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：鉴于每分钟在线产生的大量内容，具备俚语感知能力的自动化工具对于促进社会福祉、协助政策制定者和审核员限制攻击性语言、辱骂及仇恨言论的传播至关重要。尽管大语言模型取得了成功，俚语词典也自发涌现，但其结合在面向社会福祉的下游任务中能在多大程度上实现俚语理解尚不明确。本文提出一个框架，用于研究不同的表征学习模型与知识资源组合在多种依赖俚语理解的下游任务中的表现。实验表明，在社交媒体数据上预训练的模型具有优越性，而词典的积极作用仅体现在静态词嵌入中。误差分析揭示了俚语表征学习的核心挑战，包括未登录词、一词多义、变体及标注分歧，这些问题可追溯至俚语作为一种快速演变且高度主观的语言特性。