支架网络：通过提问的增量学习和教学
Scaffolding Networks: Incremental Learning and Teaching Through Questioning
摘要：
我们引入了一种新的学习范式，用于推理、理解和预测，以及实现这种范式的脚手架网络。脚手架网络体现了一种增量学习方法，该方法被制定为教师-学生网络架构，以教机器如何理解文本和进行推理。我们的计算支架方法的关键是教师和学生之间的互动，通过顺序提问。学生逐渐观察文本中的每个句子，它使用基于注意力的神经网络来发现和记录与其当前记忆相关的关键信息。与此同时，老师会对观察到的文本提出问题，学生网络通过正确回答这些问题获得奖励。整个网络使用强化学习不断更新。我们在合成数据集和真实数据集上的实验结果表明，脚手架网络不仅优于最先进的方法，而且即使在很少的人工输入的情况下，也能以可扩展的方式进行推理。
英文摘要：
We introduce a new paradigm of learning for reasoning, understanding, and prediction, as well as the scaffolding network to implement this paradigm. The scaffolding network embodies an incremental learning approach that is formulated as a teacher-student network architecture to teach machines how to understand text and do reasoning. The key to our computational scaffolding approach is the interactions between the teacher and the student through sequential questioning. The student observes each sentence in the text incrementally, and it uses an attention-based neural net to discover and register the key information in relation to its current memory. Meanwhile, the teacher asks questions about the observed text, and the student network gets rewarded by correctly answering these questions. The entire network is updated continually using reinforcement learning. Our experimental results on synthetic and real datasets show that the scaffolding network not only outperforms state-of-the-art methods but also learns to do reasoning in a scalable way even with little human generated input.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
