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

RobustFill: Neural Program Learning under Noisy I/OPreprint

Devlin, J., Uesato, J., Bhupatiraju, S., Singh, R., Mohamed, A., & Kohli, P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.07469 · Cited by 174

An Analysis of Encoder Representations in Transformer-Based Machine Translation

Raganato, A., & Tiedemann, J. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5431 · Cited by 277

Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation

Lin, Y., Ren, P., Chen, Z., Ren, Z., Ma, J., & de Rijke, M. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2906190 · Cited by 134

EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks

Zhang, P., Xue, J., Lan, C., Zeng, W., Gao, Z., & Zheng, N. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2937724 · Cited by 126

Dual-Awareness Attention for Few-Shot Object Detection

Chen, T.-I., Liu, Y.-C., Su, H.-T., Chang, Y., Lin, Y.-H., Yeh, J.-F., Chen, W., & Hsu, W. H. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3125195 · Cited by 139

Learning Semantic Relationship among Instances for Image-Text Matching

Fu, Z., Mao, Z., Song, Y., & Zhang, Y. (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/cvpr52729.2023.01455 · Cited by 133

Reward Augmented Maximum Likelihood for Neural Structured PredictionPreprint

Norouzi, M., Samy Bengio, A., Chen, Z., Jaitly, N., Schuster, M., Wu, Y., & Schuurmans, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.00150 · Cited by 134

Investigating Backtranslation in Neural Machine TranslationPreprint

Poncelas, A., Shterionov, D., Way, A., Wenniger, G. M. D. B., & Passban, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.06189 · Cited by 126

Attention Interpretability Across NLP TasksPreprint

Vashishth, S., Upadhyay, S., Tomar, G. S., & Faruqui, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11218 · Cited by 200

Retrieval Augmented Code Generation and Summarization

Parvez, M. R., Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.232 · Cited by 141

Chromatin accessibility prediction via convolutional long short-term memory networks withk-mer embedding

Xu, M., Zeng, W., Chen, N., Chen, T., & Jiang, R. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx234 · Cited by 126

Deep Hierarchical Encoder–Decoder Network for Image Captioning

Xiao, X., Wang, L., Ding, K., Xiang, S., & Pan, C. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2915033 · Cited by 134

Training Neural Machine Translation to Apply Terminology Constraints

Dinu, G., Mathur, P., Federico, M., & Al-Onaizan, Y. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1294 · Cited by 125

Distilling Knowledge Learned in BERT for Text Generation

Chen, Y.-C., Gan, Z., Yu, C., Liu, J., & Liu, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.705 · Cited by 151

Conversational question answering: a survey

Zaib, M., Zhang, W. E., Sheng, Q. Z., Mahmood, A., & Zhang, Y. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01744-y · Cited by 146

Lexicon Integrated CNN Models with Attention for Sentiment Analysis

Shin, B., Lee, T. J., & Choi, J. D. (2017) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w17-5220 · Cited by 124

Attention Wave-U-Net for Speech Enhancement

Giri, R., Isik, U., & Krishnaswamy, A. (2019) · IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) · doi:10.1109/waspaa.2019.8937186 · Cited by 124

Rethinking Self-Attention: Towards Interpretability in Neural Parsing

Mrini, K., Dernoncourt, F., Tran, Q., Bui, T., Chang, W. H., & Nakashole, N. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.65 · Cited by 148

MuST-C: A multilingual corpus for end-to-end speech translation

Cattoni, R., Gangi, M. A. D., Bentivogli, L., Negri, M., & Turchi, M. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101155 · Cited by 134

A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research

Zhang, X. J., Guo, F., Chen, T., Pan, L., Beliakov, G., & Wu, J. (2023) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer18040110 · Cited by 148

Can neural machine translation do simultaneous translation?Preprint

Cho, K., & Esipova, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.02012 · Cited by 188

Retweet Prediction with Attention-based Deep Neural Network

Zhang, Q., Gong, Y., Wu, J., Huang, H., & Huang, X. (2016) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2983323.2983809 · Cited by 123

Multinomial Adversarial Networks for Multi-Domain Text Classification

Chen, X., & Cardie, C. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1111 · Cited by 122

Gated Mechanism for Attention Based Multi Modal Sentiment Analysis

Kumar, A., & Vepa, J. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053012 · Cited by 123

