Evaluating machine learning (ML) systems on their ability to learn known classifiers allows fine-grained examination of the patterns they can learn, which builds confidence when they are applied to the learning of unknown classifiers. This article presents a new benchmark for ML systems on sequence classification called MLRegTest, which contains training, development, and test sets from 1,800 regular languages. Different kinds of formal languages represent different kinds of long-distance dependencies, and correctly identifying long-distance dependencies in sequences is a known challenge for ML systems to generalize successfully. MLRegTest organizes its languages according to their logical complexity (monadic second order, first order, propositional, or monomial expressions) and the kind of logical literals (string, tier-string, subsequence, or combinations thereof). The logical complexity and choice of literal provides a systematic way to understand different kinds of long-distance dependencies in regular languages, and therefore to understand the capacities of different ML systems to learn such long-distance dependencies. Finally, the performance of different neural networks (simple RNN, LSTM, GRU, transformer) on MLRegTest is examined. The main conclusion is that their performance depends significantly on the kind of test set, the class of language, and the neural network architecture.

翻译：评估机器学习系统学习已知分类器的能力，可以细致考察其可习得的模式，从而增强将其应用于未知分类器学习时的信心。本文提出了一个新的序列分类机器学习系统基准测试——MLRegTest，该测试包含来自1800种正则语言的训练集、开发集和测试集。不同形式语言代表了不同类型的长距离依赖关系，而正确识别序列中的长距离依赖关系是机器学习系统成功泛化能力的已知挑战。MLRegTest根据其逻辑复杂度（一元二阶逻辑、一阶逻辑、命题逻辑或单项式表达式）以及逻辑文字类型（字符串、层次字符串、子序列或其组合）对语言进行组织。逻辑复杂度与文字类型的选择为理解正则语言中不同种类的长距离依赖关系提供了系统化方法，进而可理解不同机器学习系统学习此类长距离依赖关系的能力。最后，本文考察了不同神经网络（简单RNN、LSTM、GRU、Transformer）在MLRegTest上的性能表现。主要结论是：其性能显著取决于测试集类型、语言类别以及神经网络架构。