When learning a language, people can quickly expand their understanding of the unknown content by using compositional skills, such as from two words "go" and "fast" to a new phrase "go fast." In recent work of Lake and Baroni (2017), modern Sequence-to-Sequence(seq2seq) Recurrent Neural Networks (RNNs) can make powerful zero-shot generalizations in specifically controlled experiments. However, there is a missing regarding the property of such strong generalization and its precise requirements. This paper explores this positive result in detail and defines this pattern as the synonymous generalization, an ability to recognize an unknown sequence by decomposing the difference between it and a known sequence as corresponding existing synonyms. To better investigate it, I introduce a new environment called Colorful Extended Cleanup World (CECW), which consists of complex commands paired with logical expressions. While demonstrating that sequential RNNs can perform synonymous generalizations on foreign commands, I conclude their prerequisites for success. I also propose a data augmentation method, which is successfully verified on the Geoquery (GEO) dataset, as a novel application of synonymous generalization for real cases.

中文翻译：

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序列到序列循环网络中的同义泛化

人们在学习一门语言时，可以通过使用组合技巧，快速扩展对未知内容的理解，比如从“go”和“fast”两个词到一个新词“go fast”。在 Lake 和 Baroni (2017) 最近的工作中，现代序列到序列 (seq2seq) 循环神经网络 (RNN) 可以在特定控制的实验中进行强大的零样本泛化。然而，这种强泛化的特性及其精确要求存在缺失。本文详细探讨了这个积极的结果，并将这种模式定义为同义泛化，一种通过将未知序列与已知序列之间的差异分解为对应的现有同义词来识别未知序列的能力。为了更好地调查它，我介绍了一个称为多彩扩展清理世界 (CECW) 的新环境，它由复杂的命令和逻辑表达式组成。在证明顺序 RNN 可以对外部命令执行同义概括的同时，我总结了它们成功的先决条件。我还提出了一种数据增强方法，该方法在 Geoquery (GEO) 数据集上得到了成功验证，作为对实际案例的同义泛化的新应用。