Effective Approaches to Attention-based Neural Machine Translation

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545

BLEU: a method for automatic evaluation of machine translation

Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344

Sequence to Sequence Learning with Neural Networks

Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766

Neural Machine Translation by Jointly Learning to Align and Translate

Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248

Recurrent Models of Visual AttentionPreprint

Mnih, V., Heess, N., Graves, A., & Kavukcuoglu, K. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.6247 · Cited by 3,393

Statistical phrase-based translation

Koehn, P., Och, F. J., & Marcu, D. (2003) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1073445.1073462 · Cited by 3,265

Recurrent Neural Network RegularizationPreprint

Zaremba, W., Sutskever, I., & Vinyals, O. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.2329 · Cited by 3,174

Recurrent Continuous Translation Models

Kalchbrenner, N., & Blunsom, P. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1176 · Cited by 1,277

Statistical Phrase-Based Translation

Koehn, P., Och, F. J., & Marcu, D. (2003) · doi:10.21236/ada461156 · Cited by 970

The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009–2020

Alshaabi, T., Dewhurst, D. R., Minot, J. R., Arnold, M. V., Adams, J. L., Danforth, C. M., & Dodds, P. S. (2021) · EPJ Data Science · doi:10.1140/epjds/s13688-021-00271-0 · Cited by 92

Language-Guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning

Zhang, W., Ma, C., Wu, Q., & Yang, X. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3039522 · Cited by 75

Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network

Noor, F., Haq, S., Rakib, M., Ahmed, T., Jamal, Z., Siam, Z. S., Hasan, R. T., Adnan, M. S. G., Dewan, A. M., & Rahman, R. M. (2022) · Water · doi:10.3390/w14040612 · Cited by 72

Time series prediction of sea surface temperature based on BiLSTM model with attention mechanism

Zrira, N., Kamal-Idrissi, A., Farssi, R., & Khan, H. A. (2024) · Journal of Sea Research · doi:10.1016/j.seares.2024.102472 · Cited by 82

Anomaly Detection in Dynamic Graphs: A Comprehensive Survey

Ekle, O. A., & Eberle, W. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3669906 · Cited by 93

DialSQL: Dialogue Based Structured Query Generation

Gür, İ., Yavuz, S., Su, Y., & Yan, X. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1124 · Cited by 76

SYSTRAN's Pure Neural Machine Translation SystemsPreprint

Crego, J., Kim, J., Klein, G., Rebollo, A., Yang, K., Sénellart, J., Akhanov, E., Brunelle, P., Coquard, A., Deng, Y., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.05540 · Cited by 122

Multi-Scale Attentive Interaction Networks for Chinese Medical Question Answer Selection

Zhang, S., Zhang, X., Wang, H., Guo, L., & Liu, S. (2018) · IEEE Access · doi:10.1109/access.2018.2883637 · Cited by 85

Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems

Huang, C., Alexandropoulos, G. C., Zappone, A., Yuen, C., & Debbah, M. A. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761962 · Cited by 74

Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset

Zhang, T., Li, C., Li, P., Peng, Y., Kang, X., Jiang, C., Li, F., Zhu, X., Yao, D., Biswal, B. B., et al. (2020) · Entropy · doi:10.3390/e22080893 · Cited by 71

Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the “Big Data” Era

Siramshetty, V. B., Nguyễn, Ð.-T., Martinez, N. J., Southall, N. T., Simeonov, A. M., & Zakharov, A. V. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00884 · Cited by 74

ABL-TC: A lightweight design for network traffic classification empowered by deep learning

Wei, W., Gu, H., Deng, W., Xiao, Z., & Ren, X. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.03.007 · Cited by 72

Gloss Attention for Gloss-free Sign Language Translation

Yin, A., Zhong, T., Tang, L., Jin, W., Jin, T., & Zhao, Z. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00251 · Cited by 94

