论文标题
学会通过实例加权来适应道德价值的变化
Learning to Adapt Domain Shifts of Moral Values via Instance Weighting
论文作者
论文摘要
从社交媒体中对用户生成的文本中的道德价值进行分类对于理解社区文化和解释社交运动的用户行为至关重要。道德价值观和语言使用可能会在整个社会运动中发生变化；但是，文本分类器通常在现有社会运动的源领域进行培训，并在新的社会问题的目标领域进行了测试，而无需考虑变化。在这项研究中，我们检查了道德价值和语言使用的领域变化，量化域转移对道德分类任务的影响，并通过实例加权提出神经适应框架，以改善跨域分类任务。量化分析表明，道德转移，语言使用和分类表现之间存在很强的相关性。我们在7个社会运动中的公共Twitter数据上评估了神经适应框架，并获得最高12.1 \％的分类改进。最后，我们发布了带有道德价值标记的Covid-19疫苗的新数据，并评估了我们在新目标域上的方法。对于COVID-19疫苗的案例研究，我们的适应框架可实现高达5.26 \％的改善神经基础线。
Classifying moral values in user-generated text from social media is critical in understanding community cultures and interpreting user behaviors of social movements. Moral values and language usage can change across the social movements; however, text classifiers are usually trained in source domains of existing social movements and tested in target domains of new social issues without considering the variations. In this study, we examine domain shifts of moral values and language usage, quantify the effects of domain shifts on the morality classification task, and propose a neural adaptation framework via instance weighting to improve cross-domain classification tasks. The quantification analysis suggests a strong correlation between morality shifts, language usage, and classification performance. We evaluate the neural adaptation framework on a public Twitter data across 7 social movements and gain classification improvements up to 12.1\%. Finally, we release a new data of the COVID-19 vaccine labeled with moral values and evaluate our approach on the new target domain. For the case study of the COVID-19 vaccine, our adaptation framework achieves up to 5.26\% improvements over neural baselines.
