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

Attention-Based Temporal Weighted Convolutional Neural Network for Action Recognition

Zang, J., Wang, L., Liu, Z., Zhang, Q., Hua, G., & Zheng, N. (2018) · IFIP advances in information and communication technology · doi:10.1007/978-3-319-92007-8_9 · Cited by 82

Prediction of off-target specificity and cell-specific fitness of CRISPR-Cas System using attention boosted deep learning and network-based gene feature

Liu, Q., He, D., & Xie, L. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007480 · Cited by 86

DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

Lin, T., Zhong, R., Wang, Y., Xu, J., Jiang, H., Xu, J., Ying, Y. B., Rodríguez, L. F., Ting, K. C., & Li, H. (2020) · Environmental Research Letters · doi:10.1088/1748-9326/ab66cb · Cited by 89

Self-Attention Encoding and Pooling for Speaker Recognition

Safari, P., India, M., & Hernando, J. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1446 · Cited by 95

Re-examining the Role of Schema Linking in Text-to-SQL

Lei, W., Wang, W., Ma, Z., Gan, T., Lu, W., Kan, M., & Chua, T. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.564 · Cited by 92

FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks

Hsu, Y.-L., Tsai, Y.-C., & Li, C. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3079496 · Cited by 87

Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks

Wang, A. Y., Wang, D., Drozdal, J., Muller, M. J., Park, S., Weisz, J. D., Liu, X., Wu, L., & Dugan, C. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3489465 · Cited by 105

Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-based Deep Structured Semantic Model

Samtani, S., Chai, Y., & Chen, H. (2022) · MIS Quarterly · doi:10.25300/misq/2022/15392 · Cited by 90

An Adaptive Attention Fusion Mechanism Convolutional Network for Object Detection in Remote Sensing Images

Ye, Y., Ren, X., Zhu, B., Tang, T., Tan, X., Gui, Y., & Yao, Q. (2022) · Remote Sensing · doi:10.3390/rs14030516 · Cited by 85

Attention-Embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis

Liao, J.-X., Dong, H., Sun, Z., Sun, J., Zhang, S., & Fan, F. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3259031 · Cited by 91

GILE: A Generalized Input-Label Embedding for Text Classification

Pappas, N., & Henderson, J. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00259 · Cited by 81

Attention-based convolutional approach for misinformation identification from massive and noisy microblog posts

Yu, F., Liu, Q., Wu, S., Wang, L., & Tan, T. (2019) · Computers & Security · doi:10.1016/j.cose.2019.02.003 · Cited by 82

Convolutional Attention Networks for Scene Text Recognition

Xie, H., Fang, S., Zha, Z.-J., Yang, Y., Li, Y., & Zhang, Y. (2019) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3231737 · Cited by 81

Examining Attention Mechanisms in Deep Learning Models for Sentiment Analysis

Kardakis, S., Perikos, I., Grivokostopoulou, F., & Hatzilygeroudis, I. (2021) · Applied Sciences · doi:10.3390/app11093883 · Cited by 85

Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus

Gong, H., Bhat, S., Wu, L., Xiong, J., & Hwu, W. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1320 · Cited by 80

Weakly Labelled AudioSet Tagging With Attention Neural Networks

Kong, Q., Yu, C., Xu, Y., Iqbal, T., Wang, W., & Plumbley, M. D. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2930913 · Cited by 88

Reinforced Spatiotemporal Attentive Graph Neural Networks for Traffic Forecasting

Zhou, F., Yang, Q., Zhang, K., Trajcevski, G., Zhong, T., & Khokhar, A. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2974494 · Cited by 83

Neural machine translating from natural language to SPARQL

Yin, X., Gromann, D., & Rudolph, S. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.12.013 · Cited by 94

Wind Power Forecasting Using Attention-Based Recurrent Neural Networks: A Comparative Study

Bin, H., Liang, Y., & Qiu, X. (2021) · IEEE Access · doi:10.1109/access.2021.3065502 · Cited by 84

Is Attention Explanation? An Introduction to the Debate

Bibal, A., Cardon, R., Alfter, D., Wilkens, R., Wang, X., François, T., & Watrin, P. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.269 · Cited by 123

Relating EEG to continuous speech using deep neural networks: a review

Puffay, C., Accou, B., Bollens, L., Monesi, M. J., Vanthornhout, J., hamme, H. V., & Francart, T. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ace73f · Cited by 80

