Leveraging pre-trained language models (PLMs) as initializers for efficient transfer learning has become a universal approach for text-related tasks. However, the models not only learn the language understanding abilities but also reproduce prejudices for certain groups in the datasets used for pre-training. Recent studies show that the biased knowledge acquired from the datasets affects the model predictions on downstream tasks. In this paper, we mitigate and analyze the gender biases in PLMs with coreference resolution, which is one of the natural language understanding (NLU) tasks. PLMs exhibit two types of gender biases: stereotype and skew. The primary causes for the biases are the imbalanced datasets with more male examples and the stereotypical examples on gender roles. While previous studies mainly focused on the skew problem, we aim to mitigate both gender biases in PLMs while maintaining the model's original linguistic capabilities. Our method employs two regularization terms, Stereotype Neutralization (SN) and Elastic Weight Consolidation (EWC). The models trained with the methods show to be neutralized and reduce the biases significantly on the WinoBias dataset compared to the public BERT. We also invented a new gender bias quantification metric called the Stereotype Quantification (SQ) score. In addition to the metrics, embedding visualizations were used to interpret how our methods have successfully debiased the models.