Nearly all general-purpose neural semantic parsers generate logical forms in a strictly top-down autoregressive fashion. Though such systems have achieved impressive results across a variety of datasets and domains, recent works have called into question whether they are ultimately limited in their ability to compositionally generalize. In this work, we approach semantic parsing from, quite literally, the opposite direction; that is, we introduce a neural semantic parsing generation method that constructs logical forms from the bottom up, beginning from the logical form's leaves. The system we introduce is lazy in that it incrementally builds up a set of potential semantic parses, but only expands and processes the most promising candidate parses at each generation step. Such a parsimonious expansion scheme allows the system to maintain an arbitrarily large set of parse hypotheses that are never realized and thus incur minimal computational overhead. We evaluate our approach on compositional generalization; specifically, on the challenging CFQ dataset and three Text-to-SQL datasets where we show that our novel, bottom-up semantic parsing technique outperforms general-purpose semantic parsers while also being competitive with comparable neural parsers that have been designed for each task.

翻译：几乎所有通用型神经语义解析器都采用严格的自上而下自回归方式生成逻辑形式。尽管这类系统在多种数据集和领域取得了显著成果，但近期研究对其组合泛化能力是否本质上存在局限性提出了质疑。在本工作中，我们反其道而行之——换言之，我们提出了一种自底向上构建逻辑形式的神经语义解析生成方法，从逻辑形式的叶节点开始逐步构建。我们引入的系统具有"惰性"特征：它增量式地构建一组潜在语义解析结果，但每一步生成仅扩展和处理最有前景的候选解析方案。这种节俭的扩展策略使系统能够维持一个理论上无限大的解析假设集合，而这些假设永远不会被实际实例化，因此仅产生极小的计算开销。我们在组合泛化任务上评估了该方法，具体在具有挑战性的CFQ数据集和三个Text-to-SQL数据集上表明：我们新颖的自底向上语义解析技术不仅优于通用型语义解析器，还能与针对各任务设计的同类神经解析器相抗衡。