Gated-Attention Readers for Text Comprehension

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

Glove: Global Vectors for Word Representation

Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071

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

On the difficulty of training recurrent neural networks

Pascanu, R., Mikolov, T., & Bengio, Y. (2012) · arXiv · doi:10.48550/arxiv.1211.5063 · Cited by 4,310

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

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) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317

Teaching Machines to Read and ComprehendPreprint

Hermann, K. M., Kočiský, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03340 · Cited by 2,855

Bidirectional Attention Flow for Machine ComprehensionPreprint

Seo, M. J., Kembhavi, A., Farhadi, A., & Hajishirzi, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01603 · Cited by 1,698

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

Kumar, A., Irsoy, O., Ondruska, P., Iyyer, M., Bradbury, J., Gulrajani, I., Zhong, V., Paulus, R., & Socher, R. (2015) · arXiv · doi:10.48550/arxiv.1506.07285 · Cited by 1,020

Effective Subword Segmentation for Text Comprehension

Zhang, Z., Zhao, H., Ling, K., Li, J., Li, Z., He, S., & Fu, G. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2922537 · Cited by 31

A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction

Niu, Y., Jiao, F., Zhou, M., Yao, T., Xu, J., & Huang, M. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.361 · Cited by 33

Multi Task Mutual Learning for Joint Sentiment Classification and Topic Detection

Gui, L., Leng, J. X., Zhou, J., Xu, R., & He, Y. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2999489 · Cited by 31

PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and TextPreprint

Sun, H., Bedrax-Weiss, T., & Cohen, W. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.09537 · Cited by 33

Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions

Chen, Z., Cui, Y., Ma, W., Wang, S., & Hu, G. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016276 · Cited by 32

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

Nguyen, K. V., Tran, K. V., Luu, S. T., Nguyen, A. G.-T., & Nguyen, N. L.-T. (2020) · IEEE Access · doi:10.1109/access.2020.3035701 · Cited by 32

Complete quadruple extraction using a two-stage neural model for aspect-based sentiment analysis

Zhang, H., Chen, Z., Chen, B. Y., Hu, B., Li, M., Yang, C., & Jiang, B. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.027 · Cited by 33

Improving Pre-Trained Multilingual Model with Vocabulary Expansion

Wang, H., Yu, D., Sun, K., Chen, J., & Yu, D. (2019) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k19-1030 · Cited by 41

Multi-range Reasoning for Machine ComprehensionPreprint

Tay, Y., Luu, A. T., & Hui, S. (2018) · arXiv.org · doi:10.48550/arxiv.1803.09074 · Cited by 36

Mining Significant Microblogs for Misinformation Identification

Liu, Q., Yu, F. N., Wu, S., & Wang, L. (2018) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3173458 · Cited by 39

Temporal Attention Networks for Multitemporal Multisensor Crop Classification

Li, Z., Chen, G., & Zhang, T. (2019) · IEEE Access · doi:10.1109/access.2019.2939152 · Cited by 29

BioMRC: A Dataset for Biomedical Machine Reading Comprehension

Pappas, D., Stavropoulos, P., Androutsopoulos, I., & McDonald, R. (2020) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2020.bionlp-1.15 · Cited by 36

First-principle study on honeycomb fluorated-InTe monolayer with large Rashba spin splitting and direct bandgap

Li, K., Xian, X., Wang, J., & Yu, N. (2018) · Applied Surface Science · doi:10.1016/j.apsusc.2018.11.214 · Cited by 30

Linguistic Knowledge as Memory for Recurrent Neural NetworksPreprint

Dhingra, B., Yang, Z., Cohen, W. W., & Salakhutdinov, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.02620 · Cited by 34

A Comparative Study of Word Embeddings for Reading ComprehensionPreprint

Dhingra, B., Liu, H., Salakhutdinov, R., & Cohen, W. W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00993 · Cited by 28

Graph attention contrastive learning with missing modality for multimodal recommendation

