Neuron Specialization: Leveraging intrinsic task modularity for multilingual machine translation

Understand

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.