Don't be a Fool: Pooling Strategies in Offensive Language Detection from
  User-Intended Adversarial Attacks
Seunguk Yu Juhwan Choi Youngbin Kim
Offensive language detection is an important task for filtering out abusive
expressions and improving online user experiences. However, malicious users
often attempt to avoid filtering systems through the involvement of textual
noises. In this paper, we propose these evasions as user-intended adversarial
attacks that insert special symbols or leverage the distinctive features of the
Korean language. Furthermore, we introduce simple yet effective pooling
strategies in a layer-wise manner to defend against the proposed attacks,
focusing on the preceding layers not just the last layer to capture both
offensiveness and token embeddings. We demonstrate that these pooling
strategies are more robust to performance degradation even when the attack rate
is increased, without directly training of such patterns. Notably, we found
that models pre-trained on clean texts could achieve a comparable performance
in detecting attacked offensive language, to models pre-trained on noisy texts
by employing these pooling strategies.