Training a unified multilingual model promotes knowledge transfer but
inevitably introduces negative interference. Language-specific modeling methods
show promise in reducing interference. However, they often rely on heuristics
to distribute capacity and struggle to foster cross-lingual transfer via
isolated modules. In this paper, we explore intrinsic task modularity within
multilingual networks and leverage these observations to circumvent
interference under multilingual translation. We show that neurons in the
feed-forward layers tend to be activated in a language-specific manner.
Meanwhile, these specialized neurons exhibit structural overlaps that reflect
language proximity, which progress across layers. Based on these findings, we
propose Neuron Specialization, an approach that identifies specialized neurons
to modularize feed-forward layers and then continuously updates them through
sparse networks. Extensive experiments show that our approach achieves
consistent performance gains over strong baselines with additional analyses
demonstrating reduced interference and increased knowledge transfer.

通过神经元特化，提出一种解决多语言翻译中干扰和促进知识转移的方法，并通过大量实验证明了其相比强基准模型的性能提升以及减少干扰并增加知识转移的效果。