International Workshop on Spoken Language Translation (IWSLT) 2012

Hong Kong
December 6-7, 2012

Factored Recurrent Neural Network Language Model in TED Lecture Transcription

Youzheng Wu, Hitoshi Yamamoto, Xugang Lu, Shigeki Matsuda, Chiori Hori, Hideki Kashioka

Spoken Language Communication Laboratory, National Institute of Information and Communications Technology, Kyoto, Japan

In this study, we extend recurrent neural network-based language models (RNNLMs) by explicitly integrating morphological and syntactic factors (or features). Our proposed RNNLM is called a factored RNNLM that is expected to enhance RNNLMs. A number of experiments are carried out on top of state-of-the-art LVCSR system that show the factored RNNLM improves the performance measured by perplexity and word error rate. In the IWSLT TED test data sets, absolute word error rate reductions over RNNLM and n-gram LM are 0.4~0.8 points.

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Bibliographic reference.  Wu, Youzheng / Yamamoto, Hitoshi / Lu, Xugang / Matsuda, Shigeki / Hori, Chiori / Kashioka, Hideki (2012): "Factored recurrent neural network language model in TED lecture transcription", In IWSLT-2012, 222-228.