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Improving zero-shot learning by mitigating the hubness problem
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Improving zero-shot learning by mitigating the hubness problem
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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.
Forward citations
Cited by 5 Pith papers
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Hubness dominates as the causal driver of cross-lingual retrieval asymmetry in multilingual embeddings, with CSLS correction closing most of the reciprocity gap across five models and a 6518-item parallel corpus.
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Unsupervised Adversarial Graph Alignment with Graph Embedding
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A single hub text can unreasonably match many images in CLIP-based similarity, exposing vulnerabilities in cross-modal encoders for caption evaluation and retrieval.
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