Dataset Open Access

Improving zero-shot learning by mitigating the hubness problem

Dinu, Georgiana; Lazaridou, Angeliki; Baroni, Marco

Data associated with the paper Improving zero-shot learning by mitigating the hubness problem, proceedings of ICLR 2015 (International Conference on Learning Representations), workshop track.

Abstract: The zero-shot paradigm exploits vector-based word representations extracted from text corpora with unsupervised methods to learn general mapping functions from other feature spaces onto word space, where the words associated to the nearest neighbours of the mapped vectors are used as their linguistic labels. We show that the neighbourhoods of the mapped elements are strongly polluted by hubs, vectors that tend to be near a high proportion of items, pushing their correct labels down the neighbour list. After illustrating the problem empirically, we propose a simple method to correct it by taking the proximity distribution of potential neighbours across many mapped vectors into account. We show that this correction leads to consistent improvements in realistic zero-shot experiments in the cross-lingual, image labeling and image retrieval domains.

Files (568.7 MB)
Name Size
transmat.zip
md5:8762782254d4aff0309ab481bb88e83e
568.7 MB Download
62
43
views
downloads
All versions This version
Views 6262
Downloads 4343
Data volume 24.5 GB24.5 GB
Unique views 4949
Unique downloads 1616

Share

Cite as