Stance detection refers to the task of extracting the standpoint (Favor, Against or Neither) towards a target in given texts. Such research gains increasing attention with the proliferation of social media contents. The conventional framework of handling stance detection is converting it into text classification tasks. Deep learning models have already replaced rule-based models and traditional machine learning models in solving such problems. Current deep neural networks are facing two main challenges which are insufficient labeled data and information in social media posts and the unexplainable nature of deep learning models. A new pre-trained language model chatGPT was launched on Nov 30, 2022. For the stance detection tasks, our experiments show that ChatGPT can achieve SOTA or similar performance for commonly used datasets including SemEval-2016 and P-Stance. At the same time, ChatGPT can provide explanation for its own prediction, which is beyond the capability of any existing model. The explanations for the cases it cannot provide classification results are especially useful. ChatGPT has the potential to be the best AI model for stance detection tasks in NLP, or at least change the research paradigm of this field. ChatGPT also opens up the possibility of building explanatory AI for stance detection.
翻译:深层次的学习模式已经取代了基于规则的模式和传统的机器学习模式来解决这些问题。目前的深层神经网络面临两大挑战,在社交媒体职位上标记的数据和信息不足,深层次学习模式的性质无法解释。2022年11月30日启动了一个新的预先培训的语言模式聊天模式。对于定位检测任务,我们的实验表明,查特GPT可以实现SOTA或类似的性能,包括SemEval-2016和P-Stance等常用数据集。与此同时,查特GPT可以解释自己的预测,这超出了任何现有模式的能力。解释它无法提供分类结果的案例特别有用。查特GPT有可能成为国家定位平台中定位检测任务的最佳AI模式,或者至少改变这个领域的研究模式。查特GPTS也开启了建立AI探测的可能性。