Identifying human values behind arguments isa complex task which requires understandingof premise, stance and conclusion together. Wepropose a method that uses a pre-trained lan-guage model, DeBERTa, to tokenize and con-catenate the text before feeding it into a fullyconnected neural network. We also show thatleveraging the hierarchy in values improves theperformance by .14 F1 score.

This paper describes our system used in the SemEval-2023 Task 11 Learning With Disagreements (Le-Wi-Di). This is a subjective task since it deals with detecting hate speech, misogyny and offensive language. Thus, disagreement among annotators is expected. We experiment with different settings like loss functions specific for subjective tasks and include anonymized annotator-specific information to help us understand the level of disagreement. We perform an in-depth analysis of the performance discrepancy of these different modelling choices. Our system achieves a cross-entropy of 0.58, 4.01 and 3.70 on the test sets of HS-Brexit, ArMIS and MD-Agreement, respectively. Our code implementation is publicly available.