You Are What You Annotate: Towards Better Models through Annotator Representations
Naihao Deng, Xinliang Zhang, Siyang Liu, Winston M.C. Wu, Lu Wang, Rada F. Mihalcea · 2023
Annotator disagreement is ubiquitous in natural language processing (NLP) tasks.There are multiple reasons for such disagreements, including the subjectivity of the task, difficult cases, unclear guidelines, and so on.Rather than simply aggregating labels to obtain data annotations, we instead try to directly model the diverse perspectives of the annotators, and explicitly account for annotators' idiosyncrasies in the modeling process by creating representations for each annotator (annotator embeddings) and also their annotations (annotation embeddings).In addition, we propose TID-8, The Inherent Disagreement -8 dataset, a benchmark that consists of eight existing language understanding datasets that have inherent annotator disagreement.We test our approach on TID-8 and show that our approach helps models learn significantly better from disagreements on six different datasets in TID-8 while increasing model size by fewer than 1% parameters.By capturing the unique tendencies and subjectivity of individual annotators through embeddings, our representations prime AI models to be inclusive of diverse viewpoints.• We propose TID-8, The Iherent Disagreement -8 dataset, a benchmark that consists of eight existing language understanding datasets that have inherent annotator disagreements.• We propose weighted annotator and annotation embeddings, which are model-agnostic and improve model performances on six out of the eight datasets in TID-8.• We conduct a detailed analysis on the performance variations of our methods and how our methods can be potentially grounded to realworld demographic features.