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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (5): 1-14    DOI: 10.11925/infotech.2096-3467.2019.1317
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Review of Attention Mechanism in Natural Language Processing
Shi Lei1,Wang Yi2,Cheng Ying2,3,Wei Ruibin1()
1School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu 233030, China
2School of Information Management, Nanjing University, Nanjing 210023, China
3School of Chinese Language and Literature, Shandong Normal University, Jinan 250014, China
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Abstract  

[Objective] This paper summarizes the evolution and application of attention mechanism in natural language processing.[Coverage] We searched “attention” with the title/topic fields of WoS, ACM Digital Library, arXiv and CNKI from January 2015 to October 2019. Then, we manually screened the topic literature in the field of natural language processing, and obtained 68 related papers.[Methods] We first summarized the general attention mechanism, and sorted out its derivations. Second, we thoroughly reviewed their applications in natural language processing tasks.[Results] The application of attention mechanism in natural language processing focused on sequence labeling, text classification, reasoning and generative tasks. There were adaptation rules between tasks and the various attention mechanisms.[Limitations] Some adaptations between the mechanisms and the tasks were obtained from the overall performance of the model. More research is needed to examine the performance of different mechanisms.[Conclusions] The study of attention mechanism has effectively promoted the development of natural language processing. However, the mechanism of action is not yet clear. Future research should focus on making attention mechanism closer to those of the human beings.

Key wordsAttention Mechanism      Self-Attention      Machine Translation      Machine Reading ComprehensionSentiment Analysis     
Received: 10 December 2019      Published: 15 June 2020
ZTFLH:  TP391.1  
Corresponding Authors: Wei Ruibin     E-mail: rbwxy@126.com

Cite this article:

Shi Lei,Wang Yi,Cheng Ying,Wei Ruibin. Review of Attention Mechanism in Natural Language Processing. Data Analysis and Knowledge Discovery, 2020, 4(5): 1-14.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.1317     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I5/1

Schematic Diagram of NMT Model with Attention Mechanism
The General Form of Attention Mechanism
注意力 关注范围
全局注意力 全部元素
局部注意力 以对齐位置为中心的窗口
硬注意力 一个元素
稀疏注意力 稀疏分布的部分元素
结构注意力 结构上相关的一系列元素
Classification of Attention Mechanism by Attention Range
作者 模型 情感极性准确率(%) 注意力
Restaurant Laptop Twitter
Wang等[32] LSTM 74.3 66.5 66.5
Tang等[33] TD-LSTM 75.6 68.1 70.8 语境化注意力
Wang等[32] ATAE-LSTM 77.2 68.7 - 方面嵌入注意力
Ma等[21] IAN 78.6 72.1 - 粗粒度交互注意力
Liu等[34] BiLSTM-ATT-G 79.7 73.1 70.4 语境化注意力
Huang等[35] AOA-LSTM 81.2 74.5 - 细粒度双向注意力
Fan等[36] MGAN 81.2 75.4 72.5 多粒度双向注意力
Zheng等[37] LCR-Rot 81.3 75.2 72.7 语境化粗粒度双向注意力
Li等[38] HAPN 82.2 77.3 - 层级注意力
Song等[39] AEN-BERT 83.1 80.0 74.7 多头自注意力网络
The Performance of Aspect-Level Sentiment Analysis Models
作者 模型 Exact Match(%) F1(%) 注意力
Wang等[43] Match-LSTM 64.7 73.7
Xiong等[44] DCN 66.2 75.9 协同注意力
Seo等[17] BiDAF 68.0 77.3 双向注意力
Gong等[45] Ruminating Reader 70.6 79.5 双向多跳注意力
Wang等[42] R-Net 72.3 80.7 Self-Matching注意力
Peters等[46] BiDAF+Self-Attention 72.1 81.1 双向注意力+自注意力
Liu等[47] PhaseCond 72.6 81.4 K2Q+自注意力
Yu等[48] QANet 76.2 84.6 协同注意力+自注意力
Wang等[49] SLQA+ 80.4 87.0 协同注意力+自注意力
The Performance of Machine Reading Comprehension Models on SQuAD
作者 模型 训练集准确率(%) 测试集准确率(%) 注意力
Bowman等[50] 300D LSTM Encoders 83.9 80.6
Rocktaschel等[19] 100D LSTM with Attention 85.3 83.5 双路注意力
Lin等[27] 300D Structured Self-Attentive Sentence Embedding - 84.4 自注意力
Shen等[28] 300D Directional Self-Attention Network (DiSAN) 91.1 85.6 定向自注意力
Cheng等[22] 300D LSTMN Deep Fusion - 85.7 互注意力+内部注意力
Im等[51] 300D Distance-based Self-Attention Network 89.6 86.3 定向+距离自注意力
Shen等[52] 300D ReSAN 92.6 86.3 软硬混合自注意力
Parikh等[53] 300D Intra-Sentence Attention 90.5 86.8 互注意力+内部注意力
Tay等[54] 300D CAFE (AVGMAX+300D HN) 89.8 88.5 互注意力+内部注意力
The Performance of NLI Models on SNLI
作者 模型 网络 BLEU(%) 训练开销(FLOPs)
英-德 英-法 英-德 英-法
Wu等[59] GNMT+RL LSTM 24.6 39.92 2.3×1019 1.4×1020
GNMT+RL(ensemble) 26.3 41.16 1.8×1020 1.1×1021
Gehring等[60] ConvS2S CNN 25.16 40.46 9.6×1018 1.5×1020
ConvS2S(ensemble) 26.36 41.29 7.7×1019 1.2×1021
Vaswani等[6] Transformer(big) 多头自注意力 28.4 41 2.3×1019
The Performance of NMT Models on WMT14
作者 语料集 注意力 ROUGE-1(%) ROUGE-2(%) ROUGE-L(%)
Nallapati等[15] CNN/Daily Mail 全局注意力 32.49 11.84 29.47
平均文档/摘要词数:766/53 层级注意力(词-句) 32.75 12.21 29.01
Cohan等[57] arXiv 全局注意力 32.06 9.04 25.16
平均文档/摘要词数:4 938/220 层级注意力(词-语篇) 35.80 11.05 31.80
The Performance of Hierarchical Attention Mechanism on Some Abstractive Summarization Tasks
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