Attention Mechanism, Transformers, BERT, and GPT: Tutorial and SurveyPreprint

Ghojogh, B., & Ghodsi, A. (2020) · HAL · doi:10.31219/osf.io/m6gcn · Cited by 89

A Hierarchy-to-Sequence Attentional Neural Machine Translation Model

Su, J., Zeng, J., Xiong, D., Liu, Y., Wang, M., & Xie, J. (2018) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2018.2789721 · Cited by 79

Extracting Resource Needs and Availabilities From Microblogs for Aiding Post-Disaster Relief Operations

Basu, M., Shandilya, A., Khosla, P., Ghosh, K., & Ghosh, S. (2019) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2019.2914179 · Cited by 81

Joey NMT: A Minimalist NMT Toolkit for Novices

Kreutzer, J., Bastings, J., & Riezler, S. (2019) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/d19-3019 · Cited by 79

A general deep learning framework for history-dependent response prediction based on UA-Seq2Seq model

Wang, C., Xu, L., & Fan, J. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113357 · Cited by 83

A Dynamic Spatial-Temporal Attention-Based GRU Model With Healthy Features for State-of-Health Estimation of Lithium-Ion Batteries

Cui, S., & Joe, I. (2021) · IEEE Access · doi:10.1109/access.2021.3058018 · Cited by 83

Spatiotemporal and frequential cascaded attention networks for speech emotion recognition

Li, S., Xing, X., Fan, W., Cai, B., Fordson, P., & Xu, X. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.02.094 · Cited by 79

RCL-Learning: ResNet and convolutional long short-term memory-based spatiotemporal air pollutant concentration prediction model

Zhang, B., Zou, G., Qin, D., Ni, Q., Mao, H., & Li, M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118017 · Cited by 79

Graph Convolutional Encoders for Syntax-aware Neural Machine Translation

Bastings, J., Titov, I., Aziz, W., Marcheggiani, D., & Simaan, K. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1209 · Cited by 97

A Scenario-Generic Neural Machine Translation Data Augmentation Method

Liu, X., He, J., Liu, M., Yin, Z., Yin, L., & Zheng, W. (2023) · Electronics · doi:10.3390/electronics12102320 · Cited by 78

Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning

Li, F., Liu, W., & Yu, H. Q. (2018) · JMIR Medical Informatics · doi:10.2196/12159 · Cited by 81

Augmenting Neural Networks with First-order Logic

Li, T., & Srikumar, V. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1028 · Cited by 87

Context-aware Deep Model for Joint Mobility and Time Prediction

Chen, Y., Long, C., Cong, G., & Li, C. (2020) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3336191.3371837 · Cited by 78

AGCN: Attention-based graph convolutional networks for drug-drug interaction extraction

Park, C., Park, J., & Park, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113538 · Cited by 82

Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare

Lu, C. H., Reddy, C. K. A., Chakraborty, P., Kleinberg, S., & Ning, Y. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/486 · Cited by 95

Combining spatial response features and machine learning classifiers for landslide susceptibility mapping

Wei, R., Ye, C., Sui, T., Ge, Y., Li, Y., & Li, J. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102681 · Cited by 79

Bayesian Spatio-Temporal Graph Transformer Network (B-Star) for Multi-Aircraft Trajectory Prediction

Pang, Y., Zhao, X., Hu, J., Yan, H., & Liu, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108998 · Cited by 84

Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention

Gao, Y., Miyata, S., & Akashi, Y. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119288 · Cited by 79

DeepGRU: Deep Gesture Recognition Utility

Maghoumi, M., & LaViola, J. J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33720-9_2 · Cited by 93

A Numerical-Based Attention Method for Stock Market Prediction With Dual Information

Liu, G., & Wang, X. (2018) · IEEE Access · doi:10.1109/access.2018.2886367 · Cited by 79

E-LSTM: An Efficient Hardware Architecture for Long Short-Term Memory

Wang, M., Wang, Z., Lu, J., Lin, J. T., & Wang, Z. (2019) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2019.2911739 · Cited by 80

Differentiable Dynamic Programming for Structured Prediction and AttentionPreprint

Mensch, A., & Blondel, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.03676 · Cited by 94

Modeling Coherence for Neural Machine Translation with Dynamic and Topic CachesPreprint

Kuang, S., Xiong, D., Luo, W., & Zhou, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.11221 · Cited by 78

Neural reverse engineering of stripped binaries using augmented control flow graphs

David, Y., Alon, U., & Yahav, E. (2020) · Proceedings of the ACM on Programming Languages · doi:10.1145/3428293 · Cited by 84

Recurrent Neural Networks in Linguistic Theory: Revisiting Pinker and Prince (1988) and the Past Tense Debate

Kirov, C., & Cotterell, R. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00247 · Cited by 87

