SLOG: A Structural Generalization Benchmark for Semantic Parsing
Bingzhi Li Lucia Donatelli Alexander Koller Tal Linzen Yuekun Yao Najoung Kim
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.