  To completely understand a document, the use of textual information is not
enough. Understanding visual cues, such as layouts and charts, is also
required. While the current state-of-the-art approaches for document
understanding (both OCR-based and OCR-free) work well, a thorough analysis of
their capabilities and limitations has not yet been performed. Therefore, in
this work, we addresses the limitation of current VisualQA models when applied
to charts and plots. To investigate shortcomings of the state-of-the-art
models, we conduct a comprehensive behavioral analysis, using ChartQA as a case
study. Our findings indicate that existing models particularly underperform in
answering questions related to the chart's structural and visual context, as
well as numerical information. To address these issues, we propose three simple
pre-training tasks that enforce the existing model in terms of both
structural-visual knowledge, as well as its understanding of numerical
questions. We evaluate our pre-trained model (called MatCha-v2) on three chart
datasets - both extractive and abstractive question datasets - and observe that
it achieves an average improvement of 1.7% over the baseline model.