Zhao, W., Yang, K., Ding, P., Na, C., & Li, W. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113035 · Cited by 26

Dialog Generation Using Multi-Turn Reasoning Neural Networks

Wu, X., Martinez, A., & Klyen, M. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1186 · Cited by 25

ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice Questions

Parikh, S., Sai, A. B., Nema, P., & Khapra, M. M. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/594 · Cited by 25

Transformer-Based Extractive Social Media Question Answering on TweetQA

Butt, S., Ashraf, N., Fahim, H., Sidorov, G., & Gelbukh, A. F. (2021) · Computación y Sistemas · doi:10.13053/cys-25-1-3897 · Cited by 25

Logiformer

Xu, F., Liu, J., Lin, Q., Pan, Y., & Zhang, L. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3532016 · Cited by 24

Explicit Contextual Semantics for Text ComprehensionPreprint

Zhang, Z., Wu, Y., Li, Z., & Zhao, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.02794 · Cited by 33

Proceedings of the 2nd Workshop on Machine Reading for Question Answering

(2019) · doi:10.18653/v1/d19-58 · Cited by 26

Diagnostic Prediction with Sequence-of-sets Representation Learning for Clinical Events

Zhang, T., Chen, M., & Bui, A. A. T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59137-3_31 · Cited by 23

GCRNN: graph convolutional recurrent neural network for compound–protein interaction prediction

Elbasani, E., Njimbouom, S. N., Oh, T., Kim, E.-H., Lee, H., & Kim, J. (2021) · BMC Bioinformatics · doi:10.1186/s12859-022-04560-x · Cited by 22

Exploiting Sentence Embedding for Medical Question Answering

Hao, Y., Liu, X., Wu, J. Y., & Lv, P. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301938 · Cited by 22

Multi-Layer Transformer Aggregation Encoder for Answer Generation

Shang, S., Liu, J., & Yang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2993875 · Cited by 22

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis

Kim, S., Park, D., Choi, Y., Lee, K., Kim, B., Jeon, M., Kim, J., Tan, A. C., & Kang, J. (2018) · JMIR Medical Informatics · doi:10.2196/medinform.8751 · Cited by 21

Robust and Scalable Differentiable Neural Computer for Question Answering

Franke, J., Niehues, J., & Waibel, A. (2018) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/w18-2606 · Cited by 21

Porn Streamer Recognition in Live Video Streaming via Attention-Gated Multimodal Deep Features

Wang, L., Zhang, J., Tian, Q., Li, C., & Li, Z. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2958871 · Cited by 21

Learning Loss Functions for Semi-supervised Learning via Discriminative Adversarial NetworksPreprint

dos Santos, C. N., Wadhawan, K., & Zhou, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.02198 · Cited by 25

MemexQA: Visual Memex Question AnsweringPreprint

Jiang, L., Liang, J., Cao, L., Kalantidis, Y., Farfade, S., & Hauptmann, A. G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.01336 · Cited by 28

Subword-augmented Embedding for Cloze Reading Comprehension

Zhang, Z. (2024) · TIB Data Manager · doi:10.57702/xl3y0nut · Cited by 21

Attention-Based Recurrent Temporal Restricted Boltzmann Machine for Radar High Resolution Range Profile Sequence Recognition

Zhang, Y., Gao, X., Peng, X., Ye, J., & Li, X. (2018) · Sensors · doi:10.3390/s18051585 · Cited by 20

Towards Knowledge Enhanced Language Model for Machine Reading Comprehension

Gong, P., Liu, J., Yang, Y., & He, H. (2020) · IEEE Access · doi:10.1109/access.2020.3044308 · Cited by 20

Broad Context Language Modeling as Reading Comprehension

Chu, Z., Wang, H., Gimpel, K., & McAllester, D. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2009 · Cited by 21

Frame-based Neural Network for Machine Reading Comprehension

Guo, S., Guan, Y., Tan, H., Li, R., & Li, X. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106889 · Cited by 20

