Papers with Sinkhorn algorithm
Computationally Efficient Wasserstein Loss for Structured Labels (2021.eacl-srw)
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| Challenge: | Existing approaches to estimate the probability distribution of labels are based on tree-Wasserstein distance. |
| Approach: | They propose a tree-Wasserstein distance regularized LDL algorithm for hierarchical text classification tasks. |
| Outcome: | The proposed method performs well on synthetic and real-world datasets and compares favorably with the Sinkhorn algorithm in terms of computation time and memory usage. |
Adaptive Graph Convolutional Network for Knowledge Graph Entity Alignment (2022.findings-emnlp)
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| Challenge: | Entity alignment (EA) aims to identify equivalent entities from different Knowledge Graphs (KGs) noisy neighbors of entities transfer invalid information, drown out equivalent information, and ultimately reduce the performance of EA. |
| Approach: | They propose a method to deal with neighbor noises to reduce the performance of EA by capturing the differences and complementarities of multiple KGs. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods in supervised and unsupervised settings. |
Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport (2020.acl-main)
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| Challenge: | Existing models that use only rationales to explain a prediction are limited by the complexity of deep neural networks. |
| Approach: | They extend selective rationalization to text matching by using optimal transport to find a minimal cost alignment between inputs. |
| Outcome: | The proposed model achieves very sparse rationale selections with high fidelity while preserving prediction accuracy compared to strong attention baseline models. |
A linear time approximation of Wasserstein distance with word embedding selection (2023.emnlp-main)
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| Challenge: | Wasserstein distance is a powerful method for measuring the dissimilarity between distributions and is used in natural language processing to measure dissimilarities between documents. |
| Approach: | They propose a method to combine feature selection and tree approximation of Wasserstein distance to handle high-dimensional problems. |
| Outcome: | The proposed method achieves high performance on document classification using word embeddings and word embeds. |