  With the spreading of hate speech on social media in recent years, automatic
detection of hate speech is becoming a crucial task and has attracted attention
from various communities. This task aims to recognize online posts (e.g.,
tweets) that contain hateful information. The peculiarities of languages in
social media, such as short and poorly written content, lead to the difficulty
of learning semantics and capturing discriminative features of hate speech.
Previous studies have utilized additional useful resources, such as sentiment
hashtags, to improve the performance of hate speech detection. Hashtags are
added as input features serving either as sentiment-lexicons or extra context
information. However, our close investigation shows that directly leveraging
these features without considering their context may introduce noise to
classifiers. In this paper, we propose a novel approach to leverage sentiment
hashtags to enhance hate speech detection in a natural language inference
framework. We design a novel framework SRIC that simultaneously performs two
tasks: (1) semantic relation inference between online posts and sentiment
hashtags, and (2) sentiment classification on these posts. The semantic
relation inference aims to encourage the model to encode sentiment-indicative
information into representations of online posts. We conduct extensive
experiments on two real-world datasets and demonstrate the effectiveness of our
proposed framework compared with state-of-the-art representation learning
models.
