Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation

international conference on learning representations, 2021.

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We investigate and improve parameter-sharing strategies in multilingual Transformers by utilizing conditional computation.

Abstract:

Using a mix of shared and language-specific (LS) parameters has shown promise in multilingual neural machine translation (MNMT), but the question of when and where LS capacity matters most is still under-studied. We offer such a study by proposing conditional language-specific routing (CLSR). CLSR employs hard binary gates conditioned on ...More

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