Deep learning for named entity recognition: a survey

Hu, Z., Hou, W. Z., & Liu, X. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09646-6 · Cited by 88

TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References

Zhang, Z., Chen, P., Sapkota, M., & Yang, L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_37 · Cited by 71

Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders

Wang, Y., Liu, C., Huang, M., & Nie, L. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1204 · Cited by 72

Task Agnostic Continual Learning Using Online Variational BayesPreprint

Zeno, C., Golan, I., Hoffer, E., & Soudry, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10123 · Cited by 72

Real-time Arm Skeleton Tracking and Gesture Inference Tolerant to Missing Wearable Sensors

Liu, Y., Li, Z., Liu, Z., & Wu, K. (2019) · Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3307334.3326109 · Cited by 71

ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking

Liu, S., Gao, C., Chen, S., Nie, L. Y., & Liu, Y. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.3038681 · Cited by 93

Dependency-to-Dependency Neural Machine Translation

Wu, S., Zhang, D., Zhang, Z., Yang, N., Li, M., & Zhou, M. Q. (2018) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2018.2855968 · Cited by 71

Attentive Semantic Alignment with Offset-Aware Correlation Kernels

Seo, P. H., Lee, J., Jung, D., Han, B., & Cho, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01225-0_22 · Cited by 69

End-to-End Content and Plan Selection for Data-to-Text Generation

Gehrmann, S., Dai, F. Z., Elder, H., & Rush, A. M. (2018) · International Conference on Natural Language Generation · doi:10.18653/v1/w18-6505 · Cited by 75

Hierarchical Recurrent Deep Fusion Using Adaptive Clip Summarization for Sign Language Translation

Guo, D., Zhou, W., Li, A., Li, H., & Wang, M. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2941267 · Cited by 74

Source Code Assessment and Classification Based on Estimated Error Probability Using Attentive LSTM Language Model and Its Application in Programming Education

Rahman, M. M., Watanobe, Y., & Nakamura, K. (2020) · Applied Sciences · doi:10.3390/app10082973 · Cited by 71

A Unified Framework for Multilingual Speech Recognition in Air Traffic Control Systems

Lin, Y., Guo, D., Zhang, J., Chen, Z., & Yang, B. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015830 · Cited by 71

Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem

Li, S., Wu, L., Feng, S., Xu, F., Xu, F., & Zhong, S. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.255 · Cited by 78

Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography

Fu, M., Wang, Y., Chen, Z., Li, J., Xu, F., Liu, X., & Hou, F. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.628502 · Cited by 75

NENet: Nested EfficientNet and adversarial learning for joint optic disc and cup segmentation

Pachade, S., Porwal, P., Kokare, M., Giancardo, L., & Mériaudeau, F. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102253 · Cited by 71

Hinge attention network: A joint model for diabetic retinopathy severity grading

Shaik, N. S., & Cherukuri, T. K. (2022) · Applied Intelligence · doi:10.1007/s10489-021-03043-5 · Cited by 75

Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction

Jie, Z., Li, J., & Lu, W. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.410 · Cited by 83

Attention-Based Multidimensional Deep Learning Approach for Cross-Architecture IoMT Malware Detection and Classification in Healthcare Cyber-Physical Systems

Ravi, V., Pham, T. D., & Alazab, M. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3198123 · Cited by 71

An attention-based stacked BiLSTM framework for predicting remaining useful life of rolling bearings

Rathore, M. S., & Harsha, S. P. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109765 · Cited by 74

Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration ModelingPreprint

Shen, J., Ye, J., Chrzanowski, M., Zhang, Y., Elias, I., Zen, H., & Wu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.04301 · Cited by 71

Self-Attention Architectures for Answer-Agnostic Neural Question Generation

Scialom, T., Piwowarski, B., & Staiano, J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1604 · Cited by 70

Convolutional Self-Attention Networks

Yang, B., Wang, L., Wong, D. F., Chao, L. S., & Tu, Z. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1407 · Cited by 128

