The use of Transfer Learning & Transformers has steadily improved accuracy and has significantly contributed in solving complex computation problems. However, this transformer led accuracy improvement in Applied AI Analytics specifically in sentiment analytics comes with the dark side. It is observed during experiments that a lot of these improvements in transformer led accuracy of one class of sentiment has been at the cost of polarization of another class of sentiment and the failing of neutrality. This lack of neutrality poses an acute problem in the Applied NLP space, which relies heavily on the computational outputs of sentiment analytics for reliable industry ready tasks.

翻译：迁移学习与Transformer模型的运用持续提升了计算精度，对解决复杂计算问题作出了显著贡献。然而，在应用型人工智能分析（特别是情感分析领域）中，这种由Transformer驱动的精度提升亦伴随着阴暗面。实验观察发现，Transformer模型对某一类情感分析精度的提升，往往以另一类情感的极化与中立性失效为代价。这种中立性的缺失对应用型自然语言处理领域构成了严峻挑战，因为该领域高度依赖情感分析的计算结果来执行可靠的产业化任务。