11 citations · 17 across the 4 of their papers we have counts for
5 papers
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation
Bei Li, Quan Du, Tao Zhou +7
Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). This paper explores a deeper relationship between Transformer and numerical ODE…
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Neural Machine Translation
Bei Li, Quan Du, Tao Zhou +4
It has been found that residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODEs). In this paper, we explore a deeper relationship between…
Learning Light-Weight Translation Models from Deep Transformer
Bei Li, Ziyang Wang, Hui Liu +4
Recently, deep models have shown tremendous improvements in neural machine translation (NMT). However, systems of this kind are computationally expensive and memory intensive. In t…
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction
Yanyang Li, Yingfeng Luo, Ye Lin +5
Unsupervised Bilingual Dictionary Induction methods based on the initialization and the self-learning have achieved great success in similar language pairs, e.g., English-Spanish.…
Shallow-to-Deep Training for Neural Machine Translation
Bei Li, Ziyang Wang, Hui Liu +5
Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but training an extremely deep encoder is time consuming. Moreover, why deep m…