论文标题
lagr：标签对齐图，以在语义解析中更好地系统概括
LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing
论文作者
论文摘要
语义解析是为自然语言句子生成结构化含义表示的任务。最近的研究指出，常用的序列到序列（SEQ2SEQ）语义解析器努力系统地概括，即处理需要在新颖环境中重新组合已知知识的示例。在这项工作中，我们表明可以通过直接作为图形而不是作为序列产生含义表示来实现更好的系统概括。为此，我们提出了lagr（标签对准图），这是一个通用框架，可以通过独立预测完整多层输入对准图的节点和边缘标签来产生语义解析。强烈监督的劳格算法需要对齐图作为输入，而弱监督的劳动量侵入最初未对准的目标图的比对，则使用近似的最大A-Posteriori推断。实验表明，在强烈和弱监督的环境中，劳格在基线SEQ2SEQ解析器上的系统概括取得了重大改善。
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.
