Optimization-driven parameter sharing via neural architecture search for transformer models in machine translation

Abstract Parameter sharing offers an effective mechanism for improving the efficiency of Transformer models, particularly in low-resource settings. However, existing approaches typically rely on manual design choices or heuristic rules, which become increasingly difficult to manage as model scale an...

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書誌詳細

主要な著者:	Befekadu Bekuretsion, Wolfgang Menzel, Solomon Teferra
フォーマット:	Artigo
言語:	Inglês
出版事項:	Springer 2026-06-01
シリーズ:	Discover Computing
主題:	Neural architecture search Parameter sharing Transformer models Bayesian optimization Machine translation
オンライン･アクセス:	https://doi.org/10.1007/s10791-026-10185-y
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