Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
Zhilin Yang, Ruslan Salakhutdinov, William W. Cohen
Nov 04, 2016 (modified: Mar 04, 2017)ICLR 2017 conference submissionreaders: everyone
Abstract:Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks. One appealing property of such systems is their generality, as excellent performance can be achieved with a unified architecture and without task-specific feature engineering. However, it is unclear if such systems can be used for tasks without large amounts of training data. In this paper we explore the problem of transfer learning for neural sequence taggers, where a source task with plentiful annotations (e.g., POS tagging on Penn Treebank) is used to improve performance on a target task with fewer available annotations (e.g., POS tagging for microblogs). We examine the effects of transfer learning for deep hierarchical recurrent networks across domains, applications, and languages, and show that significant improvement can often be obtained. These improvements lead to improvements over the current state-of-the-art on several well-studied tasks.
Keywords:Natural language processing, Deep learning, Transfer Learning
Enter your feedback below and we'll get back to you as soon as possible.