Embodied Language Grounding With 3D Visual Feature Representations

Prabhudesai, M., Tung, H.-Y. F., Javed, S. A., Sieb, M., Harley, A. W., & Fragkiadaki, K. (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/cvpr42600.2020.00229 · Cited by 20

Subword-augmented Embedding for Cloze Reading ComprehensionPreprint

Zhang, Z., Huang, Y., & Zhao, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.09103 · Cited by 20

From Question to Text

Huang, H., Wei, X., Nie, L., Mao, X.-L., & Xu, X.-S. (2018) · ACM Transactions on Information Systems · doi:10.1145/3233771 · Cited by 19

Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text

Yu, J., Zha, Z.-J., & Yin, J. P. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1217 · Cited by 19

Effective Character-Augmented Word Embedding for Machine Reading Comprehension

Zhang, Z., Huang, Y., Zhu, P., & Zhao, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99495-6_3 · Cited by 18

Towards Interpreting BERT for Reading Comprehension Based QA

Ramnath, S., Nema, P., Sahni, D., & Khapra, M. M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.261 · Cited by 28

Letter-Level Online Writer Identification

Chen, Z., Yu, H.-X., Wu, A., & Zheng, W. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01414-y · Cited by 18

Dynamic dual graph networks for textbook question answering

Wang, Y., Liu, J., Ma, J., Zeng, H., Zhang, L., & Li, J. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109441 · Cited by 17

Discriminative Sentence Modeling for Story Ending Prediction

Cui, Y., Che, W., Zhang, W., Liu, T., Wang, S., & Hu, G. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6260 · Cited by 16

Read, Attend, and Exclude: Multi-Choice Reading Comprehension by Mimicking Human Reasoning Process

Zhang, C., Luo, C., Lu, J., Liu, A., Bai, B., Bai, K., & Xu, Z. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401326 · Cited by 17

Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking

Guo, J., Shuang, K., Li, J., & Wang, Z. (2021) · doi:10.18653/v1/2021.acl-long.12 · Cited by 18

Audio-Oriented Multimodal Machine Comprehension via Dynamic Inter- and Intra-modality Attention

Huang, Z., Liu, F., Wu, X., Ge, S., Wang, H., Fan, W., & Zou, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17548 · Cited by 18

Dual-axial self-attention network for text classification

Zhang, X., Qiu, X., Pang, J., Liu, F., & Li, X. (2021) · Science China Information Sciences · doi:10.1007/s11432-019-2744-2 · Cited by 16

Smarnet: Teaching Machines to Read and Comprehend Like HumanPreprint

Chen, Z., Yang, R., Cao, B., Zhao, Z., Cai, D., & He, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.02772 · Cited by 18

Multi-modal adaptive gated mechanism for visual question answering

Xu, Y., Zhang, L., & Shen, X. (2023) · PLoS ONE · doi:10.1371/journal.pone.0287557 · Cited by 18

A Survey of Semantic Parsing Techniques

Jiang, P., & Cai, X. (2024) · Symmetry · doi:10.3390/sym16091201 · Cited by 18

Relation Classification via LSTMs Based on Sequence and Tree Structure

Dai, Y., Guo, W., Chen, X., & Zhang, Z. (2018) · IEEE Access · doi:10.1109/access.2018.2877934 · Cited by 14

Multi-task joint training model for machine reading comprehension

Li, F., Shan, Y., Mao, X., Ren, X., Liu, X., & Zhang, S. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.02.082 · Cited by 15

Can Small and Synthetic Benchmarks Drive Modeling Innovation? A Retrospective Study of Question Answering Modeling Approaches.Preprint

Liu, N. F., Lee, T., Jia, R., & Liang, P. (2021) · arXiv (Cornell University) · Cited by 17

Scene Restoring for Narrative Machine Reading Comprehension

Tian, Z., Zhang, Y., Liu, K., Zhao, J., Jia, Y., & Sheng, Z. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.247 · Cited by 14

