Density Matching for Bilingual Word Embedding
Table 1: Precision@1 for the MUSE BLI task compared with previous work. All the baseline results employ CSLS as the retrieval metric except for Sinkhorn∗ which uses cosine similarity. R represents refinement. Bold and italic indicate the best unsupervised and overall numbers respectively. (’en’ is English, ’es’ is Spanish, ’de’ is German, ’fr’ is French, ’ru’ is Russian, ’zh’ is traditional Chinese, ’ja’ is Japanese.)
['[EMPTY]', 'en-es', 'es-en', 'en-de', 'de-en', 'en-fr', 'fr-en', 'en-ru', 'ru-en', 'en-zh', 'zh-en', 'en-ja', 'ja-en']
In Tab. For DeMa-BME, we present results with and without refinement, and with CSLS and CSLS-D as retrieval methods. From Tab. The results without the iterative refinement CSLS (w/o R) are strong on almost all language pairs with particularly strong performance being observed on es-en, en-de and en-fr on which DeMa-BME outperforms or is on par with the best performing methods. Applying refinement to DeMa-BME brings slightly better performance on most language pairs but degrades the performance on some language pairs such as es-en, zh-en for which the DeMa-BME already obtains very good results.