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

How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey

Ye, J., Zhao, J., Ye, K., & Xu, C. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3043250 · Cited by 298

Outlier Detection for Time Series with Recurrent Autoencoder Ensembles

Kieu, T., Yang, B., Guo, C., & Jensen, C. S. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/378 · Cited by 302

Graph Neural Networks for Natural Language Processing: A Survey

Wu, L., Chen, Y., Shen, K., Guo, X., Gao, H., Li, S., Pei, J., & Long, B. (2023) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000096 · Cited by 417

Exploiting Source-side Monolingual Data in Neural Machine Translation

Zhang, J., & Zong, C. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1160 · Cited by 287

BS-Nets: An End-to-End Framework for Band Selection of Hyperspectral Image

Cai, Y., Liu, X., & Cai, Z. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2951433 · Cited by 290

An Introductory Survey on Attention Mechanisms in NLP Problems

Hu, D. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-29513-4_31 · Cited by 296

An effective spatial-temporal attention based neural network for traffic flow prediction

Loan, N. N., Vu, H. L., Vo, B. Q., Liu, Z., & Phung, D. (2019) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2019.09.008 · Cited by 280

Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing

Choi, Y., Youngnam, L., Junghyun, C., Baek, J., Kim, B., Cha, Y., Shin, D., Bae, C., & Heo, J. (2020) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3386527.3405945 · Cited by 279

Vision Transformers in medical computer vision—A contemplative retrospection

Parvaiz, A., Khalid, M. A., Zafar, R., Ameer, H., Ali, M. U., & Fraz, M. M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106126 · Cited by 302

When2com: Multi-Agent Perception via Communication Graph Grouping

Liu, Y., Tian, J., Glaser, N., & Kira, Z. (2020) · 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/cvpr42600.2020.00416 · Cited by 307

FANG

Nguyen, V.-H., Sugiyama, K., Nakov, P., & Kan, M. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412046 · Cited by 297

Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures

Tang, G., Mathias, M., Rios, A., & Sennrich, R. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1458 · Cited by 283

Controllable Abstractive Summarization

Fan, A., Grangier, D., & Auli, M. (2018) · Proceedings of the 2nd Workshop on Neural Machine Translation and Generation · doi:10.18653/v1/w18-2706 · Cited by 281

Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies

Zhang, Z., Li, M., Lin, X., Wang, Y., & He, F. (2019) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2019.05.039 · Cited by 297

Tree-to-Sequence Attentional Neural Machine Translation

Eriguchi, A., Hashimoto, K., & Tsuruoka, Y. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1078 · Cited by 279

Selective Encoding for Abstractive Sentence Summarization

Zhou, Q. S., Yang, N., Wei, F., & Zhou, M. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1101 · Cited by 276

Automatically generating commit messages from diffs using neural machine translation

Jiang, S., Armaly, A., & McMillan, C. (2017) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2017.8115626 · Cited by 267

Attention mechanism in intelligent fault diagnosis of machinery: A review of technique and application

Lv, H., Chen, J., Pan, T., Zhang, T., Feng, Y., & Liu, S. (2022) · Measurement · doi:10.1016/j.measurement.2022.111594 · Cited by 299

Neural Machine Translation

Koehn, P. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108608480 · Cited by 375

Graph U-Nets

Gao, H., & Ji, S. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3081010 · Cited by 281

Zero-Resource Translation with Multi-Lingual Neural Machine Translation

Fırat, O., Sankaran, B., Al-Onaizan, Y., Vural, F. T. Y., & Cho, K. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1026 · Cited by 278

Deep Concatenated Residual Network With Bidirectional LSTM for One-Hour-Ahead Wind Power Forecasting

Ko, M.-S., Lee, K., Kim, J.-K., Hong, C. W., Dong, Z. Y., & Hur, K. (2020) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2020.3043884 · Cited by 273

DailyDialog: A Manually Labelled Multi-Turn Dialogue Dataset

Li, Y. (2025) · TIB Data Manager · doi:10.57702/l9x4bl6j · Cited by 262

Improved Training of End-to-end Attention Models for Speech Recognition

Zeyer, A., Irie, K., Schlüter, R., & Ney, H. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1616 · Cited by 260

Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction

Yao, H., Liu, Y., Wei, Y., Tang, X., & Li, Z. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313577 · Cited by 270

Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding

Ku, A., Anderson, P., Patel, R., Ie, E., & Baldridge, J. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.356 · Cited by 274

Watch, attend and parse: An end-to-end neural network based approach to handwritten mathematical expression recognition

Zhang, J., Du, J., Zhang, S. S., Liu, D., Hu, Y., Hu, J., Wei, S., & Dai, L.-R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.06.017 · Cited by 259

A Survey on Text Classification Algorithms: From Text to Predictions

Gasparetto, A., Marcuzzo, M., Zangari, A., & Albarelli, A. (2022) · Information · doi:10.3390/info13020083 · Cited by 305

Adaptation of Deep Bidirectional Multilingual Transformers for Russian LanguagePreprint

Kuratov, Y., & Arkhipov, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.07213 · Cited by 389

Dynabench: Rethinking Benchmarking in NLP

Kiela, D., Bartolo, M., Nie, Y., Kaushik, D., Geiger, A., Wu, Z., Vidgen, B., Prasad, G., Singh, A., Ringshia, P., et al. (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.324 · Cited by 254

Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog

Schuster, S., Gupta, S., Shah, R., & Lewis, M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1380 · Cited by 270

Computation Offloading in Multi-Access Edge Computing Using a Deep Sequential Model Based on Reinforcement Learning

Wang, J., Hu, J. W., Min, G., Zhan, W., Ni, Q., & Georgalas, N. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1800971 · Cited by 252

Survey on Deep Neural Networks in Speech and Vision Systems

Alam, M., Samad, M. D., Vidyaratne, L., Glandon, A., & Iftekharuddin, K. M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.053 · Cited by 265

ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance ComputingPreprint

Elnaggar, A., Heinzinger, M., Dallago, C., Rihawi, G., Wang, Y., Jones, L., Gibbs, T., Fehér, T., Angerer, C. M., Steinegger, M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.06225 · Cited by 364

Detect Rumors on Twitter by Promoting Information Campaigns with Generative Adversarial Learning

Ma, J., Gao, W. G., & Wong, K. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313741 · Cited by 253

Human-Centered Explainable AI: Towards a Reflective Sociotechnical Approach

Ehsan, U., & Riedl, M. O. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-60117-1_33 · Cited by 274

COVID-19 Sensing: Negative Sentiment Analysis on Social Media in China via BERT Model

Wang, T., Lu, K., Chow, K. P., & Zhu, Q. (2020) · IEEE Access · doi:10.1109/access.2020.3012595 · Cited by 268

Ensemble unsupervised autoencoders and Gaussian mixture model for cyberattack detection

An, P., Wang, Z., & Zhang, C. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2021.102844 · Cited by 251

Artificial Intelligence in Pathology

Chang, H. Y., Jung, C. K., Woo, J. I., Lee, S.-H., Cho, J., Kim, S. W., & Kwak, T.-Y. (2018) · Journal of Pathology and Translational Medicine · doi:10.4132/jptm.2018.12.16 · Cited by 256

Hierarchical LSTMs with Adaptive Attention for Visual Captioning

Gao, L., Li, X., Song, J., & Shen, H. T. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2894139 · Cited by 249

Visual question answering: Datasets, algorithms, and future challenges

Kafle, K., & Kanan, C. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.06.005 · Cited by 259

Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction

Nayak, T. K., & Ng, H. T. (2020) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v34i05.6374 · Cited by 251

From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers

Lauscher, A., Ravishankar, V., Vulić, I., & Glavaš, G. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.363 · Cited by 283

Improving position encoding of transformers for multivariate time series classification

Foumani, N. M., Tan, C. W., Webb, G. I., & Salehi, M. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00948-2 · Cited by 279

Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Zhao, S., Peng, Y., Liu, J., & Wu, S. (2021) · Agriculture · doi:10.3390/agriculture11070651 · Cited by 255

Summarizing Videos with Attention

Fajtl, J., Sokeh, H. S., Argyriou, V., Monekosso, D., & Remagnino, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21074-8_4 · Cited by 244

Neural Relational Inference for Interacting SystemsPreprint

Kipf, T., Fetaya, E., Wang, K.-C. J., Welling, M., & Zemel, R. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04687 · Cited by 361

A Review on Speech Emotion Recognition Using Deep Learning and Attention Mechanism

Lieskovská, E., Jakubec, M., Jarina, R., & Chmulík, M. (2021) · Electronics · doi:10.3390/electronics10101163 · Cited by 246

Deep Reinforcement Learning for Sequence-to-Sequence Models

Keneshloo, Y., Shi, T., Ramakrishnan, N., & Reddy, C. K. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2929141 · Cited by 230

Attention-based interpretable neural network for building cooling load prediction

Li, A., Xiao, F., Zhang, C., & Fan, C. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117238 · Cited by 240

Review Networks for Caption GenerationPreprint

Yang, Z., Yuan, Y., Wu, Y., Salakhutdinov, R., & Cohen, W. W. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.07912 · Cited by 230

Macroscopic–Microscopic Attention in LSTM Networks Based on Fusion Features for Gear Remaining Life Prediction

Qin, Y., Xiang, S., Chai, Y., & Chen, H. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2959492 · Cited by 230

