  In NLP annotation, it is common to have multiple annotators label the text
and then obtain the ground truth labels based on the agreement of major
annotators. However, annotators are individuals with different backgrounds, and
minors' opinions should not be simply ignored. As annotation tasks become
subjective and topics are controversial in modern NLP tasks, we need NLP
systems that can represent people's diverse voices on subjective matters and
predict the level of diversity. This paper examines whether the text of the
task and annotators' demographic background information can be used to estimate
the level of disagreement among annotators. Particularly, we extract
disagreement labels from the annotators' voting histories in the five
subjective datasets, and then fine-tune language models to predict annotators'
disagreement. Our results show that knowing annotators' demographic
information, like gender, ethnicity, and education level, helps predict
disagreements. In order to distinguish the disagreement from the inherent
controversy from text content and the disagreement in the annotators' different
perspectives, we simulate everyone's voices with different combinations of
annotators' artificial demographics and examine its variance of the finetuned
disagreement predictor. Our paper aims to improve the annotation process for
more efficient and inclusive NLP systems through a novel disagreement
prediction mechanism. Our code and dataset are publicly available.
