Hanzhuo Tan, Chunpu Xu, Jing Li, Yuqun Zhang, Zeyang Fang, Zeyu Chen, Baohua Lai
Natural language understanding (NLU) is integral to various social media applications. However, the existing NLU models rely heavily on context for semantic learning, resulting in compromised performance when faced with short and noisy social media content. To address this issue, we leverage in-context learning (ICL), wherein language models learn to make inferences by conditioning on a handful of demonstrations to enrich the context and propose a novel hashtag-driven ICL (HICL) framework. Concretely, we pretrain a model, which employs #hashtags (user-annotated topic labels) to drive BERT-based pretraining through contrastive learning...
April 15, 2024: IEEE Transactions on Neural Networks and Learning Systems