Collaborative Perception in Autonomous Driving: Methods, Datasets, and Challenges

Han, Y., Zhang, H., Li, H., Jin, Y., Lang, C., & Li, Y. (2023) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2023.3298534 · Cited by 254

Deep Learning with Microfluidics for Biotechnology

Riordon, J., Sovilj, D., Sanner, S., Sinton, D. A., & Young, E. W. K. (2018) · Trends in biotechnology · doi:10.1016/j.tibtech.2018.08.005 · Cited by 227

Exploiting Document Knowledge for Aspect-level Sentiment Classification

He, R., Lee, W. S., Ng, H. T., & Dahlmeier, D. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2092 · Cited by 235

Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization

Cao, Z., Li, W., Li, S., & Wei, F. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1015 · Cited by 202

Exploring the frontiers of deep learning and natural language processing: A comprehensive overview of key challenges and emerging trends

Khan, W., Daud, A. Y., Khan, K., Muhammad, S., & Haq, R. (2023) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100026 · Cited by 260

Globally Coherent Text Generation with Neural Checklist Models

Kiddon, C., Zettlemoyer, L., & Choi, Y. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1032 · Cited by 228

Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks

Dibia, V. C., & Demiralp, Ç. (2019) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2019.2924636 · Cited by 233

Research commentary on recommendations with side information: A survey and research directions

Sun, Z., Guo, Q., Yang, J., Fang, H., Guo, G., Zhang, J., & Burke, R. (2019) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2019.100879 · Cited by 215

Smart augmented reality instructional system for mechanical assembly towards worker-centered intelligent manufacturing

Lai, Z.-H., Tao, W., Leu, M. C., & Yin, Z. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.02.010 · Cited by 220

SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task

Yu, T., Yasunaga, M., Yang, K., Zhang, R., Wang, D., Li, Z., & Radev, D. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1193 · Cited by 238

2021 International Joint Conference on Neural Networks (IJCNN)

(2021) · doi:10.1109/ijcnn52387.2021 · Cited by 215

Global-Locally Self-Attentive Encoder for Dialogue State Tracking

Zhong, V., Xiong, C., & Socher, R. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1135 · Cited by 208

On Learning Meaningful Code Changes Via Neural Machine Translation

Tufano, M., Pantiuchina, J., Watson, C., Bavota, G., & Poshyvanyk, D. (2019) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2019.00021 · Cited by 228

A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox

Zhang, K., Tang, B., Deng, L., & Liu, X. (2021) · Measurement · doi:10.1016/j.measurement.2021.109491 · Cited by 218

Hierarchical LSTM for Sign Language Translation

Guo, D., Zhou, W., Li, H., & Wang, M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12235 · Cited by 211

Variational Neural Machine Translation

Zhang, B., Xiong, D., Su, J., Duan, H., & Zhang, M. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1050 · Cited by 224

Neural Sign Language Translation Based on Human Keypoint Estimation

Ko, S., Kim, C., Jung, H., & Cho, C. (2019) · Applied Sciences · doi:10.3390/app9132683 · Cited by 233

Graph Embedded Pose Clustering for Anomaly Detection

Markovitz, A., Sharir, G., Friedman, I., Zelnik-Manor, L., & Avidan, S. (2019) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr42600.2020.01055 · Cited by 222

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

Shen, Y., Wu, N., Phang, J., Park, J., Liu, K., Tyagi, S., Heacock, L. C., Kim, S., Moy, L. P., Cho, K., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101908 · Cited by 222

Hard-rock tunnel lithology prediction with TBM construction big data using a global-attention-mechanism-based LSTM network

Liu, Z., Li, L., Fang, X., Qi, W., Shen, J.-M., Zhou, H., & Zhang, Y. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103647 · Cited by 209

Predicting Pedestrian Crossing Intention With Feature Fusion and Spatio-Temporal Attention

Yang, D., Zhang, H., Yurtsever, E., Redmill, K., & Özgüner, Ü. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3162719 · Cited by 225

Paraphrasing Revisited with Neural Machine Translation

Mallinson, J., Sennrich, R., & Lapata, M. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1083 · Cited by 209

Reasoning with Sarcasm by Reading In-Between

Tay, Y., Luu, A. T., Hui, S. C., & Su, J. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1093 · Cited by 244

Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks With Attention

Sehovac, L., & Grolinger, K. (2020) · IEEE Access · doi:10.1109/access.2020.2975738 · Cited by 222

Adversarial text-to-image synthesis: A review

Frolov, S., Hinz, T., Raue, F., Hees, J., & Dengel, A. R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.07.019 · Cited by 228

Controlling Output Length in Neural Encoder-Decoders

Kikuchi, Y., Neubig, G., Sasano, R., Takamura, H., & Okumura, M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1140 · Cited by 222

Is Neural Machine Translation the New State of the Art?

