CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Shortcut Sequence Tagging
Wu, Huijia; Zhang, Jiajun; Zong, Chengqing
2017-11
Conference NameNLPCC-2017
Conference Date2017-11.8-12
Conference PlaceDalian, China
Abstract

Deep stacked RNNs are usually hard to train. Recent studies have shown that shortcut connections across different RNN layers bring substantially faster convergence. However, shortcuts increase the computational complexity of the recurrent computations. To reduce the complexity, we propose the shortcut block, which is a refinement of the shortcut LSTM blocks. Our approach is to replace the self-connected parts (c l t) with shortcuts (h l−2 t ) in the internal states. We present extensive empirical experiments showing that this design performs better than the original shortcuts. We evaluate our method on CCG supertagging task, obtaining a 8% relatively improvement over current state-of-the-art results.

Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20679
Collection模式识别国家重点实验室_自然语言处理
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Wu, Huijia,Zhang, Jiajun,Zong, Chengqing. Shortcut Sequence Tagging[C],2017.
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