Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference (2021.findings-acl)
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| Challenge: | Existing approaches to document-to-document similarity ranking are limited to relatively short documents or lack similarity labels. |
| Approach: | They propose a self-supervised method for document similarity ranking that can be applied to documents of arbitrary length. |
| Outcome: | The proposed model outperforms existing methods on large documents datasets. |
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