{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T22:25:30Z","timestamp":1770762330578,"version":"3.50.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T00:00:00Z","timestamp":1616284800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T00:00:00Z","timestamp":1616284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41706010"],"award-info":[{"award-number":["41706010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s12559-021-09854-5","type":"journal-article","created":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T09:02:21Z","timestamp":1616317341000},"page":"751-760","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Attention-Augmented Machine Memory"],"prefix":"10.1007","volume":"13","author":[{"given":"Xin","family":"Lin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2952-6642","authenticated-orcid":false,"given":"Guoqiang","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Kang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Qingyang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Kaizhu","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,21]]},"reference":[{"issue":"3","key":"9854_CR1","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1038\/nrn755","volume":"3","author":"M Corbetta","year":"2002","unstructured":"Corbetta M, Shulman GL. Control of Goal-Directed and Stimulus-Driven Attention in the Brain. Nat Rev Neurosci. 2002;3(3):201\u201315.","journal-title":"Nat Rev Neurosci"},{"key":"9854_CR2","unstructured":"Posner MI. Cognitive Neuroscience of Attention. Guilford Press; 2011."},{"key":"9854_CR3","unstructured":"Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. In: ICML. 2015. pp. 2048\u20132057."},{"issue":"4","key":"9854_CR4","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1007\/s12559-014-9312-x","volume":"7","author":"F Gao","year":"2015","unstructured":"Gao F, Zhang Y, Wang J, Sun J, Yang E, Hussain A. Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach. Cogn Comput. 2015;7(4):434\u201344.","journal-title":"Cogn Comput"},{"key":"9854_CR5","doi-asserted-by":"crossref","unstructured":"Hinton G, Salakhutdinov R. Reducing the Dimensionality of Data with Neural Networks. Science. 2006;313:","DOI":"10.1126\/science.1127647"},{"key":"9854_CR6","doi-asserted-by":"crossref","unstructured":"Shen T, Zhou T, Long G, Jiang J, Pan S, Zhang C. DiSAN: Directional Self-Attention Network for RNN\/CNN-Free Language Understanding. In: AAAI. 2018. pp. 5446\u20135455.","DOI":"10.1609\/aaai.v32i1.11941"},{"key":"9854_CR7","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I. Attention Is All You Need. In: NIPS. 2017. pp. 6000\u20136010."},{"key":"9854_CR8","doi-asserted-by":"crossref","unstructured":"Luong T, Pham H, Manning CD. Effective approaches to attention-based neural machine translation. In: EMNLP. 2015. pp. 1412\u20131421.","DOI":"10.18653\/v1\/D15-1166"},{"key":"9854_CR9","unstructured":"Chung J, G\u00fcl\u00e7ehre \u00c7, Cho K, Bengio Y. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. 2014. CoRR abs\/1412.3555."},{"issue":"8","key":"9854_CR10","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural computation. 1997;9(8):1735\u201380.","journal-title":"Neural computation"},{"key":"9854_CR11","unstructured":"Lin Z, Feng M, dos Santos CN, Yu M, Xiang B, Zhou B, Bengio Y. A Structured Self-Attentive Sentence Embedding. In: ICLR. 2017."},{"key":"9854_CR12","doi-asserted-by":"crossref","unstructured":"Zhong G, Lin X, Chen K, Li Q, Huang K. Long Short-Term Attention. In: BICS. 2019. pp. 45\u201354.","DOI":"10.1007\/978-3-030-39431-8_5"},{"key":"9854_CR13","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.future.2020.08.005","volume":"115","author":"ME Basiri","year":"2021","unstructured":"Basiri ME, Nemati S, Abdar M, Cambria E, Acharya UR. ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis. Future Gener Comput Syst. 2021;115:279\u201394.","journal-title":"Future Gener Comput Syst"},{"issue":"11","key":"9854_CR14","doi-asserted-by":"publisher","first-page":"1875","DOI":"10.1109\/TMM.2015.2477044","volume":"17","author":"K Cho","year":"2015","unstructured":"Cho K, Courville A, Bengio Y. Describing Multimedia Content Using Attention-Based Encoder-Decoder Networks. IEEE Trans Multimedia. 2015;17(11):1875\u201386.","journal-title":"IEEE Trans Multimedia"},{"key":"9854_CR15","unstructured":"Sukhbaatar S, Weston J., Fergus, R., et\u00a0al. End-to-End Memory Networks. In: NIPS. 2015. pp. 2440\u20132448."},{"key":"9854_CR16","unstructured":"Weston J, Chopra S, Bordes A. Memory networks. In: Y.\u00a0Bengio, Y.\u00a0LeCun (eds.) ICLR. 2015."},{"key":"9854_CR17","unstructured":"Kim Y, Denton C, Hoang L, Rush AM. Structured Attention Networks. In: ICLR. 2017."},{"key":"9854_CR18","doi-asserted-by":"crossref","unstructured":"Hsu WT, Lin C, Lee M, Min K, Tang J, Sun M. A unified model for extractive and abstractive summarization using inconsistency loss. In: I.\u00a0Gurevych, Y.\u00a0Miyao (eds.) ACL. 2018. pp. 132\u2013141.","DOI":"10.18653\/v1\/P18-1013"},{"key":"9854_CR19","doi-asserted-by":"crossref","unstructured":"Gehrmann S, Deng Y, Rush AM. Bottom-up abstractive summarization. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium. 2018.\u00a0pp. 4098\u20134109.","DOI":"10.18653\/v1\/D18-1443"},{"key":"9854_CR20","unstructured":"Bahdanau D, Cho K, Bengio Y. Neural Machine Translation by Jointly Learning to Align and Translate. In: ICLR. 2015."},{"key":"9854_CR21","doi-asserted-by":"crossref","unstructured":"Cho K, van Merrienboer B, Bahdanau D, Bengio Y. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. In: Proceedings of SSST@EMNLP.\u00a02014.\u00a0pp. 103\u2013111.","DOI":"10.3115\/v1\/W14-4012"},{"key":"9854_CR22","doi-asserted-by":"crossref","unstructured":"Mikolov T, Karafi\u00e1t M, Burget L, \u010cernock\u1ef3 J, Khudanpur S. Recurrent Neural Network Based Language Model. In: INTERSPEECH. 2010.","DOI":"10.1109\/ICASSP.2011.5947611"},{"key":"9854_CR23","unstructured":"Sutskever I, Martens J, Hinton GE. Generating Text with Recurrent Neural Networks. In: ICML. 2011. pp. 1017\u20131024."},{"key":"9854_CR24","unstructured":"Graves A, Jaitly N. Towards End-to-End Speech Recognition with Recurrent Neural Networks. In: ICML. 2014. pp. 1764\u20131772."},{"key":"9854_CR25","doi-asserted-by":"crossref","unstructured":"Graves A, Mohamed AR, Hinton G. Speech Recognition with Deep Recurrent Neural Networks. In: ICASSP. 2013. pp. 6645\u20136649.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"9854_CR26","doi-asserted-by":"crossref","unstructured":"Donahue J, Anne\u00a0Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T. Long-Term Recurrent Convolutional Networks for Visual Recognition and Description. In: CVPR. 2015. pp. 2625\u20132634.","DOI":"10.21236\/ADA623249"},{"key":"9854_CR27","unstructured":"Li W, Shao W, Ji S, Cambria E. Bieru: Bidirectional emotional recurrent unit for conversational sentiment analysis. 2020. CoRR abs\/2006.00492."},{"key":"9854_CR28","unstructured":"Wang Y, Long M, Wang J, Gao Z, Philip SY. PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs. In: NIPS. 2017. pp. 879\u2013888."},{"key":"9854_CR29","unstructured":"He Z, Gao S, Xiao L, Liu D, He H, Barber D. Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning. In: NIPS. 2017. pp. 1\u201311."},{"key":"9854_CR30","unstructured":"Neil D, Pfeiffer M, Liu SC. Phased LSTM: Accelerating Recurrent Network Training for Long or Event-Based Sequences. In: NIPS. 2016. pp. 3882\u20133890."