Most structure inference methods either rely on exhaustive search or are
purely data-driven. Exhaustive search robustly infers the structure of
arbitrarily complex data, but it is slow. Data-driven methods allow efficient
inference, but do not generalize when test data have more complex structures
than training data. In this paper, we propose a hybrid inference algorithm, the
Neurally-Guided Structure Inference (NG-SI), keeping the advantages of both
search-based and data-driven methods. The key idea of NG-SI is to use a neural
network to guide the hierarchical, layer-wise search over the compositional
space of structures. We evaluate our algorithm on two representative structure
inference tasks: probabilistic matrix decomposition and symbolic program
parsing. It outperforms data-driven and search-based alternatives on both
tasks.

本文提出了一个混合推理算法 ——Neurally-Guided Structure Inference (NG-SI)，它将基于搜索的和基于数据的方法的优点相结合，通过使用神经网络指导结构的分层搜索，提高了结构推断的效率。在两个典型的结构推断任务中，概率矩阵分解和符号程序解析中，NG-SI 的表现优于基于数据驱动和具有搜索性质的替代方案。

神经引导的结构推断

Neurally-Guided Structure Inference

We consider the task of mapping pseudocode to long programs that are
functionally correct. Given test cases as a mechanism to validate programs, we
search over the space of possible translations of the pseudocode to find a
program that passes the validation. However, without proper credit assignment
to localize the sources of program failures, it is difficult to guide search
toward more promising programs. We propose to perform credit assignment based
on signals from compilation errors, which constitute 88.7% of program failures.
Concretely, we treat the translation of each pseudocode line as a discrete
portion of the program, and whenever a synthesized program fails to compile, an
error localization method tries to identify the portion of the program
responsible for the failure. We then focus search over alternative translations
of the pseudocode for those portions. For evaluation, we collected the SPoC
dataset (Search-based Pseudocode to Code) containing 18,356 programs with
human-authored pseudocode and test cases. Under a budget of 100 program
compilations, performing search improves the synthesis success rate over using
the top-one translation of the pseudocode from 25.6% to 44.7%.

使用编译错误信号进行归因分配的伪代码到程序的综合。