论文标题
用于组成教学的模块化网络以下
Modular Networks for Compositional Instruction Following
论文作者
论文摘要
指导中使用的标准体系结构经常在训练过程中观察到的子目标的新颖组成（例如，导航到地标或拾取物体）。我们提出了一个模块化体系结构，用于遵循描述各种子目标序列的自然语言指令。在我们的方法中，亚目标模块每个模块都针对特定的亚目标进行自然语言说明。通过学习分割指令并预测每个段的子观念类型来选择要执行的模块序列。与标准的非模块化序列到序列方法相比，基准后的一项具有挑战性的指示，我们发现模块化改善了对新型亚物质组成以及对训练中看不见的环境的概括。
Standard architectures used in instruction following often struggle on novel compositions of subgoals (e.g. navigating to landmarks or picking up objects) observed during training. We propose a modular architecture for following natural language instructions that describe sequences of diverse subgoals. In our approach, subgoal modules each carry out natural language instructions for a specific subgoal type. A sequence of modules to execute is chosen by learning to segment the instructions and predicting a subgoal type for each segment. When compared to standard, non-modular sequence-to-sequence approaches on ALFRED, a challenging instruction following benchmark, we find that modularization improves generalization to novel subgoal compositions, as well as to environments unseen in training.
