The advent of social media in recent years has fed into some highly
undesirable phenomena such as proliferation of offensive language, hate speech,
sexist remarks, etc. on the Internet. In light of this, there have been several
efforts to automate the detection and moderation of such abusive content.
However, deliberate obfuscation of words by users to evade detection poses a
serious challenge to the effectiveness of these efforts. The current state of
the art approaches to abusive language detection, based on recurrent neural
networks, do not explicitly address this problem and resort to a generic OOV
(out of vocabulary) embedding for unseen words. However, in using a single
embedding for all unseen words we lose the ability to distinguish between
obfuscated and non-obfuscated or rare words. In this paper, we address this
problem by designing a model that can compose embeddings for unseen words. We
experimentally demonstrate that our approach significantly advances the current
state of the art in abuse detection on datasets from two different domains,
namely Twitter and Wikipedia talk page.

本研究设计了一个可以为未知单词组合嵌入的模型，与现有的自然语言处理技术相比，本方法可以更好地检测社交媒体上的滥用言论，数据集包含 Twitter 和 Wikipedia talk page。