People learn in fast and flexible ways that have not been emulated by
machines. Once a person learns a new verb "dax," he or she can effortlessly
understand how to "dax twice," "walk and dax," or "dax vigorously." There have
been striking recent improvements in machine learning for natural language
processing, yet the best algorithms require vast amounts of experience and
struggle to generalize new concepts in compositional ways. To better understand
these distinctively human abilities, we study the compositional skills of
people through language-like instruction learning tasks. Our results show that
people can learn and use novel functional concepts from very few examples
(few-shot learning), successfully applying familiar functions to novel inputs.
People can also compose concepts in complex ways that go beyond the provided
demonstrations. Two additional experiments examined the assumptions and
inductive biases that people make when solving these tasks, revealing three
biases: mutual exclusivity, one-to-one mappings, and iconic concatenation. We
discuss the implications for cognitive modeling and the potential for building
machines with more human-like language learning capabilities.

人类的组合能力和语言学习技能具有高度的可塑性，能够从很少的示例中学习和使用新的功能概念，可以以超出提供的演示方式进行组合。研究了人们解决任务时的假设和归纳偏差，揭示了三个偏差：互斥性、一对一映射和图标级联。该研究显示了机器学习与人类语言学习的巨大差异，并探讨了制定更加类似于人类的机器学习算法的潜在手段。

人类少样本学习组合指令

Human few-shot learning of compositional instructions

Humans can understand and produce new utterances effortlessly, thanks to
their compositional skills. Once a person learns the meaning of a new verb
"dax," he or she can immediately understand the meaning of "dax twice" or "sing
and dax." In this paper, we introduce the SCAN domain, consisting of a set of
simple compositional navigation commands paired with the corresponding action
sequences. We then test the zero-shot generalization capabilities of a variety
of recurrent neural networks (RNNs) trained on SCAN with sequence-to-sequence
methods. We find that RNNs can make successful zero-shot generalizations when
the differences between training and test commands are small, so that they can
apply "mix-and-match" strategies to solve the task. However, when
generalization requires systematic compositional skills (as in the "dax"
example above), RNNs fail spectacularly. We conclude with a proof-of-concept
experiment in neural machine translation, suggesting that lack of systematicity
might be partially responsible for neural networks' notorious training data
thirst.

该论文介绍了 SCAN 领域，并使用序列到序列的方法对各种复发神经网络进行零样本泛化能力的测试。研究发现，当训练和测试指令之间的差异很小时，RNN 可以成功进行零样本泛化，但是当泛化需要系统的组合能力时，RNN 则失败了。研究结论显示，缺乏系统性可能部分原因导致神经网络需要大量的训练数据。