Deep facial spatiotemporal network for engagement prediction in online learning

Liao, J., Liang, Y., & Pan, J. (2021) · Applied Intelligence · doi:10.1007/s10489-020-02139-8 · Cited by 132

A Spam Transformer Model for SMS Spam Detection

Liu, X., Lu, H., & Nayak, A. R. (2021) · IEEE Access · doi:10.1109/access.2021.3081479 · Cited by 135

Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extraction with Rich Syntactic Knowledge

Wu, S., Fei, H., Ren, Y., Ji, D., & Li, J. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/545 · Cited by 136

Neural Task Graphs: Generalizing to Unseen Tasks From a Single Video Demonstration

Huang, D.-A., Nair, S., Xu, D., Zhu, Y., Garg, A., Fei-Fei, L., Savarese, S., & Niebles, J. C. (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.2019.00876 · Cited by 122

Reinforcement-Learning-Guided Source Code Summarization Using Hierarchical Attention

Wang, W., Zhang, Y., Sui, Y., Wan, Y., Zhao, Z., Wu, J. F., Yu, P. S., & Xu, G. (2020) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.2979701 · Cited by 126

A Hybrid Attention-Based Paralleled Deep Learning model for tool wear prediction

Duan, J., Zhang, X., & Shi, T. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118548 · Cited by 124

Expanding the prediction capacity in long sequence time-series forecasting

Zhou, H., Li, J., Zhang, S., Zhang, S., Yan, M., & Xiong, H. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103886 · Cited by 138

Weighted Transformer Network for Machine TranslationPreprint

Ahmed, K., Keskar, N. S., & Socher, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.02132 · Cited by 152

Word Alignment by Fine-tuning Embeddings on Parallel Corpora

Dou, Z.-Y., & Neubig, G. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.181 · Cited by 172

An asymmetric encoder–decoder model for Zn-ion battery lifetime prediction

Lu, S., Yin, Z., Liao, S., Yang, B., Liu, S., Liu, M., Yin, L., & Zheng, W. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.09.211 · Cited by 121

Triggered Attention for End-to-end Speech Recognition

Moritz, N., Hori, T., & Roux, J. L. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683510 · Cited by 122

Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions

Zhang, R., Yu, T., Er, H., Shim, S., Xue, E. R., Lin, X., Shi, T., Xiong, C., Socher, R., & Radev, D. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1537 · Cited by 132

Deep Learning Techniques on Text Classification Using Natural Language Processing (NLP) In Social Healthcare Network: A Comprehensive Survey

Lavanya, P., & Sasikala, E. (2021) · doi:10.1109/icspc51351.2021.9451752 · Cited by 129

An attention-based CNN-BiLSTM model for depression detection on social media text

Thekkekara, J. P., Yongchareon, S., & Liesaputra, V. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123834 · Cited by 141

Neural Machine Translation and Sequence-to-sequence Models: A TutorialPreprint

Neubig, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.01619 · Cited by 194

Recent Advances in Deep Learning: An OverviewPreprint

Minar, M. R., & Naher, J. (2018) · arXiv (Cornell University) · doi:10.13140/rg.2.2.24831.10403 · Cited by 164

Abstractive Summarization: A Survey of the State of the Art

Lin, H., & Ng, V. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019815 · Cited by 120

Are Transformers universal approximators of sequence-to-sequence functions?Preprint

Yun, C., Bhojanapalli, S., Rawat, A. S., Reddi, S. J., & Kumar, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.10077 · Cited by 292

Learning to Summarize Radiology Findings

Zhang, Y., Ding, D. Y., Qian, T., Manning, C. D., & Langlotz, C. P. (2018) · Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis · doi:10.18653/v1/w18-5623 · Cited by 126

Human Action Recognition by Learning Spatio-Temporal Features With Deep Neural Networks

Wang, L., Xu, Y., Cheng, J. S., Xia, H., Yin, J., & Wu, J. (2018) · IEEE Access · doi:10.1109/access.2018.2817253 · Cited by 121

Fluency Boost Learning and Inference for Neural Grammatical Error Correction

Ge, T., Wei, F., & Zhou, M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1097 · Cited by 119

Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition

Cho, M., Ha, J., Park, C., & Park, S. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103381 · Cited by 127

