Second-Order Word Embeddingsfrom Nearest Neighbor Topological Features
Table 4: Accuracy on SNLI development and test sets.
['Setting', 'Acc % (Dev)', 'Acc % (Test)']
In contrast to the NER results, here we see an increase in overall performance when augmenting skip-gram embeddings with second-order information. The second-order embeddings alone give only slightly worse results than the skip-gram embeddings, with around a 1% drop in test accuracy. Interestingly, concatenating skip-gram with a single k=10 second-order embedding decreases performance, but using multiple samples or different settings for k show equivalent or superior performance.