18653/v1/2021.wnut-1.52
%U https://aclanthology.org/2021.wnut-1.52
%U https://doi.org/10.18653/v1/2021.wnut-1.52
%P 465-472
Markdown (Informal)
[Sesame Street to Mount Sinai: BERT-constrained character-level Moses models for multilingual lexical normalization](https://aclanthology.org/2021.wnut-1.52) (Scherrer & Ljubešić, WNUT 2021)
ACL