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We propose utilizing n-best reranking to enhance the Sequence-Level KnowledgeDistillation (Kim and Rush, 2016) where we explore hypotheses beyond the top-1to acquire more accurate pseudo-labels. To accomplish this, we leverage adiverse set of models with different inductive biases, objective functions orarchitectures, including publicly-available large pretrained models. Theeffectiveness of our proposal is validated through experiments on the WMT'21German-English and Chinese-English translation tasks. Our results demonstratethat utilizing the pseudo-labels generated by our n-best reranker leads to asignificantly more accurate student model. In fact, our best student modelachieves comparable accuracy to a large translation model from (Tran et al.,2021) with 4.7 billion parameters, while having two orders of magnitude fewerparameters.

Accurate Knowledge Distillation with n-best Reranking