no code implementations • ICLR 2018 • Dalei Wu, Xiaohua Liu
Improved generative adversarial network (Improved GAN) is a successful method of using generative adversarial models to solve the problem of semi-supervised learning.
no code implementations • ICLR 2018 • Alan Do-Omri, Dalei Wu, Xiaohua Liu
In this work, we combine these two ideas and make GANs self-trainable for semi-supervised learning tasks by exploiting their infinite data generation potential.
1 code implementation • ACL 2017 • Hao Zhou, Zhaopeng Tu, Shu-Jian Huang, Xiaohua Liu, Hang Li, Jia-Jun Chen
In typical neural machine translation~(NMT), the decoder generates a sentence word by word, packing all linguistic granularities in the same time-scale of RNN.
1 code implementation • 7 Nov 2016 • Zhaopeng Tu, Yang Liu, Lifeng Shang, Xiaohua Liu, Hang Li
Although end-to-end Neural Machine Translation (NMT) has achieved remarkable progress in the past two years, it suffers from a major drawback: translations generated by NMT systems often lack of adequacy.
2 code implementations • TACL 2017 • Zhaopeng Tu, Yang Liu, Zhengdong Lu, Xiaohua Liu, Hang Li
In neural machine translation (NMT), generation of a target word depends on both source and target contexts.
3 code implementations • ACL 2016 • Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, Hang Li
Attention mechanism has enhanced state-of-the-art Neural Machine Translation (NMT) by jointly learning to align and translate.