Semantic parsing is the task of producing structured meaning representations for natural language sentences. Recent research has pointed out that the commonly-used sequence-to-sequence (seq2seq) semantic parsers struggle to generalize systematically, i.e. to handle examples that require recombining known knowledge in novel settings. In this work, we show that better systematic generalization can be achieved by producing the meaning representation directly as a graph and not as a sequence. To this end we propose LAGr (Label Aligned Graphs), a general framework to produce semantic parses by independently predicting node and edge labels for a complete multi-layer input-aligned graph. The strongly-supervised LAGr algorithm requires aligned graphs as inputs, whereas weakly-supervised LAGr infers alignments for originally unaligned target graphs using approximate maximum-a-posteriori inference. Experiments demonstrate that LAGr achieves significant improvements in systematic generalization upon the baseline seq2seq parsers in both strongly- and weakly-supervised settings.

翻译：语义分析是生成自然语言句子的结构化含义表示的任务。 最近的研究表明, 常用的序列到序列( seq2seq) 语义分析器很难系统化地普及, 也就是说, 处理需要重新组合已知新环境知识的示例。 在这项工作中, 我们显示, 可以通过直接以图表而不是顺序生成含义来实现更系统化的概括化。 为此, 我们提议LAGr (Label Association 图形), 这是一种通过独立预测多层输入校正图形的节点和边缘标签来生成语义拼法的一般框架。 强受严格监督的LAGr 算法要求将图形作为输入, 而弱受监督的LAGr 推导出原始不对齐的目标图形的对齐, 使用近似最高值的外缘性推断值。 实验表明, LAGger 在强和弱受监督的环境下, 都实现了对基线后项定量的系统化概括化。