Direct neural machine translation (direct NMT) is a type of NMT system that
translates text between two non-English languages. Direct NMT systems often
face limitations due to the scarcity of parallel data between non-English
language pairs. Several approaches have been proposed to address this
limitation, such as multilingual NMT and pivot NMT (translation between two
languages via English). Task-level Mixture of expert models (Task-level MoE),
an inference-efficient variation of Transformer-based models, has shown
promising NMT performance for a large number of language pairs. In Task-level
MoE, different language groups can use different routing strategies to optimize
cross-lingual learning and inference speed. In this work, we examine Task-level
MoE's applicability in direct NMT and propose a series of high-performing
training and evaluation configurations, through which Task-level MoE-based
direct NMT systems outperform bilingual and pivot-based models for a large
number of low and high-resource direct pairs, and translation directions. Our
Task-level MoE with 16 experts outperforms bilingual NMT, Pivot NMT models for
7 language pairs, while pivot-based models still performed better in 9 pairs
and directions.

通过对 Task-level MoE 在直接 NMT 中的适用性进行研究，我们提出了一系列高性能的训练和评估配置，通过这些配置，基于 Task-level MoE 的直接 NMT 系统在大多数低资源和高资源的直接语言对和翻译方向上优于双语和基于中间语言的模型。