论文标题
CHATGPT推出后,立场检测技术将如何发展?
How would Stance Detection Techniques Evolve after the Launch of ChatGPT?
论文作者
论文摘要
立场检测是指在给定文本中提取目标(偏爱,不反对或两者)的任务。随着社交媒体内容的扩散,此类研究越来越关注。处理立场检测的常规框架是将其转换为文本分类任务。深度学习模型已经取代了基于规则的模型和传统的机器学习模型,以解决此类问题。当前的深层神经网络面临两个主要挑战,这些挑战是社交媒体帖子中标记的数据和信息不足以及深度学习模型的无法解释的性质。 2022年11月30日启动了一个新的预训练的语言模型CHATGPT。对于立场检测任务,我们的实验表明,Chatgpt可以为包括Semeval-2016和P-Stance在内的常用数据集实现SOTA或类似性能。同时,Chatgpt可以为自己的预测提供解释,这超出了任何现有模型的能力。对于无法提供分类结果的情况的解释特别有用。 Chatgpt有可能成为NLP中立场检测任务的最佳AI模型,或者至少改变该领域的研究范例。 Chatgpt还开辟了建立解释性AI以进行立场检测的可能性。
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.