The goal of compositional generalization benchmarks is to evaluate how well models generalize to new complex linguistic expressions. Existing benchmarks often focus on lexical generalization, the interpretation of novel lexical items in syntactic structures familiar from training; structural generalization tasks, where a model needs to interpret syntactic structures that are themselves unfamiliar from training, are often underrepresented, resulting in overly optimistic perceptions of how well models can generalize. We introduce SLOG, a semantic parsing dataset that extends COGS (Kim and Linzen, 2020) with 17 structural generalization cases. In our experiments, the generalization accuracy of Transformer models, including pretrained ones, only reaches 40.6%, while a structure-aware parser only achieves 70.8%. These results are far from the near-perfect accuracy existing models achieve on COGS, demonstrating the role of SLOG in foregrounding the large discrepancy between models' lexical and structural generalization capacities.

翻译：组合泛化基准的目标是评估模型对新复杂语言表达的泛化能力。现有基准通常侧重于词汇泛化，即模型对训练中已出现句法结构中的新词汇项进行解释的能力；而结构泛化任务——要求模型解释训练中未见过的句法结构——往往代表性不足，导致对模型泛化能力的评估过于乐观。我们提出了SLOG，这是一个语义解析数据集，通过增加17种结构泛化案例对COGS（Kim和Linzen，2020）进行了扩展。在我们的实验中，包括预训练模型在内的Transformer模型的泛化准确率仅为40.6%，而结构感知解析器也仅达到70.8%。这些结果远低于现有模型在COGS上近乎完美的准确率，凸显了SLOG在揭示模型词汇泛化能力与结构泛化能力之间存在巨大差异方面的关键作用。