Sequence-to-Sequence (seq2seq) models have demonstrated excellent performance in several tasks including machine translation (Sutskever et al., 2014), summarization (Rush et al., 2015, dialogue generation (Serban et al., 2015), and image captioning (Xu et al., 2015).
the standard cross-entropy training procedure for these models suffers from the well-known problem of exposure bias: because cross-entropy training always uses gold contexts, the states and contexts encountered during training do not match those encountered at test time.