{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:03:42Z","timestamp":1776128622003,"version":"3.50.1"},"reference-count":164,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2018,8,1]],"date-time":"2018-08-01T00:00:00Z","timestamp":1533081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Comput. Intell. Mag."],"published-print":{"date-parts":[[2018,8]]},"DOI":"10.1109\/mci.2018.2840738","type":"journal-article","created":{"date-parts":[[2018,7,20]],"date-time":"2018-07-20T20:35:20Z","timestamp":1532118920000},"page":"55-75","source":"Crossref","is-referenced-by-count":2644,"title":["Recent Trends in Deep Learning Based Natural Language Processing [Review Article]"],"prefix":"10.1109","volume":"13","author":[{"given":"Tom","family":"Young","sequence":"first","affiliation":[]},{"given":"Devamanyu","family":"Hazarika","sequence":"additional","affiliation":[]},{"given":"Soujanya","family":"Poria","sequence":"additional","affiliation":[]},{"given":"Erik","family":"Cambria","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/W17-2810","article-title":"Are distributional representations ready for the real world? Evaluating word vectors for grounded perceptual meaning","author":"lucy","year":"2017"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1010"},{"key":"ref33","first-page":"1236","article-title":"Joint learning of character and word embeddings","author":"chen","year":"0","journal-title":"Proc Int Joint Conf Artificial Intelligence"},{"key":"ref32","first-page":"171","article-title":"Label embedding for zero-shot fine-grained named entity typing","author":"ma","year":"0","journal-title":"Proc Int Conf Computational Linguistics"},{"key":"ref31","first-page":"1818","article-title":"Learning character-level representations for part-of-speech tagging","author":"santos","year":"0","journal-title":"Proc 31st Int Conf Mach Learn"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W15-3904"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1030"},{"key":"ref36","article-title":"Enriching word vectors with subword information","author":"bojanowski","year":"2016"},{"key":"ref35","first-page":"347","article-title":"Radical-based hierarchical embeddings for chinese sentiment analysis at sentence level","author":"peng","year":"0","journal-title":"Proc Int Florida Artif Intell Res Soc Conf"},{"key":"ref34","first-page":"647","article-title":"Deep learning for chinese word segmentation and pos tagging","author":"zheng","year":"0","journal-title":"Proc Conf Empirical Methods Natural Language Processing"},{"key":"ref28","first-page":"2741","article-title":"Character-aware neural language models","author":"kim","year":"0","journal-title":"Association for the Advancement of Artificial Intelligence"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-2613"},{"key":"ref29","first-page":"69","article-title":"Deep convolutional neural networks for sentiment analysis of short texts","author":"dos santos","year":"0","journal-title":"Proc Int Conf Computational Linguistics"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1007"},{"key":"ref22","first-page":"919","article-title":"Semi-supervised convolutional neural networks for text categorization via region embedding","author":"johnson","year":"0","journal-title":"Proc Advances Neural Information Processing Systems"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1130"},{"key":"ref23","first-page":"151","article-title":"Semi-supervised recursive autoencoders for predicting sentiment distributions","author":"socher","year":"0","journal-title":"Proc Conf Empirical Methods Natural Language Processing"},{"key":"ref101","first-page":"5876","article-title":"Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM","author":"ma","year":"0","journal-title":"Association for the Advancement of Artificial Intelligence"},{"key":"ref26","first-page":"489","article-title":"Re-embedding words","author":"labutov","year":"0","journal-title":"Proceedings of the 17th annual meeting on Association for Computational Linguistics -"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1058"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-1146"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-2058"},{"key":"ref51","first-page":"1601","article-title":"A deeper look into sarcastic tweets using deep convolutional neural networks","author":"poria","year":"0","journal-title":"Proc Int Conf Computational Linguistics"},{"key":"ref154","first-page":"165","article-title":"Open question answering with weakly supervised embedding models","author":"bordes","year":"0","journal-title":"Proc Eur Conf Mach Learn Knowl Discovery Databases"},{"key":"ref153","first-page":"1608","article-title":"Paraphrase-driven learning for open question answering","author":"fader","year":"0","journal-title":"Proceedings of the 17th annual meeting on Association for Computational Linguistics -"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/N15-1020"},{"key":"ref155","first-page":"583","article-title":"Data-driven response generation in social media","author":"ritter","year":"0","journal-title":"Proc Conf Empirical Methods Natural Language Processing"},{"key":"ref150","first-page":"2377","article-title":"Training very deep networks","author":"srivastava","year":"0","journal-title":"Proc Advances Neural Information Processing Systems"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2016.94"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.3115\/1219840.1219855"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1056"},{"key":"ref147","article-title":"Google&#x2019;s neural machine translation system: Bridging