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

21 February 2019
Jonathan Shen
Patrick Nguyen
Yonghui Wu
Z. Chen
M. Chen
Ye Jia
Anjuli Kannan
Tara N. Sainath
Yuan Cao
Chung-Cheng Chiu
Yanzhang He
J. Chorowski
Smit Hinsu
Stella Laurenzo
James Qin
Orhan Firat
Wolfgang Macherey
Suyog Gupta
Ankur Bapna
Shuyuan Zhang
Ruoming Pang
Ron J. Weiss
Rohit Prabhavalkar
Qiao Liang
Benoit Jacob
Bowen Liang
HyoukJoong Lee
Ciprian Chelba
Sébastien Jean
Bo-wen Li
Melvin Johnson
Rohan Anil
Rajat Tibrewal
Xiaobing Liu
Akiko Eriguchi
Navdeep Jaitly
Naveen Ari
Colin Cherry
Parisa Haghani
Otavio Good
Youlong Cheng
R. Álvarez
Isaac Caswell
Wei-Ning Hsu
Zongheng Yang
Kuan Wang
Ekaterina Gonina
Katrin Tomanek
Ben Vanik
Zelin Wu
Llion Jones
M. Schuster
Yanping Huang
Dehao Chen
Kazuki Irie
George F. Foster
J. Richardson
Klaus Macherey
A. Bruguier
Heiga Zen
Colin Raffel
Shankar Kumar
Kanishka Rao
David Rybach
M. Murray
Vijayaditya Peddinti
M. Krikun
M. Bacchiani
T. Jablin
R. Suderman
Ian Williams
Benjamin Lee
Deepti Bhatia
Justin Carlson
Semih Yavuz
Yu Zhang
Ian McGraw
M. Galkin
Qi Ge
Golan Pundak
Chad Whipkey
Todd Wang
Uri Alon
Dmitry Lepikhin
Ye Tian
S. Sabour
William Chan
Shubham Toshniwal
Baohua Liao
M. Nirschl
Pat Rondon
    VLM
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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.

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