},{"key":"9854_CR31","unstructured":"Shi X, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. In: NIPS. 2015. pp. 802\u2013810."},{"key":"9854_CR32","doi-asserted-by":"crossref","unstructured":"Liu J, Wang G, Hu P, Duan L, Kot AC. Global Context-Aware Attention LSTM Networks for 3D Action Recognition. In: CVPR. 2017. pp. 3671\u20133680.","DOI":"10.1109\/CVPR.2017.391"},{"issue":"9","key":"9854_CR33","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1109\/TMM.2017.2729019","volume":"19","author":"L Gao","year":"2017","unstructured":"Gao L, Guo Z, Zhang H, Xu X, Shen HT. Video Captioning With Attention-Based LSTM and Semantic Consistency. IEEE Trans Multimedia. 2017;19(9):2045\u201355.","journal-title":"IEEE Trans Multimedia"},{"key":"9854_CR34","doi-asserted-by":"crossref","unstructured":"Li Y, Zhu Z, Kong D, Han H, Zhao Y. EA-LSTM: Evolutionary Attention-Based LSTM for Time Series Prediction. Knowl Based Syst. 2019;181:104785.","DOI":"10.1016\/j.knosys.2019.05.028"},{"key":"9854_CR35","doi-asserted-by":"crossref","unstructured":"Long X, Gan C, de\u00a0Melo G, Wu J, Liu X, Wen S. Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification. In: CVPR. 2018. pp. 7834\u20137843.","DOI":"10.1109\/CVPR.2018.00817"},{"key":"9854_CR36","doi-asserted-by":"crossref","unstructured":"Liu F, Zhou X, Wang T, Cao J, Wang Z, Wang H, Zhang Y. An Attention-based Hybrid LSTM-CNN Model for Arrhythmias Classification. In: IJCNN. 2019. pp. 1\u20138.","DOI":"10.1109\/IJCNN.2019.8852037"},{"issue":"4","key":"9854_CR37","first-page":"1","volume":"15","author":"Z Liu","year":"2019","unstructured":"Liu Z, Zhou W, Li H. AB-LSTM: Attention-based Bidirectional LSTM Model for Scene Text Detection. ACM Transactions on Multimedia Computing, Communications, and Applications. 2019;15(4):1\u201323.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications"},{"key":"9854_CR38","doi-asserted-by":"crossref","unstructured":"Guo Z, Gao L, Song J, Xu X, Shao, J., Shen, H.T. Attention-based LSTM with Semantic Consistency for Videos Captioning. In: ACMMM. 2016. pp. 357\u2013361.","DOI":"10.1145\/2964284.2967242"},{"key":"9854_CR39","unstructured":"LeCun Y, Cortes C, Burges C. MNIST Handwritten Digit Database. AT&T Labs [Online]. 2010;2."},{"key":"9854_CR40","unstructured":"Xiao H, Rasul K, Vollgraf R. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. 2017. CoRR abs\/1708.07747."},{"issue":"5\u20136","key":"9854_CR41","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","volume":"18","author":"A Graves","year":"2005","unstructured":"Graves A, Schmidhuber J. Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures. Neural Netw. 2005;18(5\u20136):602\u201310.","journal-title":"Neural Netw"},{"key":"9854_CR42","unstructured":"Moniz JRA, Krueger D. Nested LSTMs. In: ACML. 2017. pp. 530\u2013544."},{"issue":"2","key":"9854_CR43","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/MIS.2016.31","volume":"31","author":"E Cambria","year":"2016","unstructured":"Cambria E. Affective computing and sentiment analysis. IEEE Intell Syst. 2016;31(2):102\u20137.","journal-title":"IEEE Intell Syst."},{"key":"9854_CR44","doi-asserted-by":"crossref","unstructured":"Cambria E, Hussain A, Havasi C, Eckl C. Sentic computing: Exploitation of common sense for the development of emotion-sensitive systems. In: Development of Multimodal Interfaces: Active Listening and Synchrony, Second COST 2102 International Training School, Dublin, Ireland, March 23-27, 2009, Revised Selected Papers, Lecture Notes in Computer Science, vol. 5967. 2009. pp. 148\u2013156.","DOI":"10.1007\/978-3-642-12397-9_12"},{"key":"9854_CR45","doi-asserted-by":"crossref","unstructured":"Cambria E, Li Y, Xing FZ, Poria S, Kwok K. Senticnet 6: Ensemble application of symbolic and subsymbolic AI for sentiment analysis. In: M.\u00a0d\u2019Aquin, S.