Density Matching for Bilingual Word Embedding

Chunting Zhou, Xuezhe Ma, Di Wang, Graham Neubig


Abstract
Recent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages. In this paper, we propose an approach that instead expresses the two monolingual embedding spaces as probability densities defined by a Gaussian mixture model, and matches the two densities using a method called normalizing flow. The method requires no explicit supervision, and can be learned with only a seed dictionary of words that have identical strings. We argue that this formulation has several intuitively attractive properties, particularly with the respect to improving robustness and generalization to mappings between difficult language pairs or word pairs. On a benchmark data set of bilingual lexicon induction and cross-lingual word similarity, our approach can achieve competitive or superior performance compared to state-of-the-art published results, with particularly strong results being found on etymologically distant and/or morphologically rich languages.
Anthology ID:
N19-1161
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1588–1598
Language:
URL:
https://aclanthology.org/N19-1161
DOI:
10.18653/v1/N19-1161
Bibkey:
Cite (ACL):
Chunting Zhou, Xuezhe Ma, Di Wang, and Graham Neubig. 2019. Density Matching for Bilingual Word Embedding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1588–1598, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Density Matching for Bilingual Word Embedding (Zhou et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1161.pdf
Video:
 https://vimeo.com/364706803
Code
 violet-zct/DeMa-BWE