Augmenting Neural Response Generation with Context-Aware Topical Attention

Dziri, N., Kamalloo, E., Mathewson, K. W., & Zai͏̈ane, O. R. (2019) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/w19-4103 · Cited by 77

Modeling multi-aspects within one opinionated sentence simultaneously for aspect-level sentiment analysis

Ma, X., Zeng, J., Peng, L., Fortino, G., & Zhang⋆, Y. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.10.041 · Cited by 79

Latent Variable Model for Multi-modal Translation

Calixto, I., Rios, M., & Aziz, W. F. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1642 · Cited by 77

DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications

Sekhon, A., Singh, R., & Qi, Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty612 · Cited by 78

Online Segment to Segment Neural Transduction

Yu, L., Buys, J., & Blunsom, P. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1138 · Cited by 84

Modeling Emotion in Complex Stories: The Stanford Emotional Narratives Dataset

Ong, D. C., Wu, Z., Zhi‐Xuan, T., Reddan, M. C., Kahhalé, I., Mattek, A. M., & Zaki, J. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2955949 · Cited by 81

Mapping Natural Language Instructions to Mobile UI Action Sequences

Li, Y., He, J., Zhou, X., Zhang, Y., & Baldridge, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.729 · Cited by 209

Advancements in Oncology with Artificial Intelligence—A Review Article

Vobugari, N., Raja, V., Sethi, U., Gandhi, K. D., Raja, K., & Surani, S. (2022) · Cancers · doi:10.3390/cancers14051349 · Cited by 80

Comprehensive benchmark and architectural analysis of deep learning models for nanopore sequencing basecalling

Pagès-Gallego, M., & de Ridder, J. (2023) · Genome biology · doi:10.1186/s13059-023-02903-2 · Cited by 77

Deep image captioning: A review of methods, trends and future challenges

Xu, L., Tang, Q., Lv, J., Zheng, B., Zeng, X., & Li, W. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126287 · Cited by 79

Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG

Mao, H., Zhang, Z., Xiao, Z., & Gong, Z. (2019) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/eqik7357 · Cited by 77

SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code RepresentationPreprint

Wang, X. E., Wang, Y., Mi, F., Zhou, P., Wan, Y., Xiao, L., Li, L., Wu, H., Liu, J., & Jiang, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.04556 · Cited by 170

Neural Demographic Prediction using Search Query

Wu, C., Wu, F., Liu, J., He, S., Huang, Y., & Xie, X. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291034 · Cited by 75

Variational Recurrent Neural Machine Translation

Su, J., Wu, S., Xiong, D., Lu, Y., Han, X., & Zhang, B. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11985 · Cited by 79

High-Risk Prediction of Cardiovascular Diseases via Attention-Based Deep Neural Networks

An, Y., Huang, N., Chen, X., Wu, F., & Wang, J. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2935059 · Cited by 84

Pattern generation strategies for improving recognition of Handwritten Mathematical Expressions

Le, A. D., Indurkhya, B., & Nakagawa, M. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.09.002 · Cited by 76

Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTS

He, M., Deng, Y., & He, L. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1972 · Cited by 76

Machine Learning in Chemistry

Janet, J. P., & Kulik, H. J. (2020) · ACS in focus · doi:10.1021/acs.infocus.7e4001 · Cited by 81

Split, Embed and Merge: An accurate table structure recognizer

Zhang, Z., Zhang, J., Du, J., & Wang, F. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108565 · Cited by 84

Multi-label text classification via joint learning from label embedding and label correlation

Liu, H., Chen, G., Li, P., Zhao, P., & Wu, X. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.031 · Cited by 76

EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection

Tong, X., Sun, B., Wei, J., Zuo, Z., & Su, S. (2021) · Remote Sensing · doi:10.3390/rs13163200 · Cited by 76

SleePyCo: Automatic sleep scoring with feature pyramid and contrastive learning

Lee, S., Yu, Y., Back, S., Seo, H., & Lee, K. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122551 · Cited by 83

Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis

Pham, H., Manzini, T., Liang, P. P., & Póczos, B. (2018) · doi:10.18653/v1/w18-3308 · Cited by 82

Non-local Attention Optimized Deep Image CompressionPreprint

Liu, H., Chen, T., Guo, P., Shen, Q., Cao, X., Wang, Y., & Ma, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.09757 · Cited by 76

Using Target-side Monolingual Data for Neural Machine Translation through Multi-task Learning

Domhan, T., & Hieber, F. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1158 · Cited by 82