\u00a0Dietze, C.\u00a0Hauff, E.\u00a0Curry, P.\u00a0Cudr\u00e9-Mauroux (eds.) CIKM.\u00a0ACM 2020.\u00a0pp. 105\u2013114.","DOI":"10.1145\/3340531.3412003"},{"key":"9854_CR46","unstructured":"Dai AM, Le QV. Semi-supervised Sequence Learning. In: NIPS. 2015. pp. 3079\u20133087."},{"key":"9854_CR47","doi-asserted-by":"crossref","unstructured":"Dong L, Wei F, Tan C, Tang D, Zhou M, Xu K. Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification. In: ACL. 2014. pp. 49\u201354.","DOI":"10.3115\/v1\/P14-2009"},{"key":"9854_CR48","unstructured":"Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C. Learning Word Vectors for Sentiment Analysis. In: ACL-HLT. 2011. pp. 142\u2013150."},{"key":"9854_CR49","doi-asserted-by":"crossref","unstructured":"Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S. SemEval-2014 Task 4: Aspect Based Sentiment Analysis. In: SemEval@COLING. 2014. pp. 27\u201335.","DOI":"10.3115\/v1\/S14-2004"},{"key":"9854_CR50","doi-asserted-by":"crossref","unstructured":"Yan Y, Yin X, Li S, Yang M, Hao H. Learning Document Semantic Representation with Hybrid Deep Belief Network. Comp Int Neurosc. 2015.\u00a0650,527:1\u2013650,527:9","DOI":"10.1155\/2015\/650527"},{"key":"9854_CR51","doi-asserted-by":"crossref","unstructured":"Liu Q, Zhang H, Zeng Y, Huang Z, Wu Z. Content Attention Model for Aspect Based Sentiment Analysis. In: WWW. 2018. pp. 1023\u20131032.","DOI":"10.1145\/3178876.3186001"},{"issue":"4","key":"9854_CR52","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1007\/s12559-018-9549-x","volume":"10","author":"Y Ma","year":"2018","unstructured":"Ma Y, Peng H, Khan T, Cambria E, Hussain A. Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis. Cogn. Comput. 2018;10(4):639\u201350.","journal-title":"Cogn. Comput."},{"key":"9854_CR53","unstructured":"Van\u00a0Asch V. Macro-and micro-averaged evaluation measures [[basic draft]]. Belgium: CLiPS. 2013. pp. 1\u201327."},{"key":"9854_CR54","doi-asserted-by":"crossref","unstructured":"Tang D, Qin B, Liu T. Aspect Level Sentiment Classification with Deep Memory Network. In: EMNLP. 2016. pp. 214\u2013224.","DOI":"10.18653\/v1\/D16-1021"},{"key":"9854_CR55","doi-asserted-by":"crossref","unstructured":"Fan F, Feng Y, Zhao D. Multi-grained Attention Network for Aspect-Level Sentiment Classification. In: EMNLP. 2018. pp. 3433\u20133442.","DOI":"10.18653\/v1\/D18-1380"},{"key":"9854_CR56","doi-asserted-by":"crossref","unstructured":"Ma D, Li S, Zhang X, Wang H. Interactive Attention Networks for Aspect-Level Sentiment Classification. In: IJCAI. 2017. pp. 4068\u20134074.","DOI":"10.24963\/ijcai.2017\/568"},{"key":"9854_CR57","doi-asserted-by":"crossref","unstructured":"Chen P, Sun Z, Bing L, Yang W. Recurrent Attention Network on Memory for Aspect Sentiment Analysis. In: EMNLP. 2017. pp. 452\u2013461.","DOI":"10.18653\/v1\/D17-1047"},{"key":"9854_CR58","doi-asserted-by":"crossref","unstructured":"Wang Y, Huang M, Zhu X, Zhao L. Attention-based LSTM for Aspect-level Sentiment Classification. In: EMNLP. 2016. pp. 606\u2013615.","DOI":"10.18653\/v1\/D16-1058"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-021-09854-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-021-09854-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-021-09854-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T02:33:47Z","timestamp":1671676427000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-021-09854-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,21]]},"references-count":58,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["9854"],"URL":"https:\/\/doi.org\/10.1007\/s12559-021-09854-5","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,21]]},"assertion":[{"value":"24 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}]}}