AudioCaps: Generating Captions for Audios in The Wild

Kim, C. D., Kim, B., Lee, H., & Kim, G. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1011 · Cited by 96

Ensemble Learning for Multi-Source Neural Machine Translation

Garmash, E., & Monz, C. (2016) · Data Archiving and Networked Services (DANS) · Cited by 70

Interactive Visualization and Manipulation of Attention-based Neural Machine Translation

Lee, J., Shin, J.-H., & Kim, J. (2017) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-2021 · Cited by 87

Investigating End-to-end Speech Recognition for Mandarin-english Code-switching

Shan, C., Weng, C., Wang, G., Su, D., Luo, M., Yu, D., & Xie, L. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682850 · Cited by 70

GSN: A Graph-Structured Network for Multi-Party Dialogues

Hu, W., Chan, Z., Liu, B., Zhao, D., Ma, J., & Yan, R. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/696 · Cited by 70

Very Long Term Field of View Prediction for 360-Degree Video Streaming

Li, C., Zhang, W., Liu, Y., & Wang, Y. (2019) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2019.00060 · Cited by 70

SAINT: self-attention augmented inception-inside-inception network improves protein secondary structure prediction

Uddin, M. R., Mahbub, S., Rahman, M. S., & Bayzid, M. S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa531 · Cited by 69

Lexicon-Based Sentiment Convolutional Neural Networks for Online Review Analysis

Huang, M., Xie, H., Rao, Y., Liu, Y., Poon, L. K. M., & Wang, F. L. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2997769 · Cited by 70

Understanding Attention for Text Classification

Sun, X., & Lu, W. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.312 · Cited by 74

DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation

Lou, R., Liu, W., Li, R., Li, S., He, X., & Shui, W. (2021) · Nature Communications · doi:10.1038/s41467-021-26979-1 · Cited by 68

Deep Learning Approach for Aspect-Based Sentiment Classification: A Comparative Review

Trisna, K. W., & Jie, H. J. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2021.2014186 · Cited by 84

Muformer: A long sequence time-series forecasting model based on modified multi-head attention

Zeng, P., Hu, G., Zhou, X., Li, S., Liu, P., & Liu, S. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109584 · Cited by 74

Learning All Dynamics: Traffic Forecasting via Locality-Aware Spatio-Temporal Joint Transformer

Fang, Y., Zhao, F., Qin, Y., Luo, H., & Wang, C. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3197640 · Cited by 71

A Multi-View attention-based deep learning framework for malware detection in smart healthcare systems

Ravi, V., Alazab, M., Selvaganapathy, S., & Chaganti, R. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.08.015 · Cited by 70

Gesture Spotter: A Rapid Prototyping Tool for Key Gesture Spotting in Virtual and Augmented Reality Applications

Shen, J., Dudley, J. J., Mo, G., & Kristensson, P. O. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3203004 · Cited by 70

Prediction of significant wave height in hurricane area of the Atlantic Ocean using the Bi-LSTM with attention model

Luo, Q.-R., Xu, H., & Bai, L.-H. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.112747 · Cited by 70

Table-to-Text: Describing Table Region With Natural Language

Bao, J., Tang, D., Duan, N., Yan, Z., Lv, Y., Zhou, M., & Zhao, T. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11944 · Cited by 75

Graph Neural Networks for Natural Language Processing: A SurveyPreprint

Wu, L., Chen, Y., Shen, K., Guo, X., Gao, H., Li, S., Pei, J., & Long, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06090 · Cited by 70

Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing

Chen, B., Sun, L., & Han, X. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1071 · Cited by 69

Adversarial Domain Adaptation for Duplicate Question Detection

Shah, D., Lei, T., Moschitti, A., Romeo, S. O., & Nakov, P. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1131 · Cited by 81

