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

Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey

He, K., Kim, D. S., & Asghar, M. R. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3233793 · Cited by 518

Dynamic Coattention Networks For Question AnsweringPreprint

Xiong, C., Zhong, V., & Socher, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01604 · Cited by 504

Large-Scale Learnable Graph Convolutional Networks

Gao, H., Wang, Z., & Ji, S. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219947 · Cited by 638

An Empirical Study of Spatial Attention Mechanisms in Deep Networks

Zhu, X., Cheng, D., Zhang, Z., Lin, S., & Dai, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00679 · Cited by 659

Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates

Kudo, T. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1007 · Cited by 1,021

Explainable Deep Learning: A Field Guide for the Uninitiated

Ras, G., Xie, N., van Gerven, M. A. J., & Doran, D. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13200 · Cited by 551

Data Recombination for Neural Semantic Parsing

Jia, R., & Liang, P. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1002 · Cited by 441

Hierarchical Cross-Modal Talking Face Generation With Dynamic Pixel-Wise Loss

Chen, L., Maddox, R. K., Duan, Z., & Xu, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00802 · Cited by 437

Train longer, generalize better: closing the generalization gap in large batch training of neural networksPreprint

Hoffer, E., Hubara, I., & Soudry, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08741 · Cited by 602

Deep reinforcement learning for page-wise recommendations

Zhao, X., Xia, L., Zhang, L., Ding, Z., Yin, D., & Tang, J. (2018) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3240323.3240374 · Cited by 447

An Unsupervised Neural Attention Model for Aspect Extraction

He, R., Lee, W. S., Ng, H. T., & Dahlmeier, D. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1036 · Cited by 427

Attention mechanism in neural networks: where it comes and where it goes

Soydaner, D. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07366-3 · Cited by 477

A Convolutional Encoder Model for Neural Machine Translation

Gehring, J., Auli, M., Grangier, D., & Dauphin, Y. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1012 · Cited by 422

SMILES-BERT

Wang, S., Guo, Y., Wang, Y., Sun, H., & Huang, J. (2019) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3307339.3342186 · Cited by 412

Shifting machine learning for healthcare from development to deployment and from models to data

Zhang, A., Xing, L., Zou, J., & Wu, J. C. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00898-y · Cited by 420

Stanford neural machine translation systems for spoken language domains.

Luong, M.-T., & Manning, C. D. (2015) · Cited by 402

A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition

Hu, Y., Wong, Y., Wei, W., Du, Y., Kankanhalli, M., & Geng, W. (2018) · PLoS ONE · doi:10.1371/journal.pone.0206049 · Cited by 403

Fully Character-Level Neural Machine Translation without Explicit Segmentation

Lee, J., Cho, K., & Hofmann, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00067 · Cited by 412

Key-Value Retrieval Networks for Task-Oriented Dialogue

Eric, M., Krishnan, L., Charette, F., & Manning, C. D. (2017) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w17-5506 · Cited by 442

A Transformer-based Approach for Source Code Summarization

Ahmad, W. U., Chakraborty, S., Ray, B., & Chang, K.-W. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.449 · Cited by 464

Video Summarization With Attention-Based Encoder–Decoder Networks

Ji, Z., Xiong, K., Pang, Y., & Li, X. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2904996 · Cited by 393

Graph-to-Sequence Learning using Gated Graph Neural Networks

Beck, D., Haffari, G., & Cohn, T. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1026 · Cited by 380

A novel deep learning method based on attention mechanism for bearing remaining useful life prediction

Chen, Y., Peng, G., Zhu, Z., & Li, S. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105919 · Cited by 400

DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction

Liu, Y., Gong, C., Yang, L., & Chen, Y. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.113082 · Cited by 410

A review of deep learning techniques for speech processing

Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., & Poria, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101869 · Cited by 389

Deep Learning Based Text Classification: A Comprehensive ReviewPreprint

Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Asgari-Chenaghlu, M., & Gao, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.03705 · Cited by 385

