@inproceedings{sorokin-2020-getting,
title = "Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation",
author = "Sorokin, Alexey",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.490",
pages = "3978--3983",
abstract = "We investigate how to improve quality of low-resource morphological inflection without annotating more data. We examine two methods, language models and data augmentation. We show that the model whose decoder that additionally uses the states of the langauge model improves the model quality by 1.5{\%} in combination with both baselines. We also demonstrate that the augmentation of data improves performance by 9{\%} in average when adding 1000 artificially generated word forms to the dataset.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<namePart type="given">Hitoshi</namePart>
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<namePart type="given">Hélène</namePart>
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<abstract>We investigate how to improve quality of low-resource morphological inflection without annotating more data. We examine two methods, language models and data augmentation. We show that the model whose decoder that additionally uses the states of the langauge model improves the model quality by 1.5% in combination with both baselines. We also demonstrate that the augmentation of data improves performance by 9% in average when adding 1000 artificially generated word forms to the dataset.</abstract>
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<url>https://aclanthology.org/2020.lrec-1.490</url>
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<date>2020-05</date>
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<start>3978</start>
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%0 Conference Proceedings
%T Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation
%A Sorokin, Alexey
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F sorokin-2020-getting
%X We investigate how to improve quality of low-resource morphological inflection without annotating more data. We examine two methods, language models and data augmentation. We show that the model whose decoder that additionally uses the states of the langauge model improves the model quality by 1.5% in combination with both baselines. We also demonstrate that the augmentation of data improves performance by 9% in average when adding 1000 artificially generated word forms to the dataset.
%U https://aclanthology.org/2020.lrec-1.490
%P 3978-3983
Markdown (Informal)
[Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation](https://aclanthology.org/2020.lrec-1.490) (Sorokin, LREC 2020)
ACL