The Prompt Learning Framework for Stance Detection Using Topic Knowledge Enhancement in Large-scale Models
Stance detection aims to analyze the attitudes of opinionated texts towards a given target,such as support,neutrality,or opposition.With the development of pre-trained models,existing methods mainly construct stance detection models based on fine-tuning frameworks.Recently,the prompt learning framework has achieved success in natural language processing tasks.However,building a prompt learn-ing framework for stance detection still faces challenges in practical applications.Tweet texts may not ex-plicitly express a certain attitude but use various topic labels or background knowledge to convey stance views.In this paper,we propose a background knowledge-enhanced prompt learning framework(BKEF).Specifically,we first introduce a topic model to learn topic representations.Then a prompt-learning network is proposed for integrate topic knowledge.Finally,we evaluate our method on three publicly available data-sets,and experimental results demonstrate that our proposed BKEF method outperforms existing methods.
NETL
NSTL
万方数据
