30 citations · 231 across the 47 of their papers we have counts for
8 papers · 2 filters
On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation
Xuebo Liu, Longyue Wang, Derek F. Wong +4
Pre-training (PT) and back-translation (BT) are two simple and powerful methods to utilize monolingual data for improving the model performance of neural machine translation (NMT).…
Rethinking Negative Sampling for Handling Missing Entity Annotations
Yangming Li, Lemao Liu, Shuming Shi
Negative sampling is highly effective in handling missing annotations for named entity recognition (NER). One of our contributions is an analysis on how it makes sense through intr…
On the Copying Behaviors of Pre-Training for Neural Machine Translation
Xuebo Liu, Longyue Wang, Derek F. Wong +4
Previous studies have shown that initializing neural machine translation (NMT) models with the pre-trained language models (LM) can speed up the model training and boost the model…
On the Language Coverage Bias for Neural Machine Translation
Shuo Wang, Zhaopeng Tu, Zhixing Tan +3
Language coverage bias, which indicates the content-dependent differences between sentence pairs originating from the source and target languages, is important for neural machine t…
Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation
Wenxiang Jiao, Xing Wang, Zhaopeng Tu +3
Self-training has proven effective for improving NMT performance by augmenting model training with synthetic parallel data. The common practice is to construct synthetic data based…
GWLAN: General Word-Level AutocompletioN for Computer-Aided Translation
Huayang Li, Lemao Liu, Guoping Huang +1
Computer-aided translation (CAT), the use of software to assist a human translator in the translation process, has been proven to be useful in enhancing the productivity of human t…