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Pre-trained models like BERT (Devlin et al., 2018) have dominated NLP / IRapplications such as single sentence classification, text pair classification,and question answering. However, deploying these models in real systems ishighly non-trivial due to their exorbitant computational costs. A common remedyto this is knowledge distillation (Hinton et al., 2015), leading to fasterinference. However -- as we show here -- existing works are not optimized fordealing with pairs (or tuples) of texts. Consequently, they are either notscalable or demonstrate subpar performance. In this work, we propose DiPair --a novel framework for distilling fast and accurate models on text pair tasks.Coupled with an end-to-end training strategy, DiPair is both highly scalableand offers improved quality-speed tradeoffs. Empirical studies conducted onboth academic and real-world e-commerce benchmarks demonstrate the efficacy ofthe proposed approach with speedups of over 350x and minimal quality droprelative to the cross-attention teacher BERT model.

DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling