A Simple Approach to LearningUnsupervised Multilingual Embeddings
Table 1: Average Precision@1 for BLI obtained by multilingual algorithms on six European languages (German, English, Spanish, French, Italian, and Portuguese) from the MUSE dataset. The results are obtained for every combination of source-target pair. The column de-xx (xx-de) denotes average performance when German is considered the source (target) language. It can be observed that all the approaches obtain similar generalization on the test set. The final average of each method is computed on thirty results.
['[EMPTY]', 'de-xx', 'en-xx', 'es-xx', 'fr-xx', 'it-xx', 'pt-xx', 'xx-de', 'xx-en', 'xx-es', 'xx-fr', 'xx-it', 'xx-pt', 'avg.']
Results Experiment 1: We begin with BLI task on a group of six relatively close European languages – German, English, Spanish, French, Italian, and Portuguese – from the MUSE dataset. We experiment on every pair of languages and in both directions, leading to thirty results for each method. We observe that the proposed two-stage methods, UMML-GW and UMML-SL, obtain scores on par with state-of-the-art methods, UMWE and UMH. This shows that in case of similar/close-by languages, all the methods are able to learn a shared multilingual space with similar generalization performance. We also observe that all the multilingual methods outperform BilingUnsup, highlighting the benefit of transfer learning in this scenario.