28 citations · 52 across the 14 of their papers we have counts for
14 papers
Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features
Mengyu Bu, Shuhao Gu, Yang Feng
The many-to-many multilingual neural machine translation can be regarded as the process of integrating semantic features from the source sentences and linguistic features from the…
Addressing the Length Bias Problem in Document-Level Neural Machine Translation
Zhuocheng Zhang, Shuhao Gu, Min Zhang +1
Document-level neural machine translation (DNMT) has shown promising results by incorporating more context information. However, this approach also introduces a length bias problem…
Enhancing Neural Machine Translation with Semantic Units
Langlin Huang, Shuhao Gu, Zhuocheng Zhang +1
Conventional neural machine translation (NMT) models typically use subwords and words as the basic units for model input and comprehension. However, complete words and phrases comp…
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…
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…
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…
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…
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,…
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…
Token-level Adaptive Training for Neural Machine Translation
Shuhao Gu, Jinchao Zhang, Fandong Meng +4
There exists a token imbalance phenomenon in natural language as different tokens appear with different frequencies, which leads to different learning difficulties for tokens in Ne…
Modeling Fluency and Faithfulness for Diverse Neural Machine Translation
Yang Feng, Wanying Xie, Shuhao Gu +4
Neural machine translation models usually adopt the teacher forcing strategy for training which requires the predicted sequence matches ground truth word by word and forces the pro…
Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation
Zhengxin Yang, Jinchao Zhang, Fandong Meng +3
Context modeling is essential to generate coherent and consistent translation for Document-level Neural Machine Translations. The widely used method for document-level translation…
Improving Multi-Head Attention with Capsule Networks
Shuhao Gu, Yang Feng
Multi-head attention advances neural machine translation by working out multiple versions of attention in different subspaces, but the neglect of semantic overlapping between subsp…
Improving Domain Adaptation Translation with Domain Invariant and Specific Information
Shuhao Gu, Yang Feng, Qun Liu
In domain adaptation for neural machine translation, translation performance can benefit from separating features into domain-specific features and common features. In this paper,…