On Knowledge distillation from complex networks for response prediction

Arora, S., Khapra, M. M., & Ramaswamy, H. G. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1382 · Cited by 14

Multi-Task Learning with Generative Adversarial Training for Multi-Passage Machine Reading Comprehension

Ren, Q., Cheng, X., & Su, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6396 · Cited by 13

Separating Answers from Queries for Neural Reading ComprehensionPreprint

Weissenborn, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.03316 · Cited by 18

Dynamic Fusion Networks for Machine Reading ComprehensionPreprint

Xu, Y., Liu, J., Gao, J., Shen, Y., & Liu, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.04964 · Cited by 14

Saliency Learning: Teaching the Model Where to Pay AttentionPreprint

Ghaeini, R., Fern, X. Z., Shahbazi, H., & Tadepalli, P. V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.08649 · Cited by 23

SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning

Zheng, B., Yang, X., Ruan, Y.-P., Ling, Z.-H., Liu, Q., Wei, S., & Zhu, X. (2021) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2021.semeval-1.4 · Cited by 15

Conditioned fully convolutional denoising autoencoder for multi-target NILM

García-Pérez, D., Pérez, D., Papapetrou, P., Díaz, I., Cuadrado, A. A., Enguita, J. M., & Domí­nguez, M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10552-0 · Cited by 14

Reading Comprehension with Graph-based Temporal-Casual Reasoning

Sun, Y., Gong, C., & Qu, Y. (2018) · International Conference on Computational Linguistics · Cited by 13

Evidence Integration for Multi-Hop Reading Comprehension With Graph Neural Networks

Song, L., Wang, Z., Yu, M., Zhang, Y., Florian, R., & Gildea, D. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2982894 · Cited by 128

A Natural Language Process-Based Framework for Automatic Association Word Extraction

Hu, Z., Luo, J., Zhang, C., & Li, W. (2019) · IEEE Access · doi:10.1109/access.2019.2962154 · Cited by 13

Unified Generative Adversarial Networks for Multiple-Choice Oriented Machine Comprehension

Liu, Z., Xiao, K., Jin, B., Huang, K., Huang, D., & Zhang, Y. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3372120 · Cited by 13

Multi-task Learning for Multi-modal Emotion Recognition and Sentiment AnalysisPreprint

Akhtar, S., Chauhan, D. S., Ghosal, D., Poria, S., Ekbal, A., & Bhattacharyya, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.05812 · Cited by 21

Entity Tracking Improves Cloze-style Reading Comprehension

Hoang, L., Wiseman, S., & Rush, A. M. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1130 · Cited by 12

Fact-Driven Logical Reasoning for Machine Reading Comprehension

Ouyang, S., Zhang, Z., & Zhao, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29850 · Cited by 27

Chinese Pinyin Aided IME, Input What You Have Not Keystroked Yet

Huang, Y., & Zhao, H. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1321 · Cited by 12

Verification mechanism to obtain an elaborate answer span in machine reading comprehension

Yu, P., Li, X., Song, J., Luo, Y., Hu, S., & Qian, W. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.084 · Cited by 12

A Simple Data Augmentation Method to Improve the Performance of Named Entity Recognition Models in Medical Domain

Issifu, A. M., & Ganiz, M. C. (2021) · International Conference on Computer Science and Engineering (UBMK) · doi:10.1109/ubmk52708.2021.9558986 · Cited by 13

elBERto: Self-supervised commonsense learning for question answering

Zhan, X., Li, Y., Dong, X., Liang, X., Hu, Z., & Carin, L. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109964 · Cited by 12

A Gated Dilated Convolution with Attention Model for Clinical Cloze-Style Reading Comprehension

Wang, B., Zhang, X., Zhou, X., & Li, J. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17041323 · Cited by 12

Highway Transformer: Self-Gating Enhanced Self-Attentive Networks

Chai, Y., Jin, S., & Hou, X. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.616 · Cited by 25

