# Making Neural Machine Reading Comprehension Faster

arXiv: https://arxiv.org/abs/1904.00796 · HTML: https://mlsanity.com/paper/1904.00796/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2019.

## Built on by (most cited first, 2 of 2)

- [Distilling Large Language Models into Tiny and Effective Students using pQRNN](https://mlsanity.com/paper/2101.08890/) (2021)
- [Dual-View Distilled BERT for Sentence Embedding](https://mlsanity.com/paper/2104.08675/) (2021)

## Abstract (arXiv)

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.