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

该论文介绍了一个针对较小数据集的任务感知专家网络混合模型，重点研究常识学习问题，并通过训练不同的专家网络来捕获每个段落、问题和选项三元组之间不同类型的关系，同时灵感来自多任务和迁移学习的最新进展。通过将多个网络融合，强制实现它们的特定目标和关系，作者达到了最先进的结果并减少了过拟合。