the gap between human and machine translation","author":"wu","year":"2016"},{"key":"ref148","article-title":"Convolutional sequence to sequence learning","author":"gehring","year":"2017"},{"key":"ref149","article-title":"Attention is all you need","author":"vaswani","year":"2017"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2014.2339736"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1017"},{"key":"ref57","article-title":"Modeling relational information in question-answer pairs with convolutional neural networks","author":"severyn","year":"2016"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1026"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-2105"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/2661829.2661935"},{"key":"ref53","first-page":"2042","article-title":"Convolutional neural network architectures for matching natural language sentences","author":"hu","year":"0","journal-title":"Proc Advances Neural Information Processing Systems"},{"key":"ref52","first-page":"1601","article-title":"Modelling, visualising and summarising documents with a single convolutional neural network","author":"denil","year":"0","journal-title":"26th Int Conf Computational Linguistics"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref164","article-title":"Multimodal machine learning: A survey and taxonomy","author":"baltru\u0161aitis","year":"2017"},{"key":"ref163","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref162","first-page":"3776","article-title":"Building end-to-end dialogue systems using generative hierarchical neural network models","author":"serban","year":"0","journal-title":"Association for the Advancement of Artificial Intelligence"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.3115\/1073083.1073135"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.21236\/ADA461156"},{"key":"ref4","first-page":"1631","article-title":"Recursive deep models for semantic compositionality over a sentiment treebank","author":"socher","year":"0","journal-title":"Proc Conf Empirical Methods Natural Language Processing"},{"key":"ref3","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","author":"mikolov","year":"0","journal-title":"Proc Advances Neural Information Processing Systems"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1613\/jair.4992"},{"key":"ref5","first-page":"2493","article-title":"Natural language processing (almost) from scratch","volume":"12","author":"collobert","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1036"},{"key":"ref8","article-title":"Efficient estimation of word representations in vector space","author":"mikolov","year":"2013"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S16-1053"},{"key":"ref7","first-page":"1137","article-title":"A neural probabilistic language model","volume":"3","author":"bengio","year":"2003","journal-title":"J Mach Learn Res"},{"key":"ref157","article-title":"A diversity-promoting objective function for neural conversation models","author":"li","year":"2015"},{"key":"ref158","article-title":"Evaluating prerequisite qualities for learning end-to-end dialog systems","author":"dodge","year":"2015"},{"key":"ref9","first-page":"2764","article-title":"Wsabie: Scaling up to large vocabulary image annotation","volume":"11","author":"weston","year":"0","journal-title":"Proc Int Joint Conf Artificial Intelligence"},{"key":"ref46","article-title":"Efficient likelihood learning of a generic CNN-CRF model for semantic segmentation","author":"kirillov","year":"2015"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.06.009"},{"key":"ref48","first-page":"339","article-title":"Aspect extraction through semi-supervised modeling","author":"mukherjee","year":"0","journal-title":"Proceedings annual meeting of the Association for Computational Linguistics"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/29.21701"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"ref41","first-page":"806","article-title":"CNN features off-the-shelf: An astounding baseline for recognition","author":"sharif razavian","year":"0","journal-title":"Proc IEEE Conf Computer Vision and Pattern Recognition Workshops"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-1062"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1231"},{"key":"ref126","article-title":"Bidirectional LSTM-CRF models for sequence tagging","author":"huang","year":"2015"},{"key":"ref125","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1075\/cilt.260.17gim","article-title":"Fast and accurate part-of-speech tagging: The SVM approach revisited","author":"gim\u00e9nez","year":"2004","journal-title":"Recent Advances in Natural Language Processing III"},{"key":"ref124","article-title":"Adversarial generation of natural language","author":"rajeswar","year":"2017"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1167"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298932"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1082"},{"key":"ref71","article-title":"Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition","author":"sak","year":"2014"},{"key":"ref128","first-page":"1378","article-title":"Ask me anything: Dynamic memory networks for natural language processing","author":"kumar","year":"0","journal-title":"Proc Int Conf Machine Learning"},{"key":"ref70","first-page":"1764","article-title":"Towards end-to-end speech recognition with recurrent neural networks","author":"graves","year":"0","journal-title":"Proc 31st Int Conf Mach