  We develop novel annotation guidelines for sentence-level subjectivity
detection, which are not limited to language-specific cues. We use our
guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective
sentences extracted from English news articles on controversial topics. Our
corpus paves the way for subjectivity detection in English and across other
languages without relying on language-specific tools, such as lexicons or
machine translation. We evaluate state-of-the-art multilingual
transformer-based models on the task in mono-, multi-, and cross-language
settings. For this purpose, we re-annotate an existing Italian corpus. We
observe that models trained in the multilingual setting achieve the best
performance on the task.
