Multi-Task Learning for Machine Reading Comprehension

By Yichong Xu, Xiaodong Liu, Yelong Shen, Jingjing Liu, Jianfeng Gao. 2018

13 citations recorded by the source corpus.

Subfields: Natural Language Processing

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In Natural Language Processing

1. Jianfeng Gao
2. Xiaodong Liu
3. Weizhu Chen
4. Pengcheng He
5. Kevin Duh
6. Caiming Xiong
7. Richard Socher
8. Samuel R. Bowman

Citations in the corpus

• Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding (2019, methodology): “We extend the knowledge distillation method (Hinton et al., 2015) to the multi-task learning setting (Caruana, 1997; Xu et al., 2018; Collobert et al., 2011; Zhang and Yang, 2017; Liu et al., 2015).”
• Multi-Task Deep Neural Networks for Natural Language Understanding (2019, background): “Recently, there is a growing interest in applying MTL to representation learning using deep neural networks (DNNs) (Collobert et al., 2011; Liu et al., 2015; Luong et al., 2015; Xu et al., 2018) for two reasons.”
• Stochastic Answer Networks for Natural Language Inference (2018, methodology): “Inspired by the recent success of multi-step inference on Machine Reading Comprehension (MRC) (Hill et al., 2016; Dhin-gra et al., 2016; Sordoni et al., 2016; Kumar et al., 2015; Liu et al., 2018; Shen et al., 2017; Xu et al., 2018), we explore the multi-step inference strategies on NLI.”