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In this work we present a Mixture of Task-Aware Experts Network for MachineReading Comprehension on a relatively small dataset. We particularly focus onthe issue of common-sense learning, enforcing the common ground knowledge byspecifically training different expert networks to capture different kinds ofrelationships between each passage, question and choice triplet. Moreover, wetake inspi ration on the recent advancements of multitask and transfer learningby training each network a relevant focused task. By making themixture-of-networks aware of a specific goal by enforcing a task and arelationship, we achieve state-of-the-art results and reduce over-fitting.

Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts