We present a novel corpus for subjectivity detection at the sentence level.
We develop new annotation guidelines for the task, which are not limited to
language-specific cues, and apply them to produce a new corpus in English. The
corpus consists of 411 subjective and 638 objective sentences extracted from
ongoing coverage of political affairs from online news outlets. This new
resource paves the way for the development of models for subjectivity detection
in English and across other languages, without relying on language-specific
tools like lexicons or machine translation. We evaluate state-of-the-art
multilingual transformer-based models on the task, both in mono- and
cross-lingual settings, the latter with a similar existing corpus in Italian
language. We observe that enriching our corpus with resources in other
languages improves the results on the task.

该研究介绍了一个用于句子级别主体性检测的新语料库，其中包括英语政治事务的主观句子和客观句子，同时开发了新的标注指南和使用最新的多语言转换模型，使得该语料库能够用于英语和其他语言的主体性检测，并在其他语言丰富资源的情况下，提高了该任务的结果。