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

Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization