Meghana Gottapu
Author directory2026
Evaluating Automatically Inferred Linguistic Perspective as an Auxiliary Signal for Toxicity Detection
Meghana Gottapu
Proceedings of the 10th Workshop on Online Abuse and Harms (WOAH)
Meghana Gottapu
Proceedings of the 10th Workshop on Online Abuse and Harms (WOAH)
Identity-related language occurs in both self-referential discussion and speech aboiut other groups, complicating toxicity detection. We ask whether an automatically inferred linguistic-perspective signal is useful as an auxiliary traiing objective. A regex and zero-shot NLI pipeline lables comments as SELF or OTHER; these are noisy linguistic proxies, not verified speak indetities. On Jigsaw Unintended Bias, we fine-tune a Toxic-BERT model with toxicity and proxy labels heads and compare it with a matched control whose perspective-loss weight is zero. The auxiliary objective gives a small descriptive mean ROC-AUC increase but lower mean accuracy. On decontaminated CivilComments-WILDS, ROC-AUC is 0.9346 while worst group accuracy is 0.5805. The main contribution is a leakage-controlled, matced evaluation showing that the full fine-tuning recipe improves toxic class F1 and ranking substantially, where as the automatic perspective proxy provides only limited additional benefit.