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- W4385688710 abstract "Knowledge distillation is commonly used in training a neural document ranking model by employing a teacher to guide model refinement. As a teacher may not be correct in all cases, over-calibration between the student and teacher models can make training less effective. This paper focuses on the KL divergence loss used for knowledge distillation in document re-ranking, and re-visits balancing of knowledge distillation with explicit contrastive learning. The proposed loss function takes a conservative approach in imitating teacher's behavior, and allows student to deviate from a teacher's model sometimes through training. This paper presents analytic results with an evaluation on MS MARCO passages to validate the usefulness of the proposed loss for the transformer-based ColBERT re-ranking." @default.
- W4385688710 created "2023-08-10" @default.
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- W4385688710 date "2023-08-09" @default.
- W4385688710 modified "2023-09-26" @default.
- W4385688710 title "Balanced Knowledge Distillation with Contrastive Learning for Document Re-ranking" @default.
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- W4385688710 doi "https://doi.org/10.1145/3578337.3605120" @default.
- W4385688710 hasPublicationYear "2023" @default.
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