Analysis methods which enable us to better understand the representations and
functioning of neural models of language are increasingly needed as deep
learning becomes the dominant approach in NLP. Here we present two methods
based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which
allow us to directly quantify how strongly the information encoded in neural
activation patterns corresponds to information represented by symbolic
structures such as syntax trees. We first validate our methods on the case of a
simple synthetic language for arithmetic expressions with clearly defined
syntax and semantics, and show that they exhibit the expected pattern of
results. We then apply our methods to correlate neural representations of
English sentences with their constituency parse trees.

本文介绍了它们称为 RSA 和 TK 的两种基于表示相似性分析和树核的分析方法，这些方法可以直接量化神经激活模式编码的信息与句法树等符号结构所表示信息的相对强度，验证了这些方法对于具有明确定义语法和语义的简单合成语言的情况，然后将这些方法应用于将英语句子的神经表示与其从属树相关联。

语言的神经和符号表示的相关性

Correlating neural and symbolic representations of language

We present new data and semantic parsing methods for the problem of mapping
English sentences to Bash commands (NL2Bash). Our long-term goal is to enable
any user to perform operations such as file manipulation, search, and
application-specific scripting by simply stating their goals in English. We
take a first step in this domain, by providing a new dataset of challenging but
commonly used Bash commands and expert-written English descriptions, along with
baseline methods to establish performance levels on this task.

本论文提出了一种新的数据集以及语义解析方法，用于将英文句子映射为 Bash 命令，并提供了基准方法来评估此任务的性能水平。该研究的长期目标是使任何用户都能够通过简单地用英语陈述其目标来执行文件操作、搜索和特定应用程序脚本的操作。