Future Word Contexts in Neural Network Language Models

18 Aug 2017 Xie Chen Xunying Liu Anton Ragni Yu Wang Mark Gales

Recently, bidirectional recurrent network language models (bi-RNNLMs) have been shown to outperform standard, unidirectional, recurrent neural network language models (uni-RNNLMs) on a range of speech recognition tasks. This indicates that future word context information beyond the word history can be useful... (read more)

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