Weakly-supervised semantic parsers are trained on utterance-denotation pairs, treating logical forms as latent. The task is challenging due to the large search space and spuriousness of logical forms. In this paper we introduce a neural parser-ranker system for weakly-supervised semantic parsing. The parser generates candidate tree-structured logical forms from utterances using clues of denotations. These candidates are then ranked based on two criterion: their likelihood of executing to the correct denotation, and their agreement with the utterance semantics. We present a scheduled training procedure to balance the contribution of the two objectives. Furthermore, we propose to use a neurally encoded lexicon to inject prior domain knowledge to the model. Experiments on three Freebase datasets demonstrate the effectiveness of our semantic parser, achieving results within the state-of-the-art range.

本文提出了一种基于神经网络的解析器-排序器系统用于弱监督语义解析, 通过对表征的推理公式使用线索来生成候选的树结构逻辑形式, 并对其进行排序, 以此平衡正确执行和掌握语义的两个目标, 进一步使用神经编码的词表注入领域知识, 并在三个Freebase数据集上进行实验, 获得了达到最先进水平的效果.

基于生成排名模型的弱监督神经语义解析