The problem of spurious programs is a longstanding challenge when training a
semantic parser from weak supervision. To eliminate such programs that have
wrong semantics but correct denotation, existing methods focus on exploiting
similarities between examples based on domain-specific knowledge. In this
paper, we propose a domain-agnostic filtering mechanism based on program
execution results. Specifically, for each program obtained through the search
process, we first construct a representation that captures the program's
semantics as execution results under various inputs. Then, we run a majority
vote on these representations to identify and filter out programs with
significantly different semantics from the other programs. In particular, our
method is orthogonal to the program search process so that it can easily
augment any of the existing weakly supervised semantic parsing frameworks.
Empirical evaluations on the Natural Language Visual Reasoning and
WikiTableQuestions demonstrate that applying our method to the existing
semantic parsers induces significantly improved performances.

通过基于程序执行结果的领域无关过滤机制，我们的研究提出在弱监督语义分析框架中应用该方法可以显著提高性能。

基于执行的伪程序过滤的弱监督语义解析

Weakly Supervised Semantic Parsing with Execution-based Spurious Program  Filtering

The predominant challenge in weakly supervised semantic parsing is that of
spurious programs that evaluate to correct answers for the wrong reasons. Prior
work uses elaborate search strategies to mitigate the prevalence of spurious
programs; however, they typically consider only one input at a time. In this
work we explore the use of consistency between the output programs for related
inputs to reduce the impact of spurious programs. We bias the program search
(and thus the model's training signal) towards programs that map the same
phrase in related inputs to the same sub-parts in their respective programs.
Additionally, we study the importance of designing logical formalisms that
facilitate this kind of consAistency-based training. We find that a more
consistent formalism leads to improved model performance even without
consistency-based training. When combined together, these two insights lead to
a 10% absolute improvement over the best prior result on the Natural Language
Visual Reasoning dataset.

本文主要探讨利用相关输入输出程序的一致性来减少错误原因正确答案的虚假程序对弱监督语义解析的影响，通过通过自然语言视觉推理数据集从而获得优秀的模型表现的。