@inproceedings{van-der-goot-etal-2021-multilexnorm,
title = "{M}ulti{L}ex{N}orm: A Shared Task on Multilingual Lexical Normalization",
author = {van der Goot, Rob and
Ramponi, Alan and
Zubiaga, Arkaitz and
Plank, Barbara and
Muller, Benjamin and
San Vicente Roncal, I{\~n}aki and
Ljube{\v{s}}i{\'c}, Nikola and
{\c{C}}etino{\u{g}}lu, {\"O}zlem and
Mahendra, Rahmad and
{\c{C}}olako{\u{g}}lu, Talha and
Baldwin, Timothy and
Caselli, Tommaso and
Sidorenko, Wladimir},
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-1.55",
doi = "10.18653/v1/2021.wnut-1.55",
pages = "493--509",
abstract = "Lexical normalization is the task of transforming an utterance into its standardized form. This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation. Such variation is typical for social media on which information is shared in a multitude of ways, including diverse languages and code-switching. Since the seminal work of Han and Baldwin (2011) a decade ago, lexical normalization has attracted attention in English and multiple other languages. However, there exists a lack of a common benchmark for comparison of systems across languages with a homogeneous data and evaluation setup. The MultiLexNorm shared task sets out to fill this gap. We provide the largest publicly available multilingual lexical normalization benchmark including 13 language variants. We propose a homogenized evaluation setup with both intrinsic and extrinsic evaluation. As extrinsic evaluation, we use dependency parsing and part-of-speech tagging with adapted evaluation metrics (a-LAS, a-UAS, and a-POS) to account for alignment discrepancies. The shared task hosted at W-NUT 2021 attracted 9 participants and 18 submissions. The results show that neural normalization systems outperform the previous state-of-the-art system by a large margin. Downstream parsing and part-of-speech tagging performance is positively affected but to varying degrees, with improvements of up to 1.72 a-LAS, 0.85 a-UAS, and 1.54 a-POS for the winning system.",
}
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<abstract>Lexical normalization is the task of transforming an utterance into its standardized form. This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation. Such variation is typical for social media on which information is shared in a multitude of ways, including diverse languages and code-switching. Since the seminal work of Han and Baldwin (2011) a decade ago, lexical normalization has attracted attention in English and multiple other languages. However, there exists a lack of a common benchmark for comparison of systems across languages with a homogeneous data and evaluation setup. The MultiLexNorm shared task sets out to fill this gap. We provide the largest publicly available multilingual lexical normalization benchmark including 13 language variants. We propose a homogenized evaluation setup with both intrinsic and extrinsic evaluation. As extrinsic evaluation, we use dependency parsing and part-of-speech tagging with adapted evaluation metrics (a-LAS, a-UAS, and a-POS) to account for alignment discrepancies. The shared task hosted at W-NUT 2021 attracted 9 participants and 18 submissions. The results show that neural normalization systems outperform the previous state-of-the-art system by a large margin. Downstream parsing and part-of-speech tagging performance is positively affected but to varying degrees, with improvements of up to 1.72 a-LAS, 0.85 a-UAS, and 1.54 a-POS for the winning system.</abstract>
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%0 Conference Proceedings
%T MultiLexNorm: A Shared Task on Multilingual Lexical Normalization
%A van der Goot, Rob
%A Ramponi, Alan
%A Zubiaga, Arkaitz
%A Plank, Barbara
%A Muller, Benjamin
%A San Vicente Roncal, Iñaki
%A Ljubešić, Nikola
%A Çetinoğlu, Özlem
%A Mahendra, Rahmad
%A Çolakoğlu, Talha
%A Baldwin, Timothy
%A Caselli, Tommaso
%A Sidorenko, Wladimir
%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 van-der-goot-etal-2021-multilexnorm
%X Lexical normalization is the task of transforming an utterance into its standardized form. This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation. Such variation is typical for social media on which information is shared in a multitude of ways, including diverse languages and code-switching. Since the seminal work of Han and Baldwin (2011) a decade ago, lexical normalization has attracted attention in English and multiple other languages. However, there exists a lack of a common benchmark for comparison of systems across languages with a homogeneous data and evaluation setup. The MultiLexNorm shared task sets out to fill this gap. We provide the largest publicly available multilingual lexical normalization benchmark including 13 language variants. We propose a homogenized evaluation setup with both intrinsic and extrinsic evaluation. As extrinsic evaluation, we use dependency parsing and part-of-speech tagging with adapted evaluation metrics (a-LAS, a-UAS, and a-POS) to account for alignment discrepancies. The shared task hosted at W-NUT 2021 attracted 9 participants and 18 submissions. The results show that neural normalization systems outperform the previous state-of-the-art system by a large margin. Downstream parsing and part-of-speech tagging performance is positively affected but to varying degrees, with improvements of up to 1.72 a-LAS, 0.85 a-UAS, and 1.54 a-POS for the winning system.
%R 10.18653/v1/2021.wnut-1.55
%U https://aclanthology.org/2021.wnut-1.55
%U https://doi.org/10.18653/v1/2021.wnut-1.55
%P 493-509
Markdown (Informal)
[MultiLexNorm: A Shared Task on Multilingual Lexical Normalization](https://aclanthology.org/2021.wnut-1.55) (van der Goot et al., WNUT 2021)
ACL
- Rob van der Goot, Alan Ramponi, Arkaitz Zubiaga, Barbara Plank, Benjamin Muller, Iñaki San Vicente Roncal, Nikola Ljubešić, Özlem Çetinoğlu, Rahmad Mahendra, Talha Çolakoğlu, Timothy Baldwin, Tommaso Caselli, and Wladimir Sidorenko. 2021. MultiLexNorm: A Shared Task on Multilingual Lexical Normalization. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 493–509, Online. Association for Computational Linguistics.