一句话：研究符号域中小样本学习的 DSL 合成问题，证明其可判定性并给出正则树表征。
本文研究符号域中面向小样本学习的领域特定语言（DSL）合成问题。给定一个基语言以及若干小样本学习实例，每个实例划分为训练与测试样本，目标是合成该基语言上的一个文法，使得能解决训练样本的小表达式也能解决对应的测试样本。作者将表达式大小与文法分析树深度对应，并限制语义可在固定结构上由树自动机求值，证明了该问题对此类语言可判定，且解文法构成正则树集。论文还讨论了两种变体：DSL 只需能表达输入学习问题的解，以及 DSL 用宏文法定义，并分别给出可判定性结果。评估以理论证明为主，摘要未报告实现或实验。
We study the problem of synthesizing domain-specific languages (DSLs) for few-shot learning in symbolic domains. Given a base language and instances of few-shot learning problems, where each instance is split into training and testing samples, the DSL synthesis problem asks for a grammar over the base language that guarantees that small expressions solving training samples also solve corresponding testing samples. We prove that the problem is decidable for a class of languages whose semantics over fixed structures can be evaluated by tree automata and when expression size corresponds to parse tree depth in the grammar, and, furthermore, the grammars solving the problem correspond to a regular set of trees. We also prove decidability results for variants of the problem where DSLs are only required to express solutions for input learning problems and where DSLs are defined using macro grammars.