Argument Unit Recognition and Classification aims at identifying argument
units from text and classifying them as pro or against. One of the design
choices that need to be made when developing systems for this task is what the
unit of classification should be: segments of tokens or full sentences.
Previous research suggests that fine-tuning language models on the token-level
yields more robust results for classifying sentences compared to training on
sentences directly. We reproduce the study that originally made this claim and
further investigate what exactly token-based systems learned better compared to
sentence-based ones. We develop systematic tests for analysing the behavioural
differences between the token-based and the sentence-based system. Our results
show that token-based models are generally more robust than sentence-based
models both on manually perturbed examples and on specific subpopulations of
the data.

本文旨在识别和分类文本中的论证单位并将其归类为支持或反对。研究表明，基于 token 的模型可以比基于句子的模型更好地分类句子，所以本文复制并进一步探究了这一结论及其原因，并进行了系统测试以分析使用这两种模型的行为差异，结果表明使用基于 token 的模型通常比基于句子的模型更加健壮。