This paper examines the characterization and learning of grammars defined
with enriched representational models. Model-theoretic approaches to formal
language theory traditionally assume that each position in a string belongs to
exactly one unary relation. We consider unconventional string models where
positions can have multiple, shared properties, which are arguably useful in
many applications. We show the structures given by these models are partially
ordered, and present a learning algorithm that exploits this ordering relation
to effectively prune the hypothesis space. We prove this learning algorithm,
which takes positive examples as input, finds the most general grammar which
covers the data.

这篇论文研究了使用丰富的表示模型定义语法的表征和学习。我们考虑到一些不常见的字符串模型，其中位置可以具有多个共享属性，并且提出了一种基于部分排序结构的学习算法，利用这种关系来有效地修剪假设空间，该算法找到最一般的文法覆盖数据。

利用部分有序表示进行学习

Learning with Partially Ordered Representations

A number of differences have emerged between modern and classic approaches to
constituency parsing in recent years, with structural components like grammars
and feature-rich lexicons becoming less central while recurrent neural network
representations rise in popularity. The goal of this work is to analyze the
extent to which information provided directly by the model structure in
classical systems is still being captured by neural methods. To this end, we
propose a high-performance neural model (92.08 F1 on PTB) that is
representative of recent work and perform a series of investigative
experiments. We find that our model implicitly learns to encode much of the
same information that was explicitly provided by grammars and lexicons in the
past, indicating that this scaffolding can largely be subsumed by powerful
general-purpose neural machinery.

研究了现代和经典的组成分析方法的差异，以及神经网络在此领域的应用。高性能的神经模型代表了最新的工作，实验发现该模型隐式编码了过去语法和词汇表明确提供的信息，暗示这种支撑可以被强大的通用神经机制取代。