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        <title>Cross-Family Similarity Learning for Cognate Identification in Low-Resource Languages</title>
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        <namePart type="given">Eliel</namePart>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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    <abstract>We address the problem of cognate identification across vocabulary pairs of any set of languages. In particular, we focus on the case where the examined pair of languages are low-resource to the extent that no training data whatsoever in these languages, or even closely related ones, are available for the task. We investigate the extent to which training data from another, unrelated language family can be used instead. Our approach consists of learning a similarity metric from example cognates in Indo-European languages and applying it to low-resource Sami languages of the Uralic family. We apply two models following previous work: a Siamese convolutional neural network (S-CNN) and a support vector machine (SVM), and compare them with a Levenshtein-distance baseline. We test performance on three Sami languages and find that the S-CNN outperforms the other approaches, suggesting that it is better able to learn such general characteristics of cognateness that carry over across language families. We also experiment with fine-tuning the S-CNN model with data from within the language family in order to quantify how well this model can make use of a small amount of target-domain data to adapt.</abstract>
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%0 Conference Proceedings
%T Cross-Family Similarity Learning for Cognate Identification in Low-Resource Languages
%A Soisalon-Soininen, Eliel
%A Granroth-Wilding, Mark
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F soisalon-soininen-granroth-wilding-2019-cross
%X We address the problem of cognate identification across vocabulary pairs of any set of languages. In particular, we focus on the case where the examined pair of languages are low-resource to the extent that no training data whatsoever in these languages, or even closely related ones, are available for the task. We investigate the extent to which training data from another, unrelated language family can be used instead. Our approach consists of learning a similarity metric from example cognates in Indo-European languages and applying it to low-resource Sami languages of the Uralic family. We apply two models following previous work: a Siamese convolutional neural network (S-CNN) and a support vector machine (SVM), and compare them with a Levenshtein-distance baseline. We test performance on three Sami languages and find that the S-CNN outperforms the other approaches, suggesting that it is better able to learn such general characteristics of cognateness that carry over across language families. We also experiment with fine-tuning the S-CNN model with data from within the language family in order to quantify how well this model can make use of a small amount of target-domain data to adapt.
%R 10.26615/978-954-452-056-4_129
%U https://aclanthology.org/R19-1129
%U https://doi.org/10.26615/978-954-452-056-4_129
%P 1121-1130

Markdown (Informal)

[Cross-Family Similarity Learning for Cognate Identification in Low-Resource Languages](https://aclanthology.org/R19-1129) (Soisalon-Soininen & Granroth-Wilding, RANLP 2019)

• Cross-Family Similarity Learning for Cognate Identification in Low-Resource Languages (Soisalon-Soininen & Granroth-Wilding, RANLP 2019)

ACL

• Eliel Soisalon-Soininen and Mark Granroth-Wilding. 2019. Cross-Family Similarity Learning for Cognate Identification in Low-Resource Languages. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1121–1130, Varna, Bulgaria. INCOMA Ltd..