Memory-enhanced Decoder for Neural Machine Translation

Wang, M., Lu, Z., Li, H., & Liu, Q. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1027 · Cited by 67

Does Syntax Need to Grow on Trees? Sources of Hierarchical Inductive Bias in Sequence-to-Sequence Networks

McCoy, R. T., Frank, R., & Linzen, T. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00304 · Cited by 80

Deep Learning for Proactive Network Monitoring and Security Protection

Nguyen, G., Dlugolinský, Š., Tran, V., & García, Á. L. (2020) · IEEE Access · doi:10.1109/access.2020.2968718 · Cited by 75

TransOMCS: From Linguistic Graphs to Commonsense Knowledge

Zhang, H., Khashabi, D., Song, Y., & Roth, D. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/554 · Cited by 80

Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization

Yu, T., Dai, W., Liu, Z., & Fung, P. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.326 · Cited by 78

PRA-Net: Part-and-Relation Attention Network for depression recognition from facial expression

Liu, Z., Yuan, X., Li, Y., Shangguan, Z., Zhou, L., & Hu, B. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106589 · Cited by 69

An overview: Attention mechanisms in multi-agent reinforcement learning

Hu, K., Xu, K., Xia, Q., Li, M., Song, Z., Song, L., & Sun, N. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128015 · Cited by 69

Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification

Lin, J.-Y., Su, Q., Yang, P., Ma, S., & Sun, X. (2018) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1485 · Cited by 70

Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

(2019) · doi:10.18653/v1/w19-48 · Cited by 80

Dual Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization

Wang, Y., Xia, Y., Zhao, L., Bian, J., Qin, T., Liu, G., & Liu, T. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11999 · Cited by 69

Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation

Thompson, B. J., Khayrallah, H., Anastasopoulos, A., McCarthy, A. D., Duh, K., Marvin, R., McNamee, P., Gwinnup, J., Anderson, T., & Koehn, P. (2018) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w18-6313 · Cited by 72

Vertebrae Identification and Localization Utilizing Fully Convolutional Networks and a Hidden Markov Model

Chen, Y., Gao, Y., Li, K., Zhao, L., & Zhao, J. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2927289 · Cited by 69

A Zero Attention Model for Personalized Product Search

Ai, Q., Hill, D. N., Vishwanathan, S. V. N., & Croft, W. B. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357980 · Cited by 68

Restoring ancient text using deep learning: a case study on Greek epigraphy

Assael, Y., Sommerschield, T., & PRAG, J. R. W. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1668 · Cited by 78

Attention-Based Deep Learning Framework for Human Activity Recognition With User Adaptation

Buffelli, D., & Vandin, F. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3067690 · Cited by 75

Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos

Wu, J., Li, G., Han, X., & Lin, L. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413862 · Cited by 68

A Framework for Enhancing Deep Neural Networks Against Adversarial Malware

Li, D., Li, Q., Ye, Y., & Xu, S. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3051354 · Cited by 74

Applying the Transformer to Character-level Transduction

Wu, S., Cotterell, R., & Hulden, M. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.163 · Cited by 85

Landslide Deformation Prediction Based on a GNSS Time Series Analysis and Recurrent Neural Network Model

Wang, J., Nie, G., Gao, S., Wu, S., Li, H., & Ren, X. (2021) · Remote Sensing · doi:10.3390/rs13061055 · Cited by 74

Discovery of Pyrazolo[3,4- d ]pyridazinone Derivatives as Selective DDR1 Inhibitors via Deep Learning Based Design, Synthesis, and Biological Evaluation

Tan, X., Li, C., Yang, R., Zhao, S., Li, F., Li, X., Chen, L., Wan, X., Liu, X., Yang, T., et al. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c01205 · Cited by 68

Continual Learning with Lifelong Vision Transformer

Wang, Z., Liu, L., Duan, Y., Kong, Y., & Tao, D. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00027 · Cited by 76

