  The ubiquity of offensive and hateful content on online fora necessitates the
need for automatic solutions that detect such content competently across target
groups. In this paper we show that text classification models trained on large
publicly available datasets despite having a high overall performance, may
significantly under-perform on several protected groups. On the
\citet{vidgen2020learning} dataset, we find the accuracy to be 37\% lower on an
under annotated Black Women target group and 12\% lower on Immigrants, where
hate speech involves a distinct style. To address this, we propose to perform
token-level hate sense disambiguation, and utilize tokens' hate sense
representations for detection, modeling more general signals. On two publicly
available datasets, we observe that the variance in model accuracy across
target groups drops by at least 30\%, improving the average target group
performance by 4\% and worst case performance by 13\%.
