Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

Jonathan Shen, Patrick Nguyen, Yonghui Wu, Zhifeng Chen, Mia X. Chen, Ye Jia, Anjuli Kannan, Tara Sainath, Yuan Cao, Chung-Cheng Chiu, Yanzhang He, Jan Chorowski, Smit Hinsu, Stella Laurenzo, James Qin, Orhan Firat, Wolfgang Macherey, Suyog Gupta, Ankur Bapna, Shuyuan Zhang et al. (71 additional authors not shown)

Abstract:

Lingvo is a Tensorflow framework offering a complete solution for collaborative
deep learning research, with a particular focus towards sequence-to-sequence
models. Lingvo models are composed of modular building blocks that are flexible
and easily extensible, and experiment configurations are centralized and highly
customizable. Distributed training and quantized inference are supported directly
within the framework, and it contains existing implementations of a large number
of utilities, helper functions, and the newest research ideas. Lingvo has been used
in collaboration by dozens of researchers in more than 20 papers over the last
two years. This document outlines the underlying design of Lingvo and serves
as an introduction to the various pieces of the framework, while also offering
examples of advanced features that showcase the capabilities of the framework.