Political debates are vital in shaping public opinion and influencing policy decisions. However, understanding the complex linguistic structures used by politicians to ascertain their orientations and power dynamics can be challenging. In this paper we explore Natural Language Processing techniques for identifying political orientation and power structures in parliamentary debates. We introduce a Located Missing Labels-loss in order to train jointly to predict both power and ideology. Furthermore, our proposed method also trains to predict a third synthetically generated polarity label. Finally, we combine this training method with pre-processing steps including back-translation and meta data inclusion. Our results show that our method manages to improve upon conventional methods of fine-tuning.