Lookahead Convolution Layer for Unidirectional Recurrent Neural Networks

Chong Wang, Dani Yogatama, Adam Coates, Tony Han, Awni Hannun, Bo Xiao

Feb 18, 2016 (modified: Feb 18, 2016) ICLR 2016 workshop submission readers: everyone
  • Abstract: Recurrent neural networks (RNNs) have been shown to be very effective for many sequential prediction problems such as speech recognition, machine translation, part-of-speech tagging, and others. The best variant is typically a bidirectional RNN that learns representation for a sequence by performing a forward and a backward pass through the entire sequence. However, unlike unidirectional RNNs, bidirectional RNNs are challenging to deploy in an online and low-latency setting (e.g., in a speech recognition system), because they need to see an entire sequence before making a prediction. We introduce a lookahead convolution layer that incorporates information from future subsequences in a computationally efficient manner to improve unidirectional recurrent neural networks. We evaluate our method on speech recognition tasks for two languages---English and Chinese. Our experiments show that the proposed method outperforms vanilla unidirectional RNNs and is competitive with bidirectional RNNs in terms of character and word error rates.
  • Conflicts: baidu.com

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