| Autor: |
Lien, Yen-Chieh1 ylien@cs.umass.edu, Zamani, Hamed1 zamani@cs.umass.edu, Croft, Bruce1 croft@cs.umass.edu |
| Zdroj: |
ACM Transactions on Information Systems. Sep2024, Vol. 42 Issue 5, p1-26. 26p. |
| Abstrakt: |
The article focuses on training Neural Ranking Models (NRMs) via weak supervision, using automatically generated datasets from existing ranking models, which significantly reduces the need for manually labeled data. It mentions through an iterative re-labeling process called Generalized Weak Supervision (GWS), weakly supervised models iteratively improve ranking performance without manual labeling, offering four implementations: self-labeling. |
| Databáze: |
Library, Information Science & Technology Abstracts |
| Externí odkaz: |
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