We investigate the integration of a planning mechanism into sequence-to-sequence models using attention. We develop a model which can plan ahead in the future when it computes its alignments between input and output sequences, constructing a matrix of proposed future alignments and a commitment vector that governs whether to follow or recompute the plan. This mechanism is inspired by the recently proposed strategic attentive reader and writer (STRAW) model for Reinforcement Learning. Our proposed model is end-to-end trainable using primarily differentiable operations. We show that it outperforms a strong baseline on character-level translation tasks from WMT'15, the algorithmic task of finding Eulerian circuits of graphs, and question generation from the text. Our analysis demonstrates that the model computes qualitatively intuitive alignments, converges faster than the baselines, and achieves superior performance with fewer parameters.

该研究研究如何将规划机制集成到序列到序列模型中，使用注意机制计算输入和输出序列之间的对齐来构建未来计划矩阵和承诺向量，提出的方法基于强化学习中的STRAW模型，该模型可以使用可微分运算进行端到端训练，且在字符级翻译、寻找Eulerian环路的算法任务和从文本生成问题等任务上的性能比强基线模型更好。

计划、关注、生成：序列到序列模型的规划