  Current methods of toxic language detection (TLD) typically rely on specific
tokens to conduct decisions, which makes them suffer from lexical bias, leading
to inferior performance and generalization. Lexical bias has both "useful" and
"misleading" impacts on understanding toxicity. Unfortunately, instead of
distinguishing between these impacts, current debiasing methods typically
eliminate them indiscriminately, resulting in a degradation in the detection
accuracy of the model. To this end, we propose a Counterfactual Causal
Debiasing Framework (CCDF) to mitigate lexical bias in TLD. It preserves the
"useful impact" of lexical bias and eliminates the "misleading impact".
Specifically, we first represent the total effect of the original sentence and
biased tokens on decisions from a causal view. We then conduct counterfactual
inference to exclude the direct causal effect of lexical bias from the total
effect. Empirical evaluations demonstrate that the debiased TLD model
incorporating CCDF achieves state-of-the-art performance in both accuracy and
fairness compared to competitive baselines applied on several vanilla models.
The generalization capability of our model outperforms current debiased models
for out-of-distribution data.
