@inproceedings{cao-etal-2022-xltime,
title = "{XLT}ime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction",
author = "Cao, Yuwei and
Groves, William and
Saha, Tanay Kumar and
Tetreault, Joel and
Jaimes, Alejandro and
Peng, Hao and
Yu, Philip",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.148",
doi = "10.18653/v1/2022.findings-naacl.148",
pages = "1931--1942",
abstract = "Temporal Expression Extraction (TEE) is essential for understanding time in natural language. It has applications in Natural Language Processing (NLP) tasks such as question answering, information retrieval, and causal inference. To date, work in this area has mostly focused on English as there is a scarcity of labeled data for other languages. We propose XLTime, a novel framework for multilingual TEE. XLTime works on top of pre-trained language models and leverages multi-task learning to prompt cross-language knowledge transfer both from English and within the non-English languages. XLTime alleviates problems caused by a shortage of data in the target language. We apply XLTime with different language models and show that it outperforms the previous automatic SOTA methods on French, Spanish, Portuguese, and Basque, by large margins. XLTime also closes the gap considerably on the handcrafted HeidelTime method.",
}
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<abstract>Temporal Expression Extraction (TEE) is essential for understanding time in natural language. It has applications in Natural Language Processing (NLP) tasks such as question answering, information retrieval, and causal inference. To date, work in this area has mostly focused on English as there is a scarcity of labeled data for other languages. We propose XLTime, a novel framework for multilingual TEE. XLTime works on top of pre-trained language models and leverages multi-task learning to prompt cross-language knowledge transfer both from English and within the non-English languages. XLTime alleviates problems caused by a shortage of data in the target language. We apply XLTime with different language models and show that it outperforms the previous automatic SOTA methods on French, Spanish, Portuguese, and Basque, by large margins. XLTime also closes the gap considerably on the handcrafted HeidelTime method.</abstract>
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%0 Conference Proceedings
%T XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction
%A Cao, Yuwei
%A Groves, William
%A Saha, Tanay Kumar
%A Tetreault, Joel
%A Jaimes, Alejandro
%A Peng, Hao
%A Yu, Philip
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F cao-etal-2022-xltime
%X Temporal Expression Extraction (TEE) is essential for understanding time in natural language. It has applications in Natural Language Processing (NLP) tasks such as question answering, information retrieval, and causal inference. To date, work in this area has mostly focused on English as there is a scarcity of labeled data for other languages. We propose XLTime, a novel framework for multilingual TEE. XLTime works on top of pre-trained language models and leverages multi-task learning to prompt cross-language knowledge transfer both from English and within the non-English languages. XLTime alleviates problems caused by a shortage of data in the target language. We apply XLTime with different language models and show that it outperforms the previous automatic SOTA methods on French, Spanish, Portuguese, and Basque, by large margins. XLTime also closes the gap considerably on the handcrafted HeidelTime method.
%R 10.18653/v1/2022.findings-naacl.148
%U https://aclanthology.org/2022.findings-naacl.148
%U https://doi.org/10.18653/v1/2022.findings-naacl.148
%P 1931-1942
Markdown (Informal)
[XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction](https://aclanthology.org/2022.findings-naacl.148) (Cao et al., Findings 2022)
ACL