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

Weakly-supervised Neural Semantic Parsing with a Generative Ranker

To learn a semantic parser from denotations, a learning algorithm must search
over a combinatorially large space of logical forms for ones consistent with
the annotated denotations. We propose a new online learning algorithm that
searches faster as training progresses. The two key ideas are using macro
grammars to cache the abstract patterns of useful logical forms found thus far,
and holistic triggering to efficiently retrieve the most relevant patterns
based on sentence similarity. On the WikiTableQuestions dataset, we first
expand the search space of an existing model to improve the state-of-the-art
accuracy from 38.7% to 42.7%, and then use macro grammars and holistic
triggering to achieve an 11x speedup and an accuracy of 43.7%.

该研究提出了一种新的在线学习算法，利用宏语法缓存已发现的有用逻辑形式的抽象模式，以及利用整体触发根据句子相似度高效检索相关模式，其在 WikiTableQuestions 数据集上达到 43.7% 的准确率和 11 倍的速度提升。