Tracking Typological Traits of Uralic Languages in Distributed Language Representations

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Standard

Tracking Typological Traits of Uralic Languages in Distributed Language Representations. / Bjerva, Johannes; Augenstein, Isabelle.

Proceedings, Fourth International Workshop on Computational Linguistics for Uralic Languages. Association for Computational Linguistics, 2018. p. 78-88.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Bjerva, J & Augenstein, I 2018, Tracking Typological Traits of Uralic Languages in Distributed Language Representations. in Proceedings, Fourth International Workshop on Computational Linguistics for Uralic Languages. Association for Computational Linguistics, pp. 78-88, Fourth International Workshop on Computational Linguistics for Uralic Languages, Helsinki, Finland, 08/01/2018. https://doi.org/10.18653/v1/W18-02

APA

Bjerva, J., & Augenstein, I. (2018). Tracking Typological Traits of Uralic Languages in Distributed Language Representations. In Proceedings, Fourth International Workshop on Computational Linguistics for Uralic Languages (pp. 78-88). Association for Computational Linguistics. https://doi.org/10.18653/v1/W18-02

Vancouver

Bjerva J, Augenstein I. Tracking Typological Traits of Uralic Languages in Distributed Language Representations. In Proceedings, Fourth International Workshop on Computational Linguistics for Uralic Languages. Association for Computational Linguistics. 2018. p. 78-88 https://doi.org/10.18653/v1/W18-02

Author

Bjerva, Johannes ; Augenstein, Isabelle. / Tracking Typological Traits of Uralic Languages in Distributed Language Representations. Proceedings, Fourth International Workshop on Computational Linguistics for Uralic Languages. Association for Computational Linguistics, 2018. pp. 78-88

Bibtex

@inproceedings{06178b898e5d487ca290b8bbba8ffc4b,
title = "Tracking Typological Traits of Uralic Languages in Distributed Language Representations",
abstract = "Although linguistic typology has a long history,computational approaches have only recentlygained popularity. The use of distributedrepresentations in computational linguisticshas also become increasingly popular.A recent development is to learn distributedrepresentations of language, such that typologicallysimilar languages are spatially closeto one another. Although empirical successeshave been shown for such language representations,they have not been subjected to muchtypological probing. In this paper, we firstlook at whether this type of language representationsare empirically useful for model transferbetween Uralic languages in deep neuralnetworks. We then investigate which typologicalfeatures are encoded in these representationsby attempting to predict features in theWorld Atlas of Language Structures, at variousstages of fine-tuning of the representations.We focus on Uralic languages, and findthat some typological traits can be automaticallyinferred with accuracies well above astrong baseline",
author = "Johannes Bjerva and Isabelle Augenstein",
year = "2018",
doi = "10.18653/v1/W18-02",
language = "English",
pages = "78--88",
booktitle = "Proceedings, Fourth International Workshop on Computational Linguistics for Uralic Languages",
publisher = "Association for Computational Linguistics",
note = "Fourth International Workshop on Computational Linguistics for Uralic Languages, IWCLUL 2018 ; Conference date: 08-01-2018 Through 09-01-2018",

}

RIS

TY - GEN

T1 - Tracking Typological Traits of Uralic Languages in Distributed Language Representations

AU - Bjerva, Johannes

AU - Augenstein, Isabelle

PY - 2018

Y1 - 2018

N2 - Although linguistic typology has a long history,computational approaches have only recentlygained popularity. The use of distributedrepresentations in computational linguisticshas also become increasingly popular.A recent development is to learn distributedrepresentations of language, such that typologicallysimilar languages are spatially closeto one another. Although empirical successeshave been shown for such language representations,they have not been subjected to muchtypological probing. In this paper, we firstlook at whether this type of language representationsare empirically useful for model transferbetween Uralic languages in deep neuralnetworks. We then investigate which typologicalfeatures are encoded in these representationsby attempting to predict features in theWorld Atlas of Language Structures, at variousstages of fine-tuning of the representations.We focus on Uralic languages, and findthat some typological traits can be automaticallyinferred with accuracies well above astrong baseline

AB - Although linguistic typology has a long history,computational approaches have only recentlygained popularity. The use of distributedrepresentations in computational linguisticshas also become increasingly popular.A recent development is to learn distributedrepresentations of language, such that typologicallysimilar languages are spatially closeto one another. Although empirical successeshave been shown for such language representations,they have not been subjected to muchtypological probing. In this paper, we firstlook at whether this type of language representationsare empirically useful for model transferbetween Uralic languages in deep neuralnetworks. We then investigate which typologicalfeatures are encoded in these representationsby attempting to predict features in theWorld Atlas of Language Structures, at variousstages of fine-tuning of the representations.We focus on Uralic languages, and findthat some typological traits can be automaticallyinferred with accuracies well above astrong baseline

U2 - 10.18653/v1/W18-02

DO - 10.18653/v1/W18-02

M3 - Article in proceedings

SP - 78

EP - 88

BT - Proceedings, Fourth International Workshop on Computational Linguistics for Uralic Languages

PB - Association for Computational Linguistics

T2 - Fourth International Workshop on Computational Linguistics for Uralic Languages

Y2 - 8 January 2018 through 9 January 2018

ER -

ID: 195046443