Large language models (LLMs): survey, technical frameworks, and future challenges

Kumar, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10888-y · Cited by 395

Multimodal Speech Emotion Recognition Using Audio and Text

Yoon, S., Byun, S., & Jung, K. (2018) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639583 · Cited by 379

Coarse-to-Fine Decoding for Neural Semantic Parsing

Dong, L., & Lapata, M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1068 · Cited by 410

Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification

Tang, H., Ji, D., Li, C., & Zhou, Q. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.588 · Cited by 386

Minimum Risk Training for Neural Machine Translation

Shen, S., Cheng, Y., He, Z., He, W. P., Wu, H. R., Sun, M., & Liu, Y. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1159 · Cited by 377

Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks

Hasson, U., Nastase, S. A., & Goldstein, A. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.002 · Cited by 387

Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language UnderstandingPreprint

Yi, K., Wu, J., Gan, C., Torralba, A. B., Kohli, P., & Tenenbaum, J. B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.02338 · Cited by 416

Semi-Supervised Sequence Modeling with Cross-View Training

Clark, K., Luong, M.-T., Manning, C. D., & Le, Q. V. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1217 · Cited by 368

An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

Tufano, M., Watson, C., Bavota, G., Penta, M. D., White, M., & Poshyvanyk, D. (2019) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3340544 · Cited by 370

Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions

Ji, G., Liu, K., He, S., & Zhao, J. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10953 · Cited by 369

Artificial Intelligence for Drug Toxicity and Safety

Basile, A. O., Yahi, A., & Tatonetti, N. P. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.07.005 · Cited by 401

A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting

Bai, J., Zhu, J., Song, Y., Zhao, L. D., Hou, Z., Du, R., & Li, H. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10070485 · Cited by 409

An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals

Du, X., Ma, C., Zhang, G., Li, J., Lai, Y., Zhao, G., Deng, X., Liu, Y., & Wang, H. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3013711 · Cited by 374

An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning

Şişman, B., Yamagishi, J., King, S., & Li, H. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3038524 · Cited by 399

Faithful to the Original: Fact Aware Neural Abstractive Summarization

Cao, Z., Wei, F., Li, W., & Li, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11912 · Cited by 369

Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting

Wang, S., Wang, X., Wang, S., & Wang, D. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.02.022 · Cited by 363

Neural Question Generation from Text: A Preliminary Study

Zhou, Q. S., Yang, N., Wei, F., Tan, C., Bao, H., & Zhou, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-73618-1_56 · Cited by 371

Learning Improvement Heuristics for Solving Routing Problems

Wu, Y., Song, W. W., Cao, Z., Zhang, J., & Lim, A. E. B. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3068828 · Cited by 398

Efficient Content-Based Sparse Attention with Routing Transformers

Roy, A., Saffar, M., Vaswani, A., & Grangier, D. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00353 · Cited by 509

End-to-End Learnt Image Compression via Non-Local Attention Optimization and Improved Context Modeling

Chen, T., Liu, H., Ma, Z., Shen, Q., Cao, X., & Wang, Y. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3058615 · Cited by 350

Style transfer through back-translation

Prabhumoye, S., Tsvetkov, Y., Salakhutdinov, R., & Black, A. W. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1080 · Cited by 348

CoCoNuT: combining context-aware neural translation models using ensemble for program repair

Lutellier, T., Pham, H., Pang, L., Li, Y., Wei, M., & Tan, L. (2020) · International Symposium on Software Testing and Analysis · doi:10.1145/3395363.3397369 · Cited by 463

Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models

Luong, M.-T., & Manning, C. D. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1100 · Cited by 375

Learning a Neural Semantic Parser from User Feedback

Iyer, S. V., Konstas, I., Cheung, A., Krishnamurthy, J., & Zettlemoyer, L. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1089 · Cited by 375

