Deep Transformers with Latent Depth

Conference on Neural Information Processing Systems (NeurIPS)


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.

Related Publications

All Publications

NeurIPS - December 6, 2020

High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization

Qing Feng, Benjamin Letham, Hongzi Mao, Eytan Bakshy

Innovative Technology at the Interface of Finance and Operations - March 31, 2021

Market Equilibrium Models in Large-Scale Internet Markets

Christian Kroer, Nicolas E. Stier-Moses

Human Interpretability Workshop at ICML - July 17, 2020

Investigating Effects of Saturation in Integrated Gradients

Vivek Miglani, Bilal Alsallakh, Narine Kokhlikyan, Orion Reblitz-Richardson

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy