论文标题
关于从预训练的语言模型中提取的语法的分支偏见
On the Branching Bias of Syntax Extracted from Pre-trained Language Models
论文作者
论文摘要
许多努力都致力于从预先训练的语言模型中提取选区树，通常在两个阶段进行：特征定义和解析。但是，这种方法可能会遭受分支偏置问题的困扰，这将使与其偏见相同分支的语言上的表现膨胀。在这项工作中，我们提出了通过比较语言及其反向语言的性能差距来定量测量分支偏见的，这对语言模型和提取方法都是不可知的。此外，我们分析了三个因素对分支偏见的影响，即解析算法，特征定义和语言模型。实验表明，现有的几项作品表现出分支偏见，这三个因素的某些实现会引入分支偏见。
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.