Learn"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966144"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1032"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1081"},{"key":"ref74","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"chung","year":"2014"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref133","first-page":"433","article-title":"Learning accurate, compact, and interpretable tree annotation","author":"petrov","year":"0","journal-title":"Proc 21st Int Conf Computational Linguistics"},{"key":"ref134","first-page":"434","article-title":"Fast and accurate shift-reduce constituent parsing","author":"zhu","year":"0","journal-title":"Proceedings of the 17th annual meeting on Association for Computational Linguistics -"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1033"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1115"},{"key":"ref132","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1613\/jair.5259","article-title":"A neural probabilistic structured-prediction method for transition-based natural language processing","volume":"58","author":"zhou","year":"2017","journal-title":"J Artif Intell Res"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1142"},{"key":"ref136","first-page":"2440","article-title":"End-to-end memory networks","author":"sukhbaatar","year":"0","journal-title":"Proc Advances Neural Information Processing Systems"},{"key":"ref135","article-title":"Memory networks","author":"weston","year":"2014"},{"key":"ref138","doi-asserted-by":"crossref","DOI":"10.3115\/v1\/W14-1609","article-title":"Lexicon infused phrase embeddings for named entity resolution","author":"passos","year":"2014"},{"key":"ref137","first-page":"2397","article-title":"Dynamic memory networks for visual and textual question answering","author":"xiong","year":"0","journal-title":"Proc Int Conf Machine Learning"},{"key":"ref60","author":"palaz","year":"2015","journal-title":"Analysis of CNN-based speech recognition system using raw speech as input"},{"key":"ref139","article-title":"Named entity recognition with bidirectional LSTM-CNNs","author":"chiu","year":"2015"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/0364-0213(90)90002-E"},{"key":"ref61","article-title":"Context-dependent translation selection using convolutional neural network","author":"tu","year":"2015"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2011.5947611"},{"key":"ref64","first-page":"1017","article-title":"Generating text with recurrent neural networks","author":"sutskever","year":"0","journal-title":"Proc 28th Int Conf Machine Learning"},{"key":"ref140","first-page":"879","article-title":"Joint named entity recognition and disambiguation","author":"luo","year":"0","journal-title":"Proc Conf Empirical Methods Natural Language Processing"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-1140"},{"key":"ref141","article-title":"Fast and accurate sequence labeling with iterated dilated convolutions","author":"strubell","year":"2017"},{"key":"ref66","first-page":"1044","article-title":"Joint language and translation modeling with recurrent neural networks","author":"auli","year":"0","journal-title":"Proc Conf Empirical Methods Natural Language Processing"},{"key":"ref142","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1162\/tacl_a_00120","article-title":"Efficient inference and structured learning for semantic role labeling","volume":"3","author":"t\u00e4ckstr\u00f6m","year":"2015","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"ref67","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","author":"sutskever","year":"0","journal-title":"Proc Advances Neural Information Processing Systems"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1109"},{"key":"ref68","first-page":"233","article-title":"The use of recurrent neural networks in continuous speech recognition","author":"robinson","year":"0","journal-title":"Proc Automatic Speech and Speaker Recognition"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1044"},{"key":"ref2","first-page":"3","article-title":"Recurrent neural network based language model","volume":"2","author":"mikolov","year":"0","journal-title":"Proc INTERSPEECH"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref145","first-page":"1188","article-title":"Distributed representations of sentences and documents","author":"le","year":"0","journal-title":"Proc 31st Int Conf Mach Learn"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2014.2307227"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992696"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1044"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1127"},{"key":"ref94","article-title":"Neural machine translation by jointly learning to align and translate","author":"bahdanau","year":"2014"},{"key":"ref107","article-title":"Sequence level training with recurrent neural networks","author":"ranzato","year":"2015"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.9"},{"key":"ref106","first-page":"1171","article-title":"Scheduled sampling for sequence prediction with recurrent neural networks","author":"bengio","year":"0","journal-title":"Proc Advances Neural Information Processing Systems"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1094"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1150"},{"key":"ref91","article-title":"A neural conversational model","author":"vinyals","year":"2015"},{"key":"ref104","article-title":"Recursive neural networks can learn logical semantics","author":"bowman","year":"2014"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"ref103","first-page":"25","article-title":"Max-margin Markov networks","author":"taskar","year":"0","journal-title":"Proc Advances Neural Information Processing