@inproceedings{dan-etal-2020-locally,
title = "A Locally Linear Procedure for Word Translation",
author = "Dan, Soham and
Taitelbaum, Hagai and
Goldberger, Jacob",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.528",
doi = "10.18653/v1/2020.coling-main.528",
pages = "6013--6018",
abstract = "Learning a mapping between word embeddings of two languages given a dictionary is an important problem with several applications. A common mapping approach is using an orthogonal matrix. The Orthogonal Procrustes Analysis (PA) algorithm can be applied to find the optimal orthogonal matrix. This solution restricts the expressiveness of the translation model which may result in sub-optimal translations. We propose a natural extension of the PA algorithm that uses multiple orthogonal translation matrices to model the mapping and derive an algorithm to learn these multiple matrices. We achieve better performance in a bilingual word translation task and a cross-lingual word similarity task compared to the single matrix baseline. We also show how multiple matrices can model multiple senses of a word.",
}
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%0 Conference Proceedings
%T A Locally Linear Procedure for Word Translation
%A Dan, Soham
%A Taitelbaum, Hagai
%A Goldberger, Jacob
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F dan-etal-2020-locally
%X Learning a mapping between word embeddings of two languages given a dictionary is an important problem with several applications. A common mapping approach is using an orthogonal matrix. The Orthogonal Procrustes Analysis (PA) algorithm can be applied to find the optimal orthogonal matrix. This solution restricts the expressiveness of the translation model which may result in sub-optimal translations. We propose a natural extension of the PA algorithm that uses multiple orthogonal translation matrices to model the mapping and derive an algorithm to learn these multiple matrices. We achieve better performance in a bilingual word translation task and a cross-lingual word similarity task compared to the single matrix baseline. We also show how multiple matrices can model multiple senses of a word.
%R 10.18653/v1/2020.coling-main.528
%U https://aclanthology.org/2020.coling-main.528
%U https://doi.org/10.18653/v1/2020.coling-main.528
%P 6013-6018
Markdown (Informal)
[A Locally Linear Procedure for Word Translation](https://aclanthology.org/2020.coling-main.528) (Dan et al., COLING 2020)
ACL
- Soham Dan, Hagai Taitelbaum, and Jacob Goldberger. 2020. A Locally Linear Procedure for Word Translation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6013–6018, Barcelona, Spain (Online). International Committee on Computational Linguistics.