Lookahead Convolution Layer for Unidirectional Recurrent Neural Networks
Authors: Chong Wang, Dani Yogatama, Adam Coates, Tony Han, Awni Hannun, Bo Xiao

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.