Generalizing Procrustes Analysis for Better Bilingual Dictionary Induction

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

Standard

Generalizing Procrustes Analysis for Better Bilingual Dictionary Induction. / Kementchedjhieva, Yova Radoslavova; Ruder, Sebastian ; Cotterell, Ryan ; Søgaard, Anders.

Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018). Association for Computational Linguistics, 2018. p. 211–220.

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

Harvard

Kementchedjhieva, YR, Ruder, S, Cotterell, R & Søgaard, A 2018, Generalizing Procrustes Analysis for Better Bilingual Dictionary Induction. in Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018). Association for Computational Linguistics, pp. 211–220, 22nd Conference on Computational Natural Language Learning (CoNLL 2018), Brussels, Belgium, 31/10/2018.

APA

Kementchedjhieva, Y. R., Ruder, S., Cotterell, R., & Søgaard, A. (2018). Generalizing Procrustes Analysis for Better Bilingual Dictionary Induction. In Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018) (pp. 211–220). Association for Computational Linguistics.

Vancouver

Kementchedjhieva YR, Ruder S, Cotterell R, Søgaard A. Generalizing Procrustes Analysis for Better Bilingual Dictionary Induction. In Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018). Association for Computational Linguistics. 2018. p. 211–220

Author

Kementchedjhieva, Yova Radoslavova ; Ruder, Sebastian ; Cotterell, Ryan ; Søgaard, Anders. / Generalizing Procrustes Analysis for Better Bilingual Dictionary Induction. Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018). Association for Computational Linguistics, 2018. pp. 211–220

Bibtex

@inproceedings{3a8ec48814ff4a6cbc086cdfa14cd47e,
title = "Generalizing Procrustes Analysis for Better Bilingual Dictionary Induction",
abstract = "Most recent approaches to bilingual dictionaryinduction find a linear alignment between theword vector spaces of two languages. Weshow that projecting the two languages ontoa third, latent space, rather than directly ontoeach other, while equivalent in terms of expressivity,makes it easier to learn approximatealignments. Our modified approach also allowsfor supporting languages to be includedin the alignment process, to obtain an even betterperformance in low resource settings.",
author = "Kementchedjhieva, {Yova Radoslavova} and Sebastian Ruder and Ryan Cotterell and Anders S{\o}gaard",
year = "2018",
language = "English",
pages = "211–220",
booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018)",
publisher = "Association for Computational Linguistics",
note = "22nd Conference on Computational Natural Language Learning (CoNLL 2018) ; Conference date: 31-10-2018 Through 01-11-2018",

}

RIS

TY - GEN

T1 - Generalizing Procrustes Analysis for Better Bilingual Dictionary Induction

AU - Kementchedjhieva, Yova Radoslavova

AU - Ruder, Sebastian

AU - Cotterell, Ryan

AU - Søgaard, Anders

PY - 2018

Y1 - 2018

N2 - Most recent approaches to bilingual dictionaryinduction find a linear alignment between theword vector spaces of two languages. Weshow that projecting the two languages ontoa third, latent space, rather than directly ontoeach other, while equivalent in terms of expressivity,makes it easier to learn approximatealignments. Our modified approach also allowsfor supporting languages to be includedin the alignment process, to obtain an even betterperformance in low resource settings.

AB - Most recent approaches to bilingual dictionaryinduction find a linear alignment between theword vector spaces of two languages. Weshow that projecting the two languages ontoa third, latent space, rather than directly ontoeach other, while equivalent in terms of expressivity,makes it easier to learn approximatealignments. Our modified approach also allowsfor supporting languages to be includedin the alignment process, to obtain an even betterperformance in low resource settings.

M3 - Article in proceedings

SP - 211

EP - 220

BT - Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018)

PB - Association for Computational Linguistics

T2 - 22nd Conference on Computational Natural Language Learning (CoNLL 2018)

Y2 - 31 October 2018 through 1 November 2018

ER -

ID: 214760150