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20152024
most citedImproving Review Representations with User Attention and Product Attention for Sentiment Classification

39 citations · 246 across the 42 of their papers we have counts for

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Showing 2019Show all

12 papers · 1 filter

cs.CL201930 cited

Non-autoregressive Transformer by Position Learning

Yu Bao, Hao Zhou, Jiangtao Feng +4

Non-autoregressive models are promising on various text generation tasks. Previous work hardly considers to explicitly model the positions of generated words. However, position mod…

cs.CV2019

The Detection of Distributional Discrepancy for Text Generation

Xingyuan Chen, Ping Cai, Peng Jin +4

The text generated by neural language models is not as good as the real text. This means that their distributions are different. Generative Adversarial Nets (GAN) are used to allev…

cs.CL20191 cited

Generating Diverse Translation by Manipulating Multi-Head Attention

Zewei Sun, Shujian Huang, Hao-Ran Wei +2

Transformer model has been widely used on machine translation tasks and obtained state-of-the-art results. In this paper, we report an interesting phenomenon in its encoder-decoder…

cs.CL2019

Multi-Perspective Inferrer: Reasoning Sentences Relationship from Holistic Perspective

Zhen Cheng, Zaixiang Zheng, Xin-Yu Dai +2

Natural Language Inference (NLI) aims to determine the logic relationships (i.e., entailment, neutral and contradiction) between a pair of premise and hypothesis. Recently, the ali…

cs.CL2019

A Reinforced Generation of Adversarial Examples for Neural Machine Translation

Wei Zou, Shujian Huang, Jun Xie +2

Neural machine translation systems tend to fail on less decent inputs despite its significant efficacy, which may significantly harm the credibility of this systems-fathoming how a…

cs.CL2019

Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation

Huiyun Yang, Shujian Huang, Xinyu Dai +1

In sequence labeling, previous domain adaptation methods focus on the adaptation from the source domain to the entire target domain without considering the diversity of individual…