Systems"},{"key":"ref102","first-page":"1201","article-title":"Semantic compositionality through recursive matrix-vector spaces","author":"socher","year":"0","journal-title":"Proc Joint Conf Empirical Methods in Natural Language Processing and Computational Natural Language Learning"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2012.2225812"},{"key":"ref112","first-page":"2007","article-title":"Learning from real users: Rating dialogue success with neural networks for reinforcement learning in spoken dialogue systems","author":"su","year":"0","journal-title":"Proc Interspeech Conf"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2009.04.001"},{"key":"ref98","first-page":"2692","article-title":"Pointer networks","author":"vinyals","year":"0","journal-title":"Proc Advances Neural Information Processing Systems"},{"key":"ref99","article-title":"A deep reinforced model for abstractive summarization","author":"paulus","year":"2017"},{"key":"ref96","first-page":"2048","article-title":"Show, attend and tell: Neural image caption generation with visual attention","author":"xu","year":"0","journal-title":"Proc Int Conf Machine Learning"},{"key":"ref97","first-page":"2773","article-title":"Grammar as a foreign language","author":"vinyals","year":"0","journal-title":"Proc Advances Neural Information Processing Systems"},{"key":"ref10","first-page":"129","article-title":"Parsing natural scenes and natural language with recursive neural networks","author":"socher","year":"0","journal-title":"Proc 28th Int Conf Mach Learn"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1613\/jair.2934"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2017.4531228"},{"key":"ref13","first-page":"513","article-title":"Domain adaptation for large-scale sentiment classification: A deep learning approach","author":"glorot","year":"0","journal-title":"Proc 28th Int Conf Machine Learning"},{"key":"ref14","first-page":"894","article-title":"The role of syntax in vector space models of compositional semantics","volume":"1","author":"hermann","year":"0","journal-title":"Proceedings annual meeting of the Association for Computational Linguistics"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/BF00114844"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.21236\/ADA164453"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1006\/jmla.2000.2714"},{"key":"ref82","article-title":"Comparative study of CNN and RNN for natural language processing","author":"yin","year":"2017"},{"key":"ref117","first-page":"3079","article-title":"Semi-supervised sequence learning","author":"dai","year":"0","journal-title":"Proc Advances Neural Information Processing Systems"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1002\/aris.1440380105"},{"key":"ref81","article-title":"Language modeling with gated convolutional networks","author":"dauphin","year":"2016"},{"key":"ref18","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"blei","year":"2003","journal-title":"J Mach Learn Res"},{"key":"ref84","first-page":"850","article-title":"Learning to forget: Continual prediction with LSTM","author":"gers","year":"0","journal-title":"Proc 9th Int Conf Artificial Neural Networks"},{"key":"ref119","article-title":"Auto-encoding variational bayes","author":"kingma","year":"2013"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390177"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref114","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"0","journal-title":"Proc Advances Neural Information Processing Systems"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1230"},{"key":"ref116","first-page":"3294","article-title":"Skip-thought vectors","author":"kiros","year":"0","journal-title":"Proc Advances Neural Information Processing Systems"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfranklin.2017.06.007"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1230"},{"key":"ref120","article-title":"Generating sentences from a continuous space","author":"bowman","year":"2015"},{"key":"ref89","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/W15-4640","article-title":"The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems","author":"lowe","year":"2015"},{"key":"ref121","article-title":"Generating text via adversarial training","author":"zhang","year":"0","journal-title":"Proc Neural Information Processing Systems Workshop Adversarial Training"},{"key":"ref122","article-title":"Controllable text generation","author":"hu","year":"2017"},{"key":"ref123","first-page":"2852","article-title":"Seqgan: sequence generative adversarial nets with policy gradient","author":"yu","year":"0","journal-title":"Association for the Advancement of Artificial Intelligence"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1030"},{"key":"ref86","article-title":"Generating sequences with recurrent neural networks","author":"graves","year":"2013"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2015.2400218"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1003"}],"container-title":["IEEE Computational Intelligence Magazine"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10207\/8416963\/08416973.pdf?arnumber=8416973","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T12:15:15Z","timestamp":1643199315000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8416973\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":164,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/mci.2018.2840738","relation":{},"ISSN":["1556-603X","1556-6048"],"issn-type":[{"value":"1556-603X","type":"print"},{"value":"1556-6048","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8]]}}}