MuVAN: A Multi-view Attention Network for Multivariate Temporal Data

Yuan, Y., Xun, G., Ma, F., Wang, Y., Du, N., Jia, K., Su, L., & Zhang, A. (2018) · Proceedings · doi:10.1109/icdm.2018.00087 · Cited by 73

Entity matching with transformer architectures - a step forward in data integration

Brunner, U., & Stockinger, K. (2020) · Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) · doi:10.21256/zhaw-19637 · Cited by 82

We Need To Talk About Random Splits

Søgaard, A., Ebert, S., Bastings, J., & Filippova, K. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.156 · Cited by 95

ConvS2S-VC: Fully Convolutional Sequence-to-Sequence Voice Conversion

Kameoka, H., Tanaka, K., Kwaśny, D., Kaneko, T., & Hojo, N. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3001456 · Cited by 75

An Attention-based Rumor Detection Model with Tree-structured Recursive Neural Networks

Ma, J., Gao, W. G., Joty, S., & Wong, K. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3391250 · Cited by 79

Deep learning, graph-based text representation and classification: a survey, perspectives and challenges

Pham, P., Nguyen, L. T. T., Witold Pedrycz, C., & Vo, B. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10265-7 · Cited by 80

A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

Yu, S., Zhang, Z., Wang, S., Huang, X. M., & Lei, Q. (2023) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2023.06.010 · Cited by 78

Fast Hyperspectral Image Classification Combining Transformers and SimAM-Based CNNs

Liang, L., Zhang, Y., Zhang, S., Li, J., Plaza, A., & Kang, X. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3309245 · Cited by 81

De Novo Molecule Design by Translating from Reduced Graphs to SMILES

Pogány, P., Arad, N., Genway, S., & Pickett, S. D. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00626 · Cited by 74

Structure-Infused Copy Mechanisms for Abstractive SummarizationPreprint

Song, K., Zhao, L., & Liu, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.05658 · Cited by 77

How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures

Domhan, T. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1167 · Cited by 74

Simple and Effective Text Simplification Using Semantic and Neural Methods

Sulem, E., Abend, O., & Rappoport, A. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1016 · Cited by 74

DeepType

Xu, M., Qian, F., Mei, Q., Huang, K. R., & Liu, X. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3287075 · Cited by 76

Neural System Combination for Machine Translation

Zhou, L., Hu, W., Zhang, J., & Zong, C. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2060 · Cited by 74

Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity

Xu, X., Dušek, O., Konstas, I., & Rieser, V. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1432 · Cited by 80

Learning to Answer Complex Questions over Knowledge Bases with Query Composition

Bhutani, N., Zheng, X., & Jagadish, H. V. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3358033 · Cited by 74

Using improved gradient-boosted decision tree algorithm based on Kalman filter (GBDT-KF) in time series prediction

Li, L., Dai, S., Cao, Z., Hong, J., Jiang, S., & Yang, K. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-019-03130-y · Cited by 74

A Deep Learning-Based Chemical System for QSAR Prediction

Hu, S., Chen, P., Gu, P., & Wang, B. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2977009 · Cited by 74

Enhancing Machine Translation with Dependency-Aware Self-Attention

Bugliarello, E., & Okazaki, N. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.147 · Cited by 78

Graph attention temporal convolutional network for traffic speed forecasting on road networks

Zhang, K., He, F., Zhang, Z., Lin, X., & Li, M. (2020) · Transportmetrica B Transport Dynamics · doi:10.1080/21680566.2020.1822765 · Cited by 76

A survey for trust-aware recommender systems: A deep learning perspective

Dong, M., Yuan, F., Yao, L., Wang, X., Xu, X., & Zhu, L. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108954 · Cited by 93

ViolenceNet: Dense Multi-Head Self-Attention with Bidirectional Convolutional LSTM for Detecting Violence

Rendón-Segador, F. J., Álvarez-García, J. A., Enríquez, F., & Déniz, Ó. (2021) · Electronics · doi:10.3390/electronics10131601 · Cited by 74

Parallelizing Word2Vec in Shared and Distributed Memory

Ji, S., Satish, N., Li, S., & Dubey, P. K. (2019) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2019.2904058 · Cited by 74

Hierarchical Attention Network for Action Recognition in VideosPreprint

Wang, Y., Wang, S., Tang, J., O’Hare, N., Chang, Y., & Li, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.06416 · Cited by 82

RNN Decoding of Linear Block Codes

Nachmani, E., Marciano, E., Burshtein, D., & Be'ery, Y. (2017) · arXiv · doi:10.48550/arxiv.1702.07560 · Cited by 93

Hierarchical Attention Flow for Multiple-Choice Reading Comprehension

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