537 citations · 902 across the 19 of their papers we have counts for
5 papers · 1 filter
Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input
Junliang Guo, Xu Tan, Di He +3
Non-autoregressive translation (NAT) models, which remove the dependence on previous target tokens from the inputs of the decoder, achieve significantly inference speedup but at th…
Sentence-wise Smooth Regularization for Sequence to Sequence Learning
Chengyue Gong, Xu Tan, Di He +1
Maximum-likelihood estimation (MLE) is widely used in sequence to sequence tasks for model training. It uniformly treats the generation/prediction of each target token as multi-cla…
FRAGE: Frequency-Agnostic Word Representation
Chengyue Gong, Di He, Xu Tan +3
Continuous word representation (aka word embedding) is a basic building block in many neural network-based models used in natural language processing tasks. Although it is widely a…
Double Path Networks for Sequence to Sequence Learning
Kaitao Song, Xu Tan, Di He +3
Encoder-decoder based Sequence to Sequence learning (S2S) has made remarkable progress in recent years. Different network architectures have been used in the encoder/decoder. Among…
Dense Information Flow for Neural Machine Translation
Yanyao Shen, Xu Tan, Di He +2
Recently, neural machine translation has achieved remarkable progress by introducing well-designed deep neural networks into its encoder-decoder framework. From the optimization pe…