Sentence Simplification with Deep Reinforcement Learning

Zhang, X., & Lapata, M. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1062 · Cited by 374

Emergent linguistic structure in artificial neural networks trained by self-supervision

Manning, C. D., Clark, K., Hewitt, J., Khandelwal, U., & Levy, O. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907367117 · Cited by 378

Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation

Sun, G., Wang, W., Dai, J., & Gool, L. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58536-5_21 · Cited by 348

Wind power forecasting using attention-based gated recurrent unit network

Niu, Z., Yu, Z., Tang, W., Wu, Q., & Reformat, M. (2020) · Energy · doi:10.1016/j.energy.2020.117081 · Cited by 337

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

Kalyan, K. S. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100048 · Cited by 335

Joint Multi-Task Offloading and Resource Allocation for Mobile Edge Computing Systems in Satellite IoT

Chai, F., Zhang, Q., Yao, H., Xin, X., Gao, R., & Guizani, M. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3238771 · Cited by 336

REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments

Qi, Y., Wu, Q., Anderson, P., Wang, X., Wang, W. Y., Shen, C., & van den Hengel, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01000 · Cited by 331

Towards Better Decoding and Language Model Integration in Sequence to Sequence Models

Chorowski, J., & Jaitly, N. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-343 · Cited by 358

Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-Based Sequence to Sequence Network

Liu, X., Han, Z., Liu, Y.-S., & Zwicker, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018778 · Cited by 330

A Unified Generative Framework for Aspect-based Sentiment Analysis

Yan, H. R., Dai, J., Ji, T., Qiu, X., & Zhang, Z. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.188 · Cited by 378

Deep Communicating Agents for Abstractive Summarization

Çelikyılmaz, A., Bosselut, A., He, X., & Choi, Y. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1150 · Cited by 327

Object-Centric Learning with Slot AttentionPreprint

Locatello, F., Weissenborn, D., Unterthiner, T., Mahendran, A., Heigold, G., Uszkoreit, J., Dosovitskiy, A., & Kipf, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15055 · Cited by 464

Visual Attention Model for Name Tagging in Multimodal Social Media

Lu, D., Neves, L., Carvalho, V., Zhang, N., & Ji, H. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1185 · Cited by 344

Deep learning for geological hazards analysis: Data, models, applications, and opportunities

Ma, Z., & Mei, G. (2021) · Earth-Science Reviews · doi:10.1016/j.earscirev.2021.103858 · Cited by 345

Visual attention methods in deep learning: An in-depth survey

Hassanin, M., Anwar, S., Radwan, I., Khan, F. S., & Mian, A. S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102417 · Cited by 364

ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation

Zhou, C., Bai, J., Song, J., Liu, X., Zhao, Z., Chen, X., & Gao, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11618 · Cited by 330

An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation

Michelsanti, D., Tan, Z., Zhang, S.-X., Xu, Y., Yu, M., Yu, D., & Jensen, J. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3066303 · Cited by 344

Attentive Moment Retrieval in Videos

Liu, M., Wang, X., Nie, L., He, X., Chen, B., & Chua, T. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3210003 · Cited by 313

Attention in Psychology, Neuroscience, and Machine Learning

Lindsay, G. W. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00029 · Cited by 367

RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records

Kwon, B. C., Choi, M.-J., Kim, J. T., Choi, E., Kim, Y. B., Kwon, S., Sun, J., & Choo, J. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2865027 · Cited by 294

Generative Adversarial Networks for face generation: A survey

Kammoun, A., Slama, R., Tabia, H., Ouni, T., & Abid, M. (2022) · ACM Computing Surveys · doi:10.1145/1122445.1122456 · Cited by 370

Deep Semantic Role Labeling With Self-Attention

Tan, Z., Wang, M., Xie, J., Chen, Y., & Shi, X. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11928 · Cited by 312

Decoupled Attention Network for Text Recognition

Wang, T., Zhu, Y., Jin, L., Luo, C., Chen, X., Wu, Y., Wang, Q., & Cai, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6903 · Cited by 312

