基于树形语义框架的神经语义解析方法

语义解析的目标是将自然语言表达映射为机器可理解的逻辑表达,该任务的关键挑战在于难以刻画自然语言中蕴含的组合语义。目前,结合深度神经网络模型的语义解析方法已经成为该领域的主流方法,该类方法通常采用编码器—解码器框架,通过设计树形结构的解码器或者在解码器中添加语法限制,从语法层面上提升逻辑表达生成的准确率。与现有的神经语义解析方法不同,该文从语义建模角度出发,以语义框架作为中间形式,通过自顶向下的生成方式,显式地建模自然语言表达中蕴含的层次化语义结构。模型先根据自然语言输入,自顶向下地生成语义框架,再将语义框架表示融入到逻辑表达的生成过程中。三个数据集上的实验结果表明,该文提出的模型能更准确地生成语义框架,并且在语义解析任务中取得更好的效果。

Learning Tree-structured Sketch for Neural Semantic Parsing

Semantic parsing aims at mapping natural language utterances into machine interpretable logical forms. Deep neural models like encoder-decoder models, without the need of extensive feature engineering, have been applied to semantic parsing task and obtained promising results. Existing deep models for semantic parsing typically capture the compositional semantics of logical forms in a syntactic way by designing tree-structured decoders or adding grammar constraints to decoders. In this paper, we propose to generate tree-structured sketches to better capture compositional semantics of logical forms. Our model first predict the sketches in a top-town fashion, and then incorporates the sketch to generate the logical forms. Experimental results on three datasets show that our model generates sketches more accurately and achieves better performance at semantic parsing task.