A Novel Spatial-Temporal Multi-Scale Alignment Graph Neural Network Security Model for Vehicles Prediction

Diao, C., Zhang, D., Liang, W., Li, K., Hong, Y., & Gaudiot, J. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3140229 · Cited by 125

A survey on Sign Language machine translation

Núñez-Marcos, A., Perez-de-Viñaspre, O., & Labaka, G. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118993 · Cited by 140

Short-term photovoltaic power forecasting with feature extraction and attention mechanisms

Liu, W., & Mao, Z. (2024) · Renewable Energy · doi:10.1016/j.renene.2024.120437 · Cited by 130

The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting SecretsPreprint

Carlini, N., Liu, C., Kos, J., Erlingsson, Ú., & Song, D. X. (2018) · arXiv (Cornell University) · Cited by 124

AMR Parsing as Sequence-to-Graph Transduction

Zhang, S., Ma, X., Duh, K., & Durme, B. V. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1009 · Cited by 128

Neural Net Models of Open-domain Discourse Coherence

Li, J., & Jurafsky, D. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1019 · Cited by 136

Context-Aware Self-Attention Networks

Yang, B., Li, J., Wong, D. F., Chao, L. S., Wang, X., & Tu, Z. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301387 · Cited by 124

Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks

Wu, Q., Zheng, H., Guo, X., & Liu, G. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.09.036 · Cited by 118

Developing health indicators and RUL prognostics for systems with few failure instances and varying operating conditions using a LSTM autoencoder

de Pater, I., & Mitici, M. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105582 · Cited by 127

A survey of malware detection using deep learning

Bensaoud, A., Kalita, J. K., & Bensaoud, M. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100546 · Cited by 163

A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions

Toral, A., & Sánchez-Cartagena, V. M. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1100 · Cited by 143

Dyadic Memory Networks for Aspect-based Sentiment Analysis

Tay, Y., Tuan, L. A., & Hui, S. C. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132936 · Cited by 116

Deep learning for EEG data analytics: A survey

Li, G., Lee, C. H., Jung, J. J., Youn, Y. C., & Camacho, D. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5199 · Cited by 129

Data-Driven Sentence Simplification: Survey and Benchmark

Alva-Manchego, F. E., Scarton, C., & Specia, L. (2020) · Computational Linguistics · doi:10.1162/coli_a_00370 · Cited by 130

CASIE: Extracting Cybersecurity Event Information from Text

Satyapanich, T., Ferraro, F., & Finin, T. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6401 · Cited by 128

Deep pyramid local attention neural network for cardiac structure segmentation in two-dimensional echocardiography

Liu, F., Wang, K., Liu, D., Yang, X., & Tian, J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101873 · Cited by 121

Formal Limitations on the Measurement of Mutual InformationPreprint

McAllester, D., & Stratos, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.04251 · Cited by 117

Data-to-text Generation with Entity Modeling

Puduppully, R., Dong, L., & Lapata, M. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1195 · Cited by 116

k-means Mask Transformer

Yu, Q., Wang, H., Qiao, S., Collins, M. D., Zhu, Y., Adam, H., Yuille, A., & Chen, L.-C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19818-2_17 · Cited by 123

Enhancing early Parkinson’s disease detection through multimodal deep learning and explainable AI: insights from the PPMI database

Dentamaro, V., Impedovo, D., Musti, L., Pirlo, G., & Taurisano, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-70165-4 · Cited by 116

Untitled work

(2018) · Cited by 115

KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation

Chen, W., Su, Y., Yan, X., & Wang, W. Y. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.697 · Cited by 146

Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants

Stefenon, S. F., Seman, L. O., Aquino, L. S., & Coelho, L. D. S. (2023) · Energy · doi:10.1016/j.energy.2023.127350 · Cited by 115

Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models

Zhao, T., Xie, K., & Eskénazi, M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1123 · Cited by 114

Title-Guided Encoding for Keyphrase Generation

Chen, W., Gao, Y., Zhang, J., King, I., & Lyu, M. R.-T. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016268 · Cited by 113

Hybrid neural recommendation with joint deep representation learning of ratings and reviews

Liu, H., Wang, Y., Peng, Q., Wu, F., Gan, L., Pan, L., & Jiao, P. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.09.052 · Cited by 119

