Exploring Routing Strategies for Multilingual Mixture-of-Experts Models
Sparsely-Gated Mixture-of-Experts (MoE) has been a successful approach for scaling multilingual translation models to billions of parameters without a proportional increase in training computation. These models, however, are prohibitively large for serving deployment and there is no easy way to extract a sub-network to decode for a particular language pair. This work proposes improved strategies to route MoE models by tasks instead of tokens, thus enabling separation of network structures at decoding time while enjoying the benefits of scale and task sharing at training time. We compare routing strategies at multiple levels (token, sentence, task) in both, the encoder and the decoder, and conduct extensive experiments on two benchmarks: the public WMT dataset of 30 language pairs and an in-house web-scale dataset of 200 language pairs. On WMT, with a Transformer base model with 32 experts, our task-level MoE outperforms the best performing token-level MoE model by +1.0 BLEU on average over all language pairs. When scaling up to Transformer big model with 128 experts on the large-scale massively multilingual benchmark, our task-level MoE is competitive with token-level MoE while being able to reduce the decoder model size by a factor of $32$ at inference.
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