Monitoring, analyzing, and predicting political turmoil and violence is of utmost importance to a host of political scientists. This is still usually carried out using event coding systems that utilize pattern matching and fixed-size dictionaries. Recently, BERT and ConfliBERT have achieved state-of-the-art results for event coding. However, these methods use a sequence classification paradigm, and thus, are unable to explicitly model the semantics of the labels and the rich interactions among them. In this paper, we propose a novel method for political event extraction on the standard CAMEO (Conflict and Medication Event Observations)-based data set by formulating the problem as question answering overcoming the above drawbacks. We can achieve superior results, improving ConfliBERT, the previous state-of-the-art model, by an absolute F1 of 2.02%. We also propose a new method for multi-source, multi-target sentences that increases the F1 by 2.29% compared to the previous best method.