A large number of conflict events are affecting the world all the time. In
order to analyse such conflict events effectively, this paper presents a
Classification-Aware Neural Topic Model (CANTM-IA) for Conflict Information
Classification and Topic Discovery. The model provides a reliable
interpretation of classification results and discovered topics by introducing
interpretability analysis. At the same time, interpretation is introduced into
the model architecture to improve the classification performance of the model
and to allow interpretation to focus further on the details of the data.
Finally, the model architecture is optimised to reduce the complexity of the
model.

通过引入解释性分析，本文提出了用于冲突信息分类和主题发现的分类感知神经主题模型（CANTM-IA），提供对分类结果和发现的主题的可靠解释。同时，将解释性引入模型架构以提高模型的分类性能，并允许将解释进一步集中在数据的细节上。最后，对模型的架构进行了优化，以减少模型的复杂性。