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Autoregressive Knowledge Distillation through Imitation Learning
[article]
2020
arXiv
pre-print
The performance of autoregressive models on natural language generation tasks has dramatically improved due to the adoption of deep, self-attentive architectures. However, these gains have come at the cost of hindering inference speed, making state-of-the-art models cumbersome to deploy in real-world, time-sensitive settings. We develop a compression technique for autoregressive models that is driven by an imitation learning perspective on knowledge distillation. The algorithm is designed to
arXiv:2009.07253v2
fatcat:2pk5mt46zjemznssyle2zwxua4