Semi-supervised Sequence Learning

NeurIPS 2015 Andrew M. DaiQuoc V. Le

We present two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a conventional language model in natural language processing... (read more)

PDF Abstract


Evaluation results from the paper

  Submit results from this paper to get state-of-the-art GitHub badges and help community compare results to other papers.