Transformer: A General Framework from Machine Translation to Others

Zhao, Y., Zhang, J., & Zong, C. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1393-5 · Cited by 77

Short-term wind power prediction based on multiscale numerical simulation coupled with deep learning

Li, T., Ai, L., Yang, Q., Zhang, X., Li, H., Lu, D., & Shen, H. (2025) · Renewable Energy · doi:10.1016/j.renene.2025.122951 · Cited by 71

A Context-Aware Recurrent Encoder for Neural Machine Translation

Zhang, B., Xiong, D., Su, J., & Duan, H. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2751420 · Cited by 69

Why Neural Translations are the Right Length

Shi, X., Knight, K. K., & Yüret, D. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1248 · Cited by 67

Personalized Review Generation By Expanding Phrases and Attending on Aspect-Aware Representations

Ni, J., & McAuley, J. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2112 · Cited by 71

Text Mining Analysis of Teaching Evaluation Questionnaires for the Selection of Outstanding Teaching Faculty Members

Tseng, C., Chou, J.-J., & Tsai, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2878478 · Cited by 72

Pushing the Limits of Low-Resource Morphological Inflection

Anastasopoulos, A., & Neubig, G. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1091 · Cited by 67

A Two-Stage Model Based on BERT for Short Fake News Detection

Liu, C., Wu, X., Yu, M., Li, G., Jiang, J., Huang, W., & Lu, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29563-9_17 · Cited by 69

Acquiring Knowledge from Pre-Trained Model to Neural Machine Translation

Weng, R., Yu, H., Huang, S., Cheng, S., & Luo, W. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6465 · Cited by 72

An Intrusion Detection Model With Hierarchical Attention Mechanism

Liu, C., Liu, Y., Yan, Y., & Wang, J. (2020) · IEEE Access · doi:10.1109/access.2020.2983568 · Cited by 72

Robust Few-Shot Learning for User-Provided Data

Lu, J., Jin, S., Liang, J., & Zhang, C. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2984710 · Cited by 69

A Reinforced Generation of Adversarial Examples for Neural Machine Translation

zou, W., Huang, S., Xie, J., Dai, X., & Chen, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.319 · Cited by 76

GARG: Anonymous Recommendation of Point-of-Interest in Mobile Networks by Graph Convolution Network

Wu, S., Zhang, Y., Gao, C., Bian, K., & Cui, B. (2020) · Data Science and Engineering · doi:10.1007/s41019-020-00135-z · Cited by 69

NDVI-Net: A fusion network for generating high-resolution normalized difference vegetation index in remote sensing

Zhang, H., Ma, J., Chen, C., & Tian, X. S. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.08.010 · Cited by 67

Negative emotions detection on online mental-health related patients texts using the deep learning with MHA-BCNN model

Kodati, D., & Ramakrishnudu, T. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115265 · Cited by 68

Deep learning for short-term origin–destination passenger flow prediction under partial observability in urban railway systems

Jiang, W., Ma, Z., & Koutsopoulos, H. N. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06669-1 · Cited by 71

SciCap: Generating Captions for Scientific Figures

Hsu, T.-Y., Giles, C. L., & Huang, T.-H. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.277 · Cited by 59

Conversational Query Understanding Using Sequence to Sequence Modeling

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Erdem, E., Kuyu, M., Yagcioglu, S., Frank, A., Parcalabescu, L., Plank, B., Babii, A., Turuta, O., Erdem, A., Calixto, I., et al. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12918 · Cited by 54

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Lin, B. Y., Zhou, W., Shen, M., Zhou, P., Bhagavatula, C., Choi, Y., & Ren, X. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.165 · Cited by 239

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Gao, X., Gao, Q., Gong, R., Lin, K., Thattai, G., & Sukhatme, G. S. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3193254 · Cited by 55

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Meng, F., Lu, Z., Li, H., & Liu, Q. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.05011 · Cited by 68

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