  Question answering (QA) extracting answers from text to the given question in
natural language, has been actively studied and existing models have shown a
promise of outperforming human performance when trained and evaluated with
SQuAD dataset. However, such performance may not be replicated in the actual
setting, for which we need to diagnose the cause, which is non-trivial due to
the complexity of model. We thus propose a web-based UI that provides how each
model contributes to QA performances, by integrating visualization and analysis
tools for model explanation. We expect this framework can help QA model
researchers to refine and improve their models.
