Understanding commonsense knowledge is crucial in the field of Natural
Language Processing (NLP). However, the presence of demographic terms in
commonsense knowledge poses a potential risk of compromising the performance of
NLP models. This study aims to investigate and propose methods for enhancing
the performance and effectiveness of a commonsense polarization classifier by
mitigating the influence of demographic terms. Three methods are introduced in
this paper: (1) hierarchical generalization of demographic terms (2)
threshold-based augmentation and (3) integration of hierarchical generalization
and threshold-based augmentation methods (IHTA). The first method involves
replacing demographic terms with more general ones based on a term hierarchy
ontology, aiming to mitigate the influence of specific terms. To address the
limited bias-related information, the second method measures the polarization
of demographic terms by comparing the changes in the model's predictions when
these terms are masked versus unmasked. This method augments commonsense
sentences containing terms with high polarization values by replacing their
predicates with synonyms generated by ChatGPT. The third method combines the
two approaches, starting with threshold-based augmentation followed by
hierarchical generalization. The experiments show that the first method
increases the accuracy over the baseline by 2.33%, and the second one by 0.96%
over standard augmentation methods. The IHTA techniques yielded an 8.82% and
9.96% higher accuracy than threshold-based and standard augmentation methods,
respectively.

通过层次概括、基于阈值的增强和层次概括与阈值增强方法相结合等三种方法，提高了常识极化分类器的性能和效果，减轻了人口统计术语对 NLP 模型性能的影响。