29 citations · 37 across the 3 of their papers we have counts for
5 papers
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…
Improving Bidirectional Decoding with Dynamic Target Semantics in Neural Machine Translation
Yong Shan, Yang Feng, Jinchao Zhang +2
Generally, Neural Machine Translation models generate target words in a left-to-right (L2R) manner and fail to exploit any future (right) semantics information, which usually produ…
Bridging the Gap between Training and Inference for Neural Machine Translation
Wen Zhang, Yang Feng, Fandong Meng +2
Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words. At training time, it predicts with the…
Speeding Up Neural Machine Translation Decoding by Cube Pruning
Wen Zhang, Liang Huang, Yang Feng +2
Although neural machine translation has achieved promising results, it suffers from slow translation speed. The direct consequence is that a trade-off has to be made between transl…
Refining Source Representations with Relation Networks for Neural Machine Translation
Wen Zhang, Jiawei Hu, Yang Feng +1
Although neural machine translation with the encoder-decoder framework has achieved great success recently, it still suffers drawbacks of forgetting distant information, which is a…