Embedding Word Similarity with Neural Machine Translation
Table 4: Comparison of embeddings learned by the original (RNN Search - 30k French words, 50k German words) and extended-vocabulary (RNN Search-LV -500k words) models translating from English to French (EN-FR) and from English to German (EN-DE). For fair comparisons, all evaluations were restricted to the intersection of all model vocabularies.
['[EMPTY]', '[EMPTY]', '[BOLD] RNN Search  [BOLD] EN-FR', '[BOLD] RNN Search  [BOLD] EN-DE', '[BOLD] RNN Search-LV  [BOLD] EN-FR', '[BOLD] RNN Search-LV  [BOLD] EN-DE']
Note that for a fair comparison we filtered these evaluations to only include items that are present in the smaller vocabulary. Thus, the numbers do not directly reflect the quality of the additional 470k embeddings learned by the extended vocabulary models, which one would expect to be lower since they are words of lower frequency.