A Simple Approach to LearningUnsupervised Multilingual Embeddings
Table 6: Average Precision@1 for BLI on five European languages (German, English, Spanish, Finnish, Italian) from the VecMap dataset. The results are reported for every combination of source-target pair. The proposed method, UMML-SL, significantly improves the results of UMWE.
['[EMPTY]', 'de-xx', 'en-xx', 'es-xx', 'fi-xx', 'it-xx', 'xx-de', 'xx-en', 'xx-es', 'xx-fi', 'xx-it', 'avg.']
Results Experiment 6: In addition to the MUSE dataset, we also perform BLI experiment on the VecMap dataset. We observe that UMWE learns suitable alignments for only four languages (de, es, fi, and it). It is not able to effectively align the English language embeddings in the shared multilingual space. The authors have not responded to our query on this. Hence, without the required normalization, both UMML-GW and UMH fail to learn a good alignment on any pair of language.