Attentive Mimicking:Better Word Embeddings by Attending to Informative Contexts
Table 6: Significance results for the VecMap evaluation. Each cell lists the numbers of word occurrences for which the model of the row performs significantly better than the model of the column (p<0.05). For example, FCM is significantly better than skipgram for 1, 2, 4, 8 and 16 contexts.
['[BOLD] model', 'skipgram', 'fastText', 'Mimick', 'FCM', 'AM', 'FCM†', 'AM†']
For VecMap, given two models m1 and m2, we count the number of times that the embedding assigned to a word w by m1 is closer to the gold embedding of w than the embedding assigned by m2; we do so for each number of occurrences separately. As can be seen, both FCM and AM perform significantly better than the original skipgram embeddings for up to 16 contexts, but the difference between FCM and AM is only significant given one or four contexts. However, for the variants that include downsampled words during training, AM† (using attention) is significantly better than FCM† (without attention) given more than one context.