activity
20152018
most citedImproving Review Representations with User Attention and Product Attention for Sentiment Classification

39 citations · 115 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CL201839 cited

Improving Review Representations with User Attention and Product Attention for Sentiment Classification

Zhen Wu, Xin-Yu Dai, Cunyan Yin +2

Neural network methods have achieved great success in reviews sentiment classification. Recently, some works achieved improvement by incorporating user and product information to g…

cs.CL20172 cited

Modeling Past and Future for Neural Machine Translation

Zaixiang Zheng, Hao Zhou, Shujian Huang +4

Existing neural machine translation systems do not explicitly model what has been translated and what has not during the decoding phase. To address this problem, we propose a novel…

cs.CL20171 cited

Dynamic Oracle for Neural Machine Translation in Decoding Phase

Zi-Yi Dou, Hao Zhou, Shu-Jian Huang +2

The past several years have witnessed the rapid progress of end-to-end Neural Machine Translation (NMT). However, there exists discrepancy between training and inference in NMT whe…

cs.CL201711 cited

Neural Machine Translation with Word Predictions

Rongxiang Weng, Shujian Huang, Zaixiang Zheng +2

In the encoder-decoder architecture for neural machine translation (NMT), the hidden states of the recurrent structures in the encoder and decoder carry the crucial information abo…

cs.CL2017

Top-Rank Enhanced Listwise Optimization for Statistical Machine Translation

Huadong Chen, Shujian Huang, David Chiang +2

Pairwise ranking methods are the basis of many widely used discriminative training approaches for structure prediction problems in natural language processing(NLP). Decomposing the…

cs.CL201733 cited

Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder

Huadong Chen, Shujian Huang, David Chiang +1

Most neural machine translation (NMT) models are based on the sequential encoder-decoder framework, which makes no use of syntactic information. In this paper, we improve this mode…