  One of the main challenges of the machine reading comprehension (MRC) models
is their fragile out-of-domain generalization, which makes these models not
properly applicable to real-world general-purpose question answering problems.
In this paper, we leverage a zero-shot weighted ensemble method for improving
the robustness of out-of-domain generalization in MRC models. In the proposed
method, a weight estimation module is used to estimate out-of-domain weights,
and an ensemble module aggregate several base models' predictions based on
their weights. The experiments indicate that the proposed method not only
improves the final accuracy, but also is robust against domain changes.
