Enhancing BERT-Based Visual Question Answering through Keyword-Driven
  Sentence Selection
Davide Napolitano Lorenzo Vaiani Luca Cagliero
The Document-based Visual Question Answering competition addresses the
automatic detection of parent-child relationships between elements in
multi-page documents. The goal is to identify the document elements that answer
a specific question posed in natural language. This paper describes the
PoliTo's approach to addressing this task, in particular, our best solution
explores a text-only approach, leveraging an ad hoc sampling strategy.
Specifically, our approach leverages the Masked Language Modeling technique to
fine-tune a BERT model, focusing on sentences containing sensitive keywords
that also occur in the questions, such as references to tables or images.
Thanks to the effectiveness of this approach, we are able to achieve high
performance compared to baselines, demonstrating how our solution contributes
positively to this task.