Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding. Liu, X., He, P., Chen, W., & Gao, J. 4, 2019.
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding [pdf]Paper  Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding [link]Website  abstract   bibtex   
This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks. Although ensemble learning can improve model performance, serving an ensemble of large DNNs such as MT-DNN can be prohibitively expensive. Here we apply the knowledge distillation method (Hinton et al., 2015) in the multi-task learning setting. For each task, we train an ensemble of different MT-DNNs (teacher) that outperforms any single model, and then train a single MT-DNN (student) via multi-task learning to \emphdistill knowledge from these ensemble teachers. We show that the distilled MT-DNN significantly outperforms the original MT-DNN on 7 out of 9 GLUE tasks, pushing the GLUE benchmark (single model) to 83.7\% (1.5\% absolute improvement\footnote Based on the GLUE leaderboard at https://gluebenchmark.com/leaderboard as of April 1, 2019.). The code and pre-trained models will be made publicly available at https://github.com/namisan/mt-dnn.
Downloads: 0
About
Documentation
Keywords
Search
Users
Terms and Conditions of Use
Privacy Policy
Sitemap
© BibBase.org 2021