计算机技术 |
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基于LSTM与衰减自注意力的答案选择模型 |
陈巧红(),李妃玉,孙麒,贾宇波 |
浙江理工大学 计算机科学与技术学院,浙江 杭州 310018 |
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Answer selection model based on LSTM and decay self-attention |
Qiao-hong CHEN(),Fei-yu LI,Qi SUN,Yu-bo JIA |
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China |
引用本文:
陈巧红,李妃玉,孙麒,贾宇波. 基于LSTM与衰减自注意力的答案选择模型[J]. 浙江大学学报(工学版), 2022, 56(12): 2436-2444.
Qiao-hong CHEN,Fei-yu LI,Qi SUN,Yu-bo JIA. Answer selection model based on LSTM and decay self-attention. Journal of ZheJiang University (Engineering Science), 2022, 56(12): 2436-2444.
链接本文:
https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.12.012
或
https://www.zjujournals.com/eng/CN/Y2022/V56/I12/2436
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1 |
ZHANG Y T, LU W P, OU W H, et al Chinese medical question answer selection via hybrid models based on CNN and GRU[J]. Multimedia Tools and Applications, 2020, 79: 14751- 14776
doi: 10.1007/s11042-019-7240-1
|
2 |
LIU D L, NIU Z D, ZHANG C X, et al Multi-scale deformable CNN for answer selection[J]. IEEE Access, 2019, 7: 164986- 164995
doi: 10.1109/ACCESS.2019.2953219
|
3 |
李超凡, 陈羽中 一种用于答案选择的知识增强混合神经网络[J]. 小型微型计算机系统, 2021, 42 (10): 2065- 2073 LI Chao-Fan, CHEN Yu-Zhong Knowledge-enhanced hybrid neural network for answer selection[J]. Journal of Chinese Computer Systems, 2021, 42 (10): 2065- 2073
doi: 10.3969/j.issn.1000-1220.2021.10.009
|
4 |
WAKCHAURE M, KULKARNI P. A scheme of answer selection in community question answering using machine learning techniques [C]// 2019 International Conference on Intelligent Computing and Control Systems. Madurai: IEEE, 2019: 879-883.
|
5 |
MA W, LOU J, JI C, et al ACLSTM: a novel method for CQA answer quality prediction based on question-answer joint learning[J]. Computers, Materials and Continua, 2021, 66 (1): 179- 193
|
6 |
石磊, 王毅, 成颖, 等 自然语言处理中的注意力机制研究综述[J]. 数据分析与知识发现, 2020, 41 (5): 1- 14 SHI Lei, WANG Yi, CHENG Ying, et al Review of attention mechanism in natural language processing[J]. Data Analysis and Knowledge Discovery, 2020, 41 (5): 1- 14
|
7 |
YU A W, DOHAN D, LUONG M T, et al. QANet: combining local convolution with global self-attention for reading comprehension [EB/OL]. [2021-01-29]. https://arxiv.org/pdf/1804.09541.pdf.
|
8 |
CHEN X C, YANG Z Y, LIANG N Y, et al Co-attention fusion based deep neural network for Chinese medical answer selection[J]. Applied Intelligence, 2021, 51: 6633- 6646
doi: 10.1007/s10489-021-02212-w
|
9 |
TAY Y , TUAN L A , HUI S C . Multi-cast attention networks for retrieval-based question answering and response prediction [EB/OL]. [2022-01-07]. https://arxiv.org/pdf/1806.00778.pdf.
|
10 |
BAO G C, WEI Y, SUN X, et al Double attention recurrent convolution neural network for answer selection[J]. Royal Society Open Science, 2020, 7: 191517
doi: 10.1098/rsos.191517
|
11 |
江龙泉. 基于Attentive LSTM网络模型的答案匹配技术的研究[D]. 上海: 上海师范大学, 2018. JIANG Long-quan. Research on answer matching technology based on Attentive LSTM network model [D]. Shanghai: Shanghai Normal University, 2018.
|
12 |
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [EB/OL]. [2022-01-07]. https://arxiv.org/pdf/1706.03762.pdf.
|
13 |
YU A W, DOHAN D, LUONG M T, et al. QANet: combining local convolution with global self-attention for reading comprehension [EB/OL]. [2022-01-07]. https://arxiv.org/pdf/1804.09541.pdf.
|
14 |
SHAO T H, GUO Y P, CHEN H H, et al Transformer-based neural network for answer selection in question answering[J]. IEEE Access, 2019, 7: 26146- 26156
doi: 10.1109/ACCESS.2019.2900753
|
15 |
PETER M E, NEUMANN M, IYYER M, et al. Deep contextualized word representations [C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics. [S.l.]: ACL, 2018: 2227–2237.
|
16 |
RADFORD A, NARASIMHAN K, SALIMANS T, et al. Improving language understanding by generative pre-training [R/OL]. [2022-01-07]. https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
|
17 |
DEBLIN J, CHANG M W, LEE K, et al. Bert: pre-training of deep bidirectional transformers for language understanding [C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics. [S. l.]: ACL, 2019: 4171–4186.
|
18 |
BROMLEY J, BENTZ J W, BOTTOU L, et al Signature verification using a “Siamese” time delay neural network[J]. International Journal of Pattern Recognition and Artificial Intelligence, 1993, 7 (4): 669- 688
doi: 10.1142/S0218001493000339
|
19 |
HOCHREITER S, SCHMIDHUB J Long short-term memory[J]. Neural Computation, 1997, 9 (8): 1735- 1780
doi: 10.1162/neco.1997.9.8.1735
|
20 |
俞海亮, 彭冬亮, 谷雨 结合双层多头自注意力和BiLSTM-CRF的军事武器实体识别[J]. 无线电工程, 2022, 52 (5): 775- 782 YU Hai-liang, PENG Dong-liang, GU Yu Military weapon entity recognition combined with double-layer multi-head self-attention and BiLSTM-CRF[J]. Radio Engineering, 2022, 52 (5): 775- 782
doi: 10.3969/j.issn.1003-3106.2022.05.011
|
21 |
BIAN W J, LI S, YANG Z, et al. A compare-aggregate model with dynamic-clip attention for answer selection [C]// Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. [S.l.]: ACM, 2017: 1987-1990.
|
22 |
YIN W P, SCHÜTZE H, XIANG B, et al ABCNN: attention-based convolutional neural network for modeling sentence pairs[J]. Transactions of the Association for Computational Linguistics, 2016, 4: 259- 272
doi: 10.1162/tacl_a_00097
|
23 |
LECUN Y, CHOPRA S, HADSELLl R, et al. A tutorial on energy-based learning [EB/OL]. [2022-01-07]. https://typeset.io/pdf/a-tutorial-on-energy-based-learning-2fj3lvviwy.pdf.
|
24 |
YANG Y, YIH W T, MEEK C. WikiQA: a challenge dataset for open-domain question answering [C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. [S. l.]: ACL, 2015: 2013-2018
|
25 |
WANG M Q, SMITH N A, MITAMURA T. What is the jeopardy model? A quasi-synchronous grammar for QA [C]// Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. [S. l.]: ACL, 2007: 22-32.
|
26 |
FENG M W, XIANG B, GLASS M R, et al. Applying deep learning to answer selection: a study and an open task [EB/OL]. [2022-01-07]. https://arxiv.org/pdf/1508.01585.pdf.
|
27 |
PENNINGTON J, SOCHER R, MANNING C. GloVe: global vectors for word representation [C]// Proceeding of the 2014 Conference on Empirical Methods in Natural Language Processing. [S. l.]: ACL, 2014: 1532-1543.
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