A Character-level Decoder without Explicit Segmentation for Neural Machine Translation

Chung, J., Cho, K., & Bengio, Y. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1160 · Cited by 335

A Neural Model for Generating Natural Language Summaries of Program Subroutines

LeClair, A., Jiang, S., & McMillan, C. (2019) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2019.00087 · Cited by 319

Simple Recurrent Units for Highly Parallelizable Recurrence

Lei, T., Zhang, Y., Wang, S. I., Dai, H. C., & Artzi, Y. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1477 · Cited by 372

Learning to Assemble Neural Module Tree Networks for Visual Grounding

Liu, D., Zhang, H., Zha, Z.-J., & Wu, F. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00477 · Cited by 304

Vibration-based structural state identification by a 1-dimensional convolutional neural network

Zhang, Y., Miyamori, Y., Mikami, S., & Saito, T. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12447 · Cited by 314

ESIR: End-To-End Scene Text Recognition via Iterative Image Rectification

Zhan, F., & Lu, S. (2019) · 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/cvpr.2019.00216 · Cited by 302

Image–text sentiment analysis via deep multimodal attentive fusion

Huang, F., Zhang, X., Zhao, Z., Xu, J. J., & Li, Z. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.01.019 · Cited by 310

A Unified Generative Framework for Various NER Subtasks

Yan, H., Gui, T., Dai, J., Guo, Q., Zhang, Z., & Qiu, X. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.451 · Cited by 299

An Introduction to Neural Information Retrieval

Mitra, B., & Craswell, N. (2018) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000061 · Cited by 317

Aspect Term Extraction with History Attention and Selective Transformation

Li, X., Bing, L., Li, P., Lam, W. P., & Yang, Z. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/583 · Cited by 299

Attention, please! A survey of neural attention models in deep learning

Correia, A. D. S., & Colombini, E. L. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10148-x · Cited by 365

Text2Sign: Towards Sign Language Production Using Neural Machine Translation and Generative Adversarial Networks

Stoll, S., Camgöz, N. C., Hadfield, S., & Bowden, R. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01281-2 · Cited by 316

Point Transformer

Engel, N., Belagiannis, V., & Dietmayer, K. (2021) · IEEE Access · doi:10.1109/access.2021.3116304 · Cited by 304

Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking

Tay, Y., Tuan, L. A., & Hui, S. C. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186154 · Cited by 296

Adapting BERT for Target-Oriented Multimodal Sentiment Classification

Yu, J., & Jiang, J. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/751 · Cited by 306

Improving Sign Language Translation with Monolingual Data by Sign Back-Translation

Zhou, H., Zhou, W.-G., Qi, W., Pu, J., & Li, H.-Q. (2021) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr46437.2021.00137 · Cited by 414

Artificial Neural Networks, Gradient Boosting and Support Vector Machines for electric vehicle battery state estimation: A review

Manoharan, A., Begam, M., Aparow, V. R., & Sooriamoorthy, D. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105384 · Cited by 304

Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification

Chen, X., Sun, Y., Athiwaratkun, B., Cardie, C., & Weinberger, K. Q. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00039 · Cited by 293

Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data

Hu, Z., Gao, Y., Siyu, J. I., Masayuki, M. A. E., & Imaizumi, T. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.122709 · Cited by 321

Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring

Dong, F., Zhang, Y., & Yang, J. (2017) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k17-1017 · Cited by 311

Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees

Katiyar, A., & Cardie, C. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1085 · Cited by 293

Are NLP Models really able to Solve Simple Math Word Problems?

Patel, A., Bhattamishra, S., & Goyal, N. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.168 · Cited by 307

Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning

Xu, Y., Xie, Z., Feng, Y., & Chen, Z. (2018) · Remote Sensing · doi:10.3390/rs10091461 · Cited by 300

Graph Classification using Structural Attention

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