Warning: this paper contains content that may be offensive or upsetting. Considering the large amount of content created online by the minute, slang-aware automatic tools are critically needed to promote social good, and assist policymakers and moderators in restricting the spread of offensive language, abuse, and hate speech. Despite the success of large language models and the spontaneous emergence of slang dictionaries, it is unclear how far their combination goes in terms of slang understanding for downstream social good tasks. In this paper, we provide a framework to study different combinations of representation learning models and knowledge resources for a variety of downstream tasks that rely on slang understanding. Our experiments show the superiority of models that have been pre-trained on social media data, while the impact of dictionaries is positive only for static word embeddings. Our error analysis identifies core challenges for slang representation learning, including out-of-vocabulary words, polysemy, variance, and annotation disagreements, which can be traced to characteristics of slang as a quickly evolving and highly subjective language.

翻译：警告：本文包含可能引发不适或冒犯的内容。考虑到每分钟在线产生的大量内容，亟需具备俚语感知能力的自动化工具以促进社会公益，并协助政策制定者和内容审核员限制冒犯性语言、辱骂及仇恨言论的传播。尽管大语言模型已取得显著成功且俚语词典不断自发涌现，但二者结合在面向下游社会公益任务的俚语理解中能达到何种程度仍不明确。本文构建了一个框架，系统研究表征学习模型与知识资源的不同组合在依赖俚语理解的下游任务中的表现。实验表明，基于社交媒体数据预训练的模型具有显著优势，而词典仅对静态词嵌入产生正向影响。通过错误分析，我们识别出俚语表征学习的核心挑战，包括词汇表外词、多义性、变异性和标注分歧，这些问题均可追溯至俚语作为快速演变且高度主观语言的本质特征。