In order to solve the problem of overcorrection in automatic post-editing translations, the authors propose to make advantage of the neural post-editing (NPE) to build two special models: one is used to provide minor edit operations, the other is used to provide single edit operation, and make advantage of machine translation quality estimation to establish a filtering algorithm to integrate the special models with the regular NPE model into a jointed model. Experimental results on the test set of WMT16 APE shared task show that the proposed approach statistically outperforms the baseline. Deep analysis further confirms that proposed approach can bring considerable relief from the over-editing problem in APE.