Help from the Neighbors: Estonian Dialect Normalization Using a Finnish Dialect Generator

Mika Hämäläinen, Khalid Alnajjar, Tuuli Tuisk


Abstract
While standard Estonian is not a low-resourced language, the different dialects of the language are under-resourced from the point of view of NLP, given that there are no vast hand normalized resources available for training a machine learning model to normalize dialectal Estonian to standard Estonian. In this paper, we crawl a small corpus of parallel dialectal Estonian - standard Estonian sentences. In addition, we take a savvy approach of generating more synthetic training data for the normalization task by using an existing dialect generator model built for Finnish to "dialectalize" standard Estonian sentences from the Universal Dependencies tree banks. Our BERT based normalization model achieves a word error rate that is 26.49 points lower when using both the synthetic data and Estonian data in comparison to training the model with only the available Estonian data. Our results suggest that synthetic data generated by a model trained on a more resourced related language can indeed boost the results for a less resourced language.
Anthology ID:
2022.deeplo-1.7
Volume:
Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing
Month:
July
Year:
2022
Address:
Hybrid
Venue:
DeepLo
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
61–66
Language:
URL:
https://aclanthology.org/2022.deeplo-1.7
DOI:
10.18653/v1/2022.deeplo-1.7
Bibkey:
Cite (ACL):
Mika Hämäläinen, Khalid Alnajjar, and Tuuli Tuisk. 2022. Help from the Neighbors: Estonian Dialect Normalization Using a Finnish Dialect Generator. In Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing, pages 61–66, Hybrid. Association for Computational Linguistics.
Cite (Informal):
Help from the Neighbors: Estonian Dialect Normalization Using a Finnish Dialect Generator (Hämäläinen et al., DeepLo 2022)
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PDF:
https://aclanthology.org/2022.deeplo-1.7.pdf
Video:
 https://aclanthology.org/2022.deeplo-1.7.mp4