Papers by Yuki Takezawa
Large-scale similarity search with Optimal Transport (2023.emnlp-main)
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| Challenge: | Word mover's distance (WMD) is a powerful tool for comparing probability distributions in NLP. |
| Approach: | They propose a waterstein distance approximation that uses the L1 embedding method to find the k-nearest neighbors. |
| Outcome: | The proposed approximation performs comparable to the vanilla Wasserstein distance and can be computed three orders of magnitude faster than the vanilla waterstein distance. |