Papers with hubness problem
A Discriminative Latent-Variable Model for Bilingual Lexicon Induction (D18-1)
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| Challenge: | Existing methods for bilingual lexicon induction take advantage of word embeddings, but our model is not as efficient as previous work. |
| Approach: | They propose a discriminative latent-variable model for bilingual lexicon induction that combines the bipartite matching dictionary prior and an embedding-based approach. |
| Outcome: | The proposed model outperforms existing models on six language pairs and shows that it mitigates hubness problem. |
Dangling-Aware Entity Alignment with Mixed High-Order Proximities (2022.findings-naacl)
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Juncheng Liu, Zequn Sun, Bryan Hooi, Yiwei Wang, Dayiheng Liu, Baosong Yang, Xiaokui Xiao, Muhao Chen
| Challenge: | Existing methods for dangling-aware entity alignment are underexplored but important problem. |
| Approach: | They propose a framework that uses high-order proximities to detect dangling entities and align matchable entities. |
| Outcome: | The proposed framework detects dangling entities and aligns matchable entities better than existing methods. |
Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Banks (2023.emnlp-main)
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| Challenge: | a small number of gallery data points are frequently retrieved, resulting in a decline in retrieval performance. |
| Approach: | They propose a framework that leverages both gallery and query data to address hubness . they propose dual inverted softmax and dual dynamic inverted hardmax methods to normalize similarity . |
| Outcome: | The proposed framework reduces the occurrence of hubs during inference while improving similarity between non-hubs and queries. |
One Single Hub Text Breaks CLIP: Identifying Vulnerabilities in Cross-Modal Encoders via Hubness (2026.acl-long)
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| Challenge: | et al., 2010) show that hub embeddings are close to many unrelated examples in high-dimensional embeddable spaces . cross-modal encoders that project different modalities into a shared space are useful for cross-module applications . |
| Approach: | They propose a method for identifying the hub embedding and its corresponding hub text . they use images to evaluate cross-modal encoders that project different modalities into a shared space . |
| Outcome: | The proposed method can identify a single hub embedding and its corresponding hub text . it achieves comparable or higher similarity scores than human-written reference captions in many images . |