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Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language model. In this work, we present the Cold Fusion method, which leverages a pre-trained language model during training, and show its effectiveness on the speech recognition task. We show that Seq2Seqdoi:10.21437/interspeech.2018-1392 dblp:conf/interspeech/SriramJSC18 fatcat:ts3vdbyvcvdtrpihjwdk6g7mfy