Abstract
Word embedding has been found to be highly powerful to translate words from one language to another by a simple linear transform. However, we found some inconsistence among the objective functions of the embedding and the transform learning, as well as the distance measurement. This paper proposes a solution which normalizes the word vectors on a hypersphere and constrains the linear transform as an orthogonal transform. The experimental results confirmed that the proposed solution can offer better performance on a word similarity task and an English-to-Spanish word translation task.
Cite
CITATION STYLE
Xing, C., Wang, D., Liu, C., & Lin, Y. (2015). Normalized word embedding and orthogonal transform for bilingual word translation. In NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 1006–1011). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/n15-1104
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