Many efforts have been devoted to extracting constituency trees from
pre-trained language models, often proceeding in two stages: feature definition
and parsing. However, this kind of methods may suffer from the branching bias
issue, which will inflate the performances on languages with the same branch it
biases to. In this work, we propose quantitatively measuring the branching bias
by comparing the performance gap on a language and its reversed language, which
is agnostic to both language models and extracting methods. Furthermore, we
analyze the impacts of three factors on the branching bias, namely parsing
algorithms, feature definitions, and language models. Experiments show that
several existing works exhibit branching biases, and some implementations of
these three factors can introduce the branching bias.

该研究提出了一种通过比较语言和反向语言的性能差距来量化分支偏差的方法，并分析了分析算法、特征定义和语言模型对分支偏差的影响。实验结果表明，存在一些现有作品存在分支偏差，并且这三个因素的一些实现可能会引入分支偏差。

基于预训练语言模型的句法分支偏差探究

On the Branching Bias of Syntax Extracted from Pre-trained Language  Models

Head-driven phrase structure grammar (HPSG) enjoys a uniform formalism
representing rich contextual syntactic and even semantic meanings. This paper
makes the first attempt to formulate a simplified HPSG by integrating
constituent and dependency formal representations into head-driven phrase
structure. Then two parsing algorithms are respectively proposed for two
converted tree representations, division span and joint span. As HPSG encodes
both constituent and dependency structure information, the proposed HPSG
parsers may be regarded as a sort of joint decoder for both types of structures
and thus are evaluated in terms of extracted or converted constituent and
dependency parsing trees. Our parser achieves new state-of-the-art performance
for both parsing tasks on Penn Treebank (PTB) and Chinese Penn Treebank,
verifying the effectiveness of joint learning constituent and dependency
structures. In details, we report 96.33 F1 of constituent parsing and 97.20\%
UAS of dependency parsing on PTB.

该论文尝试将组成和依存句法两种形式融合到头驱动短语结构中，提出了两种基于所转换树的解析算法，成功地在 Penn Treebank（PTB）和中文 Penn Treebank 上实现了一种新型联合解码器，达到了本领域最高的解析性能。

Penn Treebank 上的基于头驱动短语结构语法分析

Head-Driven Phrase Structure Grammar Parsing on Penn Treebank

We present a system for generating parsers based directly on the metaphor of
parsing as deduction. Parsing algorithms can be represented directly as
deduction systems, and a single deduction engine can interpret such deduction
systems so as to implement the corresponding parser. The method generalizes
easily to parsers for augmented phrase structure formalisms, such as
definite-clause grammars and other logic grammar formalisms, and has been used
for rapid prototyping of parsing algorithms for a variety of formalisms
including variants of tree-adjoining grammars, categorial grammars, and
lexicalized context-free grammars.

使用演绎公式来表示解析算法实现，并使用单一演绎引擎解析系统实现相应解析器的方法可以广泛适用于各种扩充短语结构形式，例如确定性子句文法和其他逻辑文法形式，并已用于对各种形式文法进行快速原型制作。