activity
20152022
most citedImproving Review Representations with User Attention and Product Attention for Sentiment Classification

39 citations · 201 across the 29 of their papers we have counts for

collaborators
Showing 2017Show all

7 papers · 1 filter

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…

cs.CL2017

Chunk-Based Bi-Scale Decoder for Neural Machine Translation

Hao Zhou, Zhaopeng Tu, Shujian Huang +3

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. In this paper, we pr…