29 citations · 101 across the 16 of their papers we have counts for
5 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).…
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…
Progressive Multi-Granularity Training for Non-Autoregressive Translation
Liang Ding, Longyue Wang, Xuebo Liu +3
Non-autoregressive translation (NAT) significantly accelerates the inference process via predicting the entire target sequence. However, recent studies show that NAT is weak at lea…
Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation
Liang Ding, Longyue Wang, Xuebo Liu +3
Knowledge distillation (KD) is commonly used to construct synthetic data for training non-autoregressive translation (NAT) models. However, there exists a discrepancy on low-freque…
TranSmart: A Practical Interactive Machine Translation System
Guoping Huang, Lemao Liu, Xing Wang +5
Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed. This technique report introduces TranSmart, a practical human-machine…