Learning to Answer Complex Visual Questions from Multi-View Analysis

Visual Question Answering (VQA) has received increasing attention in NLP research. Most VQA images focus on natural scenes. However, some images widely used in textbooks such as diagrams often contain complicated and abstract information (e.g. constructed graphs with logic and concepts). Therefore, Diagram Question answering (DQA) is a challenging but significant task, which is also helpful for machines to understand human cognitive behaviors and learning habits. On DQA task, we propose a multi-perspective understanding based visual question-answering method, which constructs a variety of different self-monitoring tasks in the form of prompts to help the model learn deeper information. For the first time, we propose a decoding method of "Cross Entropy constraint Decoding", which can effectively constrain the content generated by the text when performing multiple selection tasks. This method has obtained SOTA in the evaluation task of CCKS-2022, which fully proves the effectiveness of the method.