activity
20192023
most citedModeling Fluency and Faithfulness for Diverse Neural Machine Translation

5 citations · 13 across the 8 of their papers we have counts for

collaborators

11 papers

cs.CL2022

Continual Learning of Neural Machine Translation within Low Forgetting Risk Regions

Shuhao Gu, Bojie Hu, Yang Feng

This paper considers continual learning of large-scale pretrained neural machine translation model without accessing the previous training data or introducing model separation. We…

cs.CL20223 cited

Improving Zero-Shot Multilingual Translation with Universal Representations and Cross-Mappings

Shuhao Gu, Yang Feng

The many-to-many multilingual neural machine translation can translate between language pairs unseen during training, i.e., zero-shot translation. Improving zero-shot translation r…

cs.CL2021

Importance-based Neuron Allocation for Multilingual Neural Machine Translation

Wanying Xie, Yang Feng, Shuhao Gu +1

Multilingual neural machine translation with a single model has drawn much attention due to its capability to deal with multiple languages. However, the current multilingual transl…

cs.CL20211 cited

Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation

Yang Feng, Shuhao Gu, Dengji Guo +2

Although teacher forcing has become the main training paradigm for neural machine translation, it usually makes predictions only conditioned on past information, and hence lacks gl…

cs.CL20213 cited

Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation

Shuhao Gu, Yang Feng, Wanying Xie

Domain Adaptation is widely used in practical applications of neural machine translation, which aims to achieve good performance on both the general-domain and in-domain. However,…

cs.CL20201 cited

Investigating Catastrophic Forgetting During Continual Training for Neural Machine Translation

Shuhao Gu, Yang Feng

Neural machine translation (NMT) models usually suffer from catastrophic forgetting during continual training where the models tend to gradually forget previously learned knowledge…