Recurrent Neural Networks can be trained to produce sequences of tokens given
some input, as exemplified by recent results in machine translation and image
captioning. The current approach to training them consists of maximizing the
likelihood of each token in the sequence given the current (recurrent) state
and the previous token. At inference, the unknown previous token is then
replaced by a token generated by the model itself. This discrepancy between
training and inference can yield errors that can accumulate quickly along the
generated sequence. We propose a curriculum learning strategy to gently change
the training process from a fully guided scheme using the true previous token,
towards a less guided scheme which mostly uses the generated token instead.
Experiments on several sequence prediction tasks show that this approach yields
significant improvements. Moreover, it was used successfully in our winning
entry to the MSCOCO image captioning challenge, 2015.

本文提出课程学习策略，用于训练循环神经网络，以尽量减小实际工作场景中训练和推理之间的差异，如在 MSCOCO 图片注释挑战赛中实测有效。