  Textbook Question Answering (TQA) is a complex multimodal task to infer
answers given large context descriptions and abundant diagrams. Compared with
Visual Question Answering (VQA), TQA contains a large number of uncommon
terminologies and various diagram inputs. It brings new challenges to the
representation capability of language model for domain-specific spans. And it
also pushes the multimodal fusion to a more complex level. To tackle the above
issues, we propose a novel model named MoCA, which incorporates multi-stage
domain pretraining and multimodal cross attention for the TQA task. Firstly, we
introduce a multi-stage domain pretraining module to conduct unsupervised
post-pretraining with the span mask strategy and supervised pre-finetune.
Especially for domain post-pretraining, we propose a heuristic generation
algorithm to employ the terminology corpus. Secondly, to fully consider the
rich inputs of context and diagrams, we propose cross-guided multimodal
attention to update the features of text, question diagram and instructional
diagram based on a progressive strategy. Further, a dual gating mechanism is
adopted to improve the model ensemble. The experimental results show the
superiority of our model, which outperforms the state-of-the-art methods by
2.21% and 2.43% for validation and test split respectively.
