Mixture-of-experts (MoE) architecture has been proven a powerful method for diverse tasks in training deep models in many applications. However, current MoE implementations are task agnostic, treating all tokens from different tasks in the same manner. In this work, we instead design a novel method that incorporates task information into MoE models at different granular levels with shared dynamic task-based adapters. Our experiments and analysis show the advantages of our approaches over the dense and canonical MoE models on multi-task multilingual machine translations. With task-specific adapters, our models can additionally generalize to new tasks efficiently.
@article{arxiv.2308.15772,
title = {Task-Based MoE for Multitask Multilingual Machine Translation},
author = {Hai Pham and Young Jin Kim and Subhabrata Mukherjee and David P. Woodruff and Barnabas Poczos and Hany Hassan Awadalla},
journal= {arXiv preprint arXiv:2308.15772},
year = {2023}
}