Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task
Table 5: Textual similarity scores (asymmetric, Multi30k).
['[EMPTY]', 'EN → DE R@1', 'EN → DE R@5', 'EN → DE R@10', 'DE → EN R@1', 'DE → EN R@5', 'DE → EN R@10']
Table 5, we show the performance on Multi30k dataset in asymmetric mode. AME outperforms the FME model, confirming the importance of word embeddings adaptation.