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:翻译标题:ChatGPT发布后立场检测技术会如何演变?
翻译摘要:立场检测是指从给定文本中提取对目标的立场(赞成、反对或无立场)的任务。此类研究随着社交媒体内容的增多而越来越受到关注。处理立场检测的传统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来解决此类问题。当前的深度神经网络面临两个主要挑战,即标注数据和社交媒体帖子中的信息不足,以及深度学习模型的不可解释性。一个新的预训练语言模型 ChatGPT 在2022年11月30日发布。对于立场检测任务,我们的实验证明 ChatGPT 可以在常用数据集(包括 SemEval-2016 和 P-Stance)上实现后续工作(SOTA)或类似的性能。同时,ChatGPT 可以为其自己的预测提供解释,这是任何现有模型无法达到的能力。对于它无法提供分类结果的情况,解释尤其有用。ChatGPT 有潜力成为 NLP 领域立场检测任务的最佳 AI 模型,或至少改变这个领域的研究范式。ChatGPT 还为立场检测构建解释性 AI 提供了可能性。