The goal of neural-symbolic computation is to integrate the connectionist and
symbolist paradigms. Prior methods learn the neural-symbolic models using
reinforcement learning (RL) approaches, which ignore the error propagation in
the symbolic reasoning module and thus converge slowly with sparse rewards. In
this paper, we address these issues and close the loop of neural-symbolic
learning by (1) introducing the \textbf{grammar} model as a \textit{symbolic
prior} to bridge neural perception and symbolic reasoning, and (2) proposing a
novel \textbf{back-search} algorithm which mimics the top-down human-like
learning procedure to propagate the error through the symbolic reasoning module
efficiently. We further interpret the proposed learning framework as maximum
likelihood estimation using Markov chain Monte Carlo sampling and the
back-search algorithm as a Metropolis-Hastings sampler. The experiments are
conducted on two weakly-supervised neural-symbolic tasks: (1) handwritten
formula recognition on the newly introduced HWF dataset; (2) visual question
answering on the CLEVR dataset. The results show that our approach
significantly outperforms the RL methods in terms of performance, converging
speed, and data efficiency. Our code and data are released at
https://liqing-ustc.github.io/NGS.

本文介绍了一种新的用于神经符号计算的学习框架，其中引入了语法模型和反向搜索算法来优化符号推理模块中的误差传播，本文将所提出的学习框架解释为最大似然估计，算法则解释为 Metropolis-Hastings 采样器，实验结果表明，本文方法相对于强化学习方法在性能、收敛速度和数据效率等方面均有显著提升。

通过集成神经感知、语法解析和符号推理实现闭环神经符号学习

Closed Loop Neural-Symbolic Learning via Integrating Neural Perception,  Grammar Parsing, and Symbolic Reasoning

We consider the problem of parsing natural language descriptions into source
code written in a general-purpose programming language like Python. Existing
data-driven methods treat this problem as a language generation task without
considering the underlying syntax of the target programming language. Informed
by previous work in semantic parsing, in this paper we propose a novel neural
architecture powered by a grammar model to explicitly capture the target syntax
as prior knowledge. Experiments find this an effective way to scale up to
generation of complex programs from natural language descriptions, achieving
state-of-the-art results that well outperform previous code generation and
semantic parsing approaches.

本文提出了一种新型神经体系结构，它由语法模型支持，旨在将自然语言描述解析为基于 Python 等通用编程语言的源代码，并将其显式地捕获为先验知识，以实现从自然语言描述生成复杂程序的规模化。实验证明，这是一种有效缩放到语言描述的复杂程序的方法，取得了优于以前代码生成和语义解析方法的最新成果。