October 26, 2020
The Transformer model has achieved state-of-the-art performance in many sequence modeling tasks. However, how to leverage model capacity with large or variable depths is still an open challenge. We present a probabilistic framework to automatically learn which layer(s) to use by learning the posterior distributions of layer selection. As an extension of this framework, we propose a novel method to train one shared Transformer network for multilingual machine translation with different layer selection posteriors for each language pair. The proposed method alleviates the vanishing gradient issue and enables stable training of deep Transformers (e.g. 100 layers). We evaluate on WMT English-German machine translation and masked language modeling tasks, where our method outperforms existing approaches for training deeper Transformers. Experiments on multilingual machine translation demonstrate that this approach can effectively leverage increased model capacity and bring universal improvement for both many-to-one and one-to-many translation with diverse language pairs.
Publisher
NeurIPS
November 30, 2023
Xutai Ma, Anna Sun, Hirofumi Inaguma, Paden Tomasello, Siqi Ouyang
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November 30, 2023
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November 30, 2023
October 04, 2023
Alexandre Defossez, Charlotte Caucheteux, Jérémy Rapin, Ori Kabeli, Jean Remi King
October 04, 2023
August 22, 2023
Seamless Communication, Loic Barrault, Andy Chung, David Dale, Ning Dong (AI), Paul-Ambroise Duquenne, Hady Elsahar, Hongyu Gong, Kevin Heffernan, John Hoffman, Christopher Klaiber, Peng-Jen Chen, Daniel Licht, Jean Maillard, Alice Rakotoarison, Kaushik Ram Sadagopan, Guillaume Wenzek, Abinesh Ramakrishnan, Alexandre Mourachko, Amanda Kallet, Ann Lee, Anna Sun, Bapi Akula, Benjamin Peloquin, Bernie Huang, Bokai Yu, Brian Ellis, Can Balioglu, Carleigh Wood, Changhan Wang, Christophe Ropers, Cynthia Gao, Daniel Li (FAIR), Elahe Kalbassi, Ethan Ye, Gabriel Mejia Gonzalez, Hirofumi Inaguma, Holger Schwenk, Igor Tufanov, Ilia Kulikov, Janice Lam, Jeff Wang (PM - AI), Juan Pino, Justin Haaheim, Justine Kao, Prangthip Hasanti, Kevin Tran, Maha Elbayad, Marta R. Costa-jussa, Mohamed Ramadan, Naji El Hachem, Onur Çelebi, Paco Guzmán, Paden Tomasello, Pengwei Li, Pierre Andrews, Ruslan Mavlyutov, Russ Howes, Safiyyah Saleem, Skyler Wang, Somya Jain, Sravya Popuri, Tuan Tran, Vish Vogeti, Xutai Ma, Yilin Yang
August 22, 2023
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