论文标题
要在社交媒体上解释个人的主观基础
Towards Explaining Subjective Ground of Individuals on Social Media
论文作者
论文摘要
大规模的语言模型一直在减少机器和人类之间在理解现实世界中的鸿沟，但是从文本中理解个人的思想和行为理论尚未得到解决。 这项研究提出了一种神经模型 - 主观基础的关注 - 了解个人的主观理由，并在社交媒体上发布的其他情况下对其他情况进行判断。使用简单的注意模块以及考虑以前的活动，我们从经验上表明，我们的模型在判断社交情况时对个人的主观偏好提供了可读的解释。我们进一步评估了该模型产生的解释，并声称我们的模型学习了个人对抽象道德概念的主观取向
Large-scale language models have been reducing the gap between machines and humans in understanding the real world, yet understanding an individual's theory of mind and behavior from text is far from being resolved. This research proposes a neural model -- Subjective Ground Attention -- that learns subjective grounds of individuals and accounts for their judgments on situations of others posted on social media. Using simple attention modules as well as taking one's previous activities into consideration, we empirically show that our model provides human-readable explanations of an individual's subjective preference in judging social situations. We further qualitatively evaluate the explanations generated by the model and claim that our model learns an individual's subjective orientation towards abstract moral concepts
