We propose a novel graph-based approach for semantic parsing that resolves two problems observed in the literature: (1) seq2seq models fail on compositional generalization tasks; (2) previous work using phrase structure parsers cannot cover all the semantic parses observed in treebanks. We prove that both MAP inference and latent tag anchoring (required for weakly-supervised learning) are NP-hard problems. We propose two optimization algorithms based on constraint smoothing and conditional gradient to approximately solve these inference problems. Experimentally, our approach delivers state-of-the-art results on Geoquery, Scan and Clevr, both for i.i.d. splits and for splits that test for compositional generalization.

翻译：我们提出了一种新颖的基于图的语义解析方法，解决了文献中观察到的两个问题：(1) seq2seq模型在组合泛化任务上表现不佳；(2) 先前使用短语结构解析器的工作无法覆盖树库中观察到的所有语义解析结果。我们证明了MAP推理和潜在标签锚定（弱监督学习所需）均为NP难问题。我们提出了两种基于约束平滑和条件梯度的优化算法来近似求解这些推理问题。实验结果表明，我们的方法在Geoquery、Scan和Clevr数据集上（无论是独立同分布划分还是测试组合泛化能力的划分）均达到了最先进的性能。