Sequence to Sequence Model Performance for Education Chatbot

Palasundram, K., Sharef, N. M., Nasharuddin, N. A., Kasmiran, K. A., & Azman, A. (2019) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v14i24.12187 · Cited by 114

Visual Question Answering with Memory-Augmented Networks

Ma, C., Shen, C., Dick, A., Wu, Q., Wang, P., van den Hengel, A., & Reid, I. D. (2018) · 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.2018.00729 · Cited by 117

Dual-side Sparse Tensor Core

Wang, Y., Zhang, C., Xie, Z., Guo, C., Liu, Y., & Leng, J. (2021) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca52012.2021.00088 · Cited by 118

Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback

Nguyen, K., Daumé III, H., & Boyd-Graber, J. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1153 · Cited by 110

Coloring with Words: Guiding Image Colorization Through Text-Based Palette Generation

Bahng, H., Yoo, S., Cho, W., Park, D. K., Wu, Z., Ma, X., & Choo, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_27 · Cited by 112

Curriculum Learning for Domain Adaptation in Neural Machine Translation

Zhang, X., Shapiro, P., Kumar, G., McNamee, P., Carpuat, M. J., & Duh, K. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1189 · Cited by 115

Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks

Ma, J., Gao, W. G., Joty, S., & Wong, K. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1244 · Cited by 114

Morphological Inflection Generation with Hard Monotonic Attention

Aharoni, R., & Goldberg, Y. (2016) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p17-1183 · Cited by 134

Multi-Source Cross-Lingual Model Transfer: Learning What to Share

Chen, X., Awadallah, A. H., Hassan, H., Wang, W., & Cardie, C. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1299 · Cited by 138

Sequence-to-Dependency Neural Machine Translation

Wu, S., Zhang, D., Yang, N., Li, M., & Zhou, M. Q. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1065 · Cited by 112

Sentiment classification with word localization based on weakly supervised learning with a convolutional neural network

Lee, G., Jeong, J.-Y., Seo, S., Kim, C., & Kang, P. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.04.006 · Cited by 112

Accelerating Neural Transformer via an Average Attention Network

Zhang, B., Xiong, D., & Su, J. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1166 · Cited by 116

Multilingual Neural Machine Translation with Language Clustering

Tan, X., Chen, J.-L., He, D., Xia, Y., Qin, T., & Liu, T.-Y. (2019) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d19-1089 · Cited by 138

An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem

Li, B., Wu, G., He, Y., Fan, M., & Witold Pedrycz, C. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105677 · Cited by 127

A hybrid NOx emission prediction model based on CEEMDAN and AM-LSTM

Wang, X., Liu, W., Wang, Y., & Yang, G. (2021) · Fuel · doi:10.1016/j.fuel.2021.122486 · Cited by 112

FluentNet: End-to-End Detection of Stuttered Speech Disfluencies With Deep Learning

Kourkounakis, T., Hajavi, A., & Etemad, A. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3110146 · Cited by 109

Incremental Transformer with Deliberation Decoder for Document Grounded Conversations

Li, Z., Niu, C., Meng, F., Feng, Y., Li, Q., & Zhou, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1002 · Cited by 112

Context Gates for Neural Machine Translation

Tu, Z., Liu, Y., Lu, Z., Liu, X., & Li, H. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00048 · Cited by 126

DeepDelta: learning to repair compilation errors

Mesbah, A., Rice, A. C., Johnston, E., Glorioso, N., & Aftandilian, E. E. (2019) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3338906.3340455 · Cited by 115

Aspect-based sentiment analysis with gated alternate neural network

Liu, N., & Shen, B. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105010 · Cited by 111

Automatic stenosis recognition from coronary angiography using convolutional neural networks

Moon, J. H., Lee, D. Y., Cha, W. C., Chung, M. J., Lee, K., Cho, B. H., & Choi, J. H. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105819 · Cited by 110

Multimodal research in vision and language: A review of current and emerging trends

Uppal, S., Bhagat, S., Hazarika, D., Majumder, N., Poria, S., Zimmermann, R., & Zadeh, A. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.07.009 · Cited by 118

Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism

Aguirre, N. A., Grall‐Maës, E., Cymberknop, L. J., & Armentano, R. L. (2021) · Sensors · doi:10.3390/s21062167 · Cited by 115

Recursive long short-term memory network for predicting nonlinear structural seismic response

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