@inproceedings{lange-etal-2023-multilingual,
title = "Multilingual Normalization of Temporal Expressions with Masked Language Models",
author = {Lange, Lukas and
Str{\"o}tgen, Jannik and
Adel, Heike and
Klakow, Dietrich},
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.84",
doi = "10.18653/v1/2023.eacl-main.84",
pages = "1174--1186",
abstract = "The detection and normalization of temporal expressions is an important task and preprocessing step for many applications. However, prior work on normalization is rule-based, which severely limits the applicability in real-world multilingual settings, due to the costly creation of new rules. We propose a novel neural method for normalizing temporal expressions based on masked language modeling. Our multilingual method outperforms prior rule-based systems in many languages, and in particular, for low-resource languages with performance improvements of up to 33 F1 on average compared to the state of the art.",
}
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%0 Conference Proceedings
%T Multilingual Normalization of Temporal Expressions with Masked Language Models
%A Lange, Lukas
%A Strötgen, Jannik
%A Adel, Heike
%A Klakow, Dietrich
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F lange-etal-2023-multilingual
%X The detection and normalization of temporal expressions is an important task and preprocessing step for many applications. However, prior work on normalization is rule-based, which severely limits the applicability in real-world multilingual settings, due to the costly creation of new rules. We propose a novel neural method for normalizing temporal expressions based on masked language modeling. Our multilingual method outperforms prior rule-based systems in many languages, and in particular, for low-resource languages with performance improvements of up to 33 F1 on average compared to the state of the art.
%R 10.18653/v1/2023.eacl-main.84
%U https://aclanthology.org/2023.eacl-main.84
%U https://doi.org/10.18653/v1/2023.eacl-main.84
%P 1174-1186
Markdown (Informal)
[Multilingual Normalization of Temporal Expressions with Masked Language Models](https://aclanthology.org/2023.eacl-main.84) (Lange et al., EACL 2023)
ACL