ExpMRC: explainability evaluation for machine reading comprehension

Cui, Y., Liu, T., Che, W., Chen, Z., & Wang, S. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e09290 · Cited by 12

An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension TasksPreprint

Shen, Y., Liu, X., Duh, K., & Gao, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.03230 · Cited by 12

Weaver: Deep Co-Encoding of Questions and Documents for Machine ReadingPreprint

Raison, M., Mazaré, P.-E., Das, R., & Bordes, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.10490 · Cited by 15

Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds

Labutov, I., Yang, B., Prakash, A., & Azaria, A. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1077 · Cited by 11

Neural Article Pair Modeling for Wikipedia Sub-article Matching

Chen, M., Meng, C., Huang, G., & Zaniolo, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10997-4_1 · Cited by 11

Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension

Tay, Y., Luu, A. T., & Hui, S. C. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1238 · Cited by 10

Intelligent Retrieval and Comprehension of Entrepreneurship Education Resources Based on Semantic Summarization of Knowledge Graphs

Hai-yang, Y., Wang, E., Lang, Q., & Wang, J. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3364155 · Cited by 15

Teaching Machines to Read, Answer and Explain

Cui, Y., Liu, T., Che, W., Chen, Z., & Wang, S. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3156789 · Cited by 13

Review of Deep Learning Techniques for Improving the Performance of Machine Reading Comprehension Problem

Farooq, H., & Kaushik, B. (2020) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs48265.2020.9121015 · Cited by 11

Multi-Hop Transformer for Document-Level Machine Translation

Zhang, L., Zhang, T., Zhang, H., Yang, B., Ye, W., & Zhang, S. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.309 · Cited by 11

Mining Significant Microblogs for Misinformation Identification: An Attention-based ApproachPreprint

Liu, Q., Yu, F. N., Wu, S., & Wang, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.06314 · Cited by 11

I Know What You Want: Semantic Learning for Text ComprehensionPreprint

Zhang, Z., Wu, Y., Li, Z., He, S., Zhao, H., Zhou, X., & Zhou, X. (2018) · arXiv (Cornell University) · Cited by 12

Emergent Predication Structure in Hidden State Vectors of Neural Readers

Wang, H., Onishi, T., Gimpel, K., & McAllester, D. (2017) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w17-2604 · Cited by 10

Utterance-focusing multiway-matching network for dialogue-based multiple-choice machine reading comprehension

Gu, Y., Gui, X., & Li, D. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.107 · Cited by 10

Chat-Oriented Social Engineering Attack Detection Using Attention-based Bi-LSTM and CNN

Lan, Y. (2021) · 2021 2nd International Conference on Computing and Data Science (CDS) · doi:10.1109/cds52072.2021.00089 · Cited by 10

Cross-document attention-based gated fusion network for automated medical licensing exam

Liu, J., Ren, J., Lu, Z., He, W., Cui, M., Zhang, Z., & Bai, R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117588 · Cited by 10

Adversarial Knowledge Distillation Based Biomedical Factoid Question Answering

Bai, J., Yin, C., Zhang, J., Wang, Y., Dong, Y., Rong, W., & Xiong, Z. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3161032 · Cited by 12

Multilingual multi-aspect explainability analyses on machine reading comprehension models

Cui, Y., Zhang, W.-N., Che, W., Liu, T., Chen, Z., & Wang, S. (2022) · iScience · doi:10.1016/j.isci.2022.104176 · Cited by 12

To Answer or Not To Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning

Ji, Y., Chen, L., Dou, C., Ma, B., & Li, X. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.96 · Cited by 12

Sequential Attention: A Context-Aware Alignment Function for Machine Reading

Brarda, S., Yeres, P., & Bowman, S. R. (2017) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w17-2610 · Cited by 10

Learning Transferable Features For Open-Domain Question Answering

Zuin, G. L., Chaimowicz, L., & Veloso, A. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489057 · Cited by 10

Affect and Personality Aided Modeling of Transcribed Speech for Depression Severity Estimation

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