16 citations · 71 across the 12 of their papers we have counts for
13 papers · 1 filter
Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet
Xingwei He, Victor O. K. Li
Lexically constrained sentence generation allows the incorporation of prior knowledge such as lexical constraints into the output. This technique has been applied to machine transl…
Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model
Juntao Li, Ruidan He, Hai Ye +3
Recent research indicates that pretraining cross-lingual language models on large-scale unlabeled texts yields significant performance improvements over various cross-lingual and l…
On the Sparsity of Neural Machine Translation Models
Yong Wang, Longyue Wang, Victor O. K. Li +1
Modern neural machine translation (NMT) models employ a large number of parameters, which leads to serious over-parameterization and typically causes the underutilization of comput…
Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks
Yong Wang, Longyue Wang, Shuming Shi +2
The key challenge of multi-domain translation lies in simultaneously encoding both the general knowledge shared across domains and the particular knowledge distinctive to each doma…
Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations
Jiatao Gu, Yong Wang, Kyunghyun Cho +1
Zero-shot translation, translating between language pairs on which a Neural Machine Translation (NMT) system has never been trained, is an emergent property when training the syste…
Meta-Learning for Low-Resource Neural Machine Translation
Jiatao Gu, Yong Wang, Yun Chen +2
In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML) for low-resource neural machine translation (NMT). We frame low-resource t…