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@inproceedings{1784399,
author = {Novotný, Vít and Ayetiran, Eniafe Festus and Bačovský, Dalibor and Lupták, Dávid and Štefánik, Michal and Sojka, Petr},
address = {Varna, Bulgaria},
booktitle = {Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)},
doi = {http://dx.doi.org/10.26615/978-954-452-072-4_120},
editor = {Mitkov, Ruslan and Angelova, Galia},
keywords = {fastText; representation learning; word analogy; hyperparameter optimization; language modeling; language distance},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Varna, Bulgaria},
isbn = {978-954-452-072-4},
pages = {1068-1074},
publisher = {INCOMA Ltd.},
title = {One Size Does Not Fit All: Finding the Optimal Subword Sizes for FastText Models across Languages},
url = {https://doi.org/10.26615/978-954-452-072-4_120},
year = {2021}
}
TY - JOUR
ID - 1784399
AU - Novotný, Vít - Ayetiran, Eniafe Festus - Bačovský, Dalibor - Lupták, Dávid - Štefánik, Michal - Sojka, Petr
PY - 2021
TI - One Size Does Not Fit All: Finding the Optimal Subword Sizes for FastText Models across Languages
PB - INCOMA Ltd.
CY - Varna, Bulgaria
SN - 9789544520724
KW - fastText
KW - representation learning
KW - word analogy
KW - hyperparameter optimization
KW - language modeling
KW - language distance
UR - https://doi.org/10.26615/978-954-452-072-4_120
N2 - Unsupervised representation learning of words from large multilingual corpora is useful for downstream tasks such as word sense disambiguation, semantic text similarity, and information retrieval. The representation precision of log-bilinear fastText models is mostly due to their use of subword information. In previous work, the optimization of fastText's subword sizes has not been fully explored, and non-English fastText models were trained using subword sizes optimized for English and German word analogy tasks. In our work, we find the optimal subword sizes on the English, German, Czech, Italian, Spanish, French, Hindi, Turkish, and Russian word analogy tasks. We then propose a simple n-gram coverage model and we show that it predicts better-than-default subword sizes on the Spanish, French, Hindi, Turkish, and Russian word analogy tasks. We show that the optimization of fastText's subword sizes matters and results in a 14% improvement on the Czech word analogy task. We also show that expensive parameter optimization can be replaced by a simple n-gram coverage model that consistently improves the accuracy of fastText models on the word analogy tasks by up to 3% compared to the default subword sizes, and that it is within 1% accuracy of the optimal subword sizes.
ER -
NOVOTNÝ, Vít, Eniafe Festus AYETIRAN, Dalibor BAČOVSKÝ, Dávid LUPTÁK, Michal ŠTEFÁNIK and Petr SOJKA. One Size Does Not Fit All: Finding the Optimal Subword Sizes for FastText Models across Languages. In Mitkov, Ruslan and Angelova, Galia. \textit{Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)}. Varna, Bulgaria: INCOMA Ltd., 2021, p.~1068-1074. ISBN~978-954-452-072-4. Available from: https://dx.doi.org/10.26615/978-954-452-072-4\_{}120.
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