Papers by Jun Hirako
Search Query Embeddings via User-behavior-driven Contrastive Learning (2025.naacl-industry)
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Sosuke Nishikawa, Jun Hirako, Nobuhiro Kaji, Koki Watanabe, Hiroki Asano, Souta Yamashiro, Shumpei Sano
| Challenge: | Existing approaches to embed search queries are limited due to shortness and surface-level variations. |
| Approach: | They propose a user-behavior-driven contrastive learning approach which directly aligns query embeddings according to user intent. |
| Outcome: | The proposed model outperforms state-of-the-art text embedding models on real-world QU tasks while minimizing lexical similarities. |
Realistic Citation Count Prediction Task for Newly Published Papers (2023.findings-eacl)
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| Challenge: | Existing studies on citation count prediction assume that future citation counts of academic papers have not had enough time pass since publication. |
| Approach: | They propose to use citation counts of newly published papers as a realistic citation count prediction task and to use them to leverage the citations of papers shortly after publication. |
| Outcome: | The proposed methods significantly improve the performance of citation count prediction for newly published papers in a realistic setting. |