Will Annotators Disagree? Identifying Subjectivity in Value-Laden Arguments
This work addresses the challenge of handling subjectivity in annotation tasks for natural language processing, but it is incremental as it builds on existing methods for value prediction and subjectivity identification.
The paper tackled the problem of identifying subjectivity in value-laden arguments to avoid hiding annotator disagreement, and found that direct subjectivity identification significantly improves model performance in flagging subjective arguments.
Aggregating multiple annotations into a single ground truth label may hide valuable insights into annotator disagreement, particularly in tasks where subjectivity plays a crucial role. In this work, we explore methods for identifying subjectivity in recognizing the human values that motivate arguments. We evaluate two main approaches: inferring subjectivity through value prediction vs. directly identifying subjectivity. Our experiments show that direct subjectivity identification significantly improves the model performance of flagging subjective arguments. Furthermore, combining contrastive loss with binary cross-entropy loss does not improve performance but reduces the dependency on per-label subjectivity. Our proposed methods can help identify arguments that individuals may interpret differently, fostering a more nuanced annotation process.