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Making Neural Machine Reading Comprehension Faster
lib:ac3ae84e998b6757 (v1.0.0)
Authors: Debajyoti Chatterjee
ArXiv: 1904.00796
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1904.00796v1
This study aims at solving the Machine Reading Comprehension problem where questions have to be answered given a context passage. The challenge is to develop a computationally faster model which will have improved inference time. State of the art in many natural language understanding tasks, BERT model, has been used and knowledge distillation method has been applied to train two smaller models. The developed models are compared with other models which have been developed with the same intention.
ArXiv: 1904.00796
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1904.00796v1
This study aims at solving the Machine Reading Comprehension problem where questions have to be answered given a context passage. The challenge is to develop a computationally faster model which will have improved inference time. State of the art in many natural language understanding tasks, BERT model, has been used and knowledge distillation method has been applied to train two smaller models. The developed models are compared with other models which have been developed with the same intention.
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