Castilho, S., Moorkens, J., Gaspari, F., Calixto, I., Tinsley, J. F., & Way, A. (2017) · The Prague Bulletin of Mathematical Linguistics · doi:10.1515/pralin-2017-0013 · Cited by 208

MIME: MIMicking Emotions for Empathetic Response Generation

Majumder, N., Hong, P., Peng, S., Lu, J., Ghosal, D., Gelbukh, A. F., Mihalcea, R. F., & Poria, S. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.721 · Cited by 239

Context Aware Graph Convolution for Skeleton-Based Action Recognition

Zhang, X., Xu, C., & Tao, D. (2020) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr42600.2020.01434 · Cited by 170

Exploring Neural Text Simplification Models

Nisioi, S., Štajner, S., Ponzetto, S. P., & Dinu, L. P. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2014 · Cited by 216

Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network

Son, L. H., Kumar, A., Sangwan, S. R., Arora, A., Nayyar, A., & Abdel-Basset, M. (2019) · IEEE Access · doi:10.1109/access.2019.2899260 · Cited by 206

Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates

Pesciullesi, G., Schwaller, P., Laino, T., & Reymond, J. (2020) · Nature Communications · doi:10.1038/s41467-020-18671-7 · Cited by 207

TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations

Liu, Q., & Xie, L. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008653 · Cited by 205

Fine-grained attention mechanism for neural machine translation

Choi, H., Cho, K., & Bengio, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.01.007 · Cited by 211

Incorporating Discrete Translation Lexicons into Neural Machine Translation

Arthur, P., Neubig, G., & Nakamura, S. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1162 · Cited by 210

MojiTalk: Generating Emotional Responses at Scale

Zhou, X., & Wang, W. Y. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1104 · Cited by 214

MuST-C: a Multilingual Speech Translation Corpus

Gangi, M. A. D., Cattoni, R., Bentivogli, L., Negri, M., & Turchi, M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1202 · Cited by 218

Sparse Sequence-to-Sequence Models

Peters, B., Niculae, V., & Martins, A. F. T. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1146 · Cited by 284

Adaptively Sparse Transformers

Correia, G. M., Niculae, V., & Martins, A. F. T. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1223 · Cited by 233

SMILES-based deep generative scaffold decorator for de-novo drug design

Arús‐Pous, J., Patronov, A., Bjerrum, E. J., Tyrchan, C., Reymond, J., Chen, H., & Engkvist, O. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00441-8 · Cited by 209

Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection

Brown, A., Tuor, A. R., Hutchinson, B., & Nichols, N. (2018) · Proceedings of the First Workshop on Machine Learning for Computing Systems · doi:10.1145/3217871.3217872 · Cited by 225

Learning to mine aligned code and natural language pairs from stack overflow

Yin, P., Deng, B., Chen, E., Vasilescu, B., & Neubig, G. (2018) · Proceedings of the 15th International Conference on Mining Software Repositories · doi:10.1145/3196398.3196408 · Cited by 218

Topological Recurrent Neural Network for Diffusion Prediction

Wang, J., Zheng, V. W., Liu, Z., & Chang, K. C. (2017) · Proceedings · doi:10.1109/icdm.2017.57 · Cited by 206

Deep ConvLSTM With Self-Attention for Human Activity Decoding Using Wearable Sensors

SINGH, S. P., Sharma, M. K., Lay-Ekuakille, A., Gangwar, D., & Gupta, S. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3045135 · Cited by 211

SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation

Wang, X., Pham, H., Dai, Z., & Neubig, G. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1100 · Cited by 203

Activate or Not: Learning Customized Activation

Ma, N., Zhang, X., Liu, M., & Sun, J. (2021) · 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/cvpr46437.2021.00794 · Cited by 206

A Simple, Fast Diverse Decoding Algorithm for Neural GenerationPreprint

Li, J., Monroe, W., & Jurafsky, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.08562 · Cited by 269

Modeling Localness for Self-Attention Networks

Yang, B., Tu, Z., Wong, D. F., Meng, F., Chao, L. S., & Zhang, T. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1475 · Cited by 200

Bilingual attention based neural machine translation

Kang, L., He, S., Wang, M., Long, F., & Su, J. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03563-8 · Cited by 49

Interactive Attention for Neural Machine TranslationPreprint

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

Neural Machine Translation with Target-Attention Model

Yang, M., Zhang, M., Chen, K., Wang, R., & Zhao, T. (2020) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2019edp7157 · Cited by 9

Neural Machine Translation with Deep Attention

Zhang, B., Xiong, D., & Su, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2876404 · Cited by 138

Neural Machine Translation with Bilingual History Involved Attention

Xue, H., Feng, Y., You, D., Zhang, W., & Li, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32236-6_23 · Cited by 1