Sparsely Mixture of Experts (MoE) has received great interest due to its
promising scaling capability with affordable computational overhead. MoE
converts dense layers into sparse experts, and utilizes a gated routing network
to make experts conditionally activated. However, as the number of experts
grows, MoE with outrageous parameters suffers from overfitting and sparse data
allocation. Such problems are especially severe on tasks with limited data,
thus hindering the progress for MoE models to improve performance by scaling
up. In this work, we propose Mixture of Expert Clusters - a general approach to
enable expert layers to learn more diverse and appropriate knowledge by
imposing variance-based constraints on the routing stage. We further propose a
cluster-level expert dropout strategy specifically designed for the expert
cluster structure. Our experiments reveal that MoEC could improve performance
on machine translation and natural language understanding tasks, and raise the
performance upper bound for scaling up experts under limited data. We also
verify that MoEC plays a positive role in mitigating overfitting and sparse
data allocation.

本研究提出了 Mixture of Expert Clusters 模型，通过在路由阶段引入基于方差的约束来促进专家层学习更多不同和适当的知识，并提出了一种专家集群结构的集群级别专家丢失策略。实验证明，该模型可以提高机器翻译和自然语言理解任务的性能，并在有限数据条件下扩展专家的性能上限，对缓解过度拟合和稀疏数据分配问题起到积极作用。