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

RACE: Large-scale ReAding Comprehension Dataset From Examinations

Lai, G., Xie, Q., Liu, H., Yang, Y., & Hovy, E. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1082 · Cited by 1,044

Gated Self-Matching Networks for Reading Comprehension and Question Answering

Wang, W., Yang, N., Wei, F., Chang, B., & Zhou, M. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1018 · Cited by 674

Attention in Natural Language Processing

Galassi, A., Lippi, M., & Torroni, P. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3019893 · Cited by 726

Differentiable Learning of Logical Rules for Knowledge Base ReasoningPreprint

Yang, F., Yang, Z., & Cohen, W. W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.08367 · Cited by 434

Multifaceted protein–protein interaction prediction based on Siamese residual RCNN

Chen, M., Ju, C. J. ‐., Zhou, G., Chen, X., Zhang, T., Chang, K.-W., Zaniolo, C., & Wang, W. W. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz328 · Cited by 388

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

Sun, H., Bedrax-Weiss, T., & Cohen, W. W. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1242 · Cited by 443

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

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

R3: Reinforced Ranker-Reader for Open-Domain Question Answering

Wang, S., Yu, M., Guo, X., Wang, Z., Klinger, T., Zhang, W., Chang, S., Tesauro, G., Zhou, B., & Jiang, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12053 · Cited by 351

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

Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

Huang, L., Bras, R. J. L., Bhagavatula, C., & Choi, Y. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1243 · Cited by 302

ReasoNet

Shen, Y., Huang, P.-S., Gao, J., & Chen, W. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098177 · Cited by 276

DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension

Sun, K., Yu, D., Chen, J., Yu, D., Choi, Y., & Cardie, C. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00264 · Cited by 252

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

Akhtar, M. S., Chauhan, D. S., Ghosal, D., Poria, S., Ekbal, A., & Bhattacharyya, P. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1034 · Cited by 238

SG-Net: Syntax-Guided Machine Reading Comprehension

Zhang, Z., Wu, Y., Zhou, J., Duan, S., Zhao, H. Y., & Wang, R. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6511 · Cited by 198

Stochastic Answer Networks for Machine Reading Comprehension

Liu, X., Shen, Y., Duh, K., & Gao, J. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1157 · Cited by 205

Contextual Inter-modal Attention for Multi-modal Sentiment Analysis

Ghosal, D., Akhtar, M. S., Chauhan, D. S., Poria, S., Ekbal, A., & Bhattacharyya, P. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1382 · Cited by 196

Retrospective Reader for Machine Reading Comprehension

Zhang, Z., Yang, J., & Zhao, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17705 · Cited by 215

Commonsense for Generative Multi-Hop Question Answering Tasks

Bauer, L., Wang, Y., & Bansal, M. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1454 · Cited by 169

Feature-wise transformations

Dumoulin, V., Perez, E., Schucher, N., Strub, F., de Vries, H., Courville, A. C., & Bengio, Y. (2018) · Distill · doi:10.23915/distill.00011 · Cited by 192

Neural Machine Reading Comprehension: Methods and Trends

Liu, S., Zhang, X., Zhang, S., Wang, H., & Zhang, W. (2019) · Applied Sciences · doi:10.3390/app9183698 · Cited by 160

Comprehension-Guided Referring Expressions

Luo, R., & Shakhnarovich, G. (2017) · 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.2017.333 · Cited by 152

Language-Based Image Editing with Recurrent Attentive Models

Chen, J., Shen, Y., Gao, J., Liu, J., & Liu, X. (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.2018.00909 · Cited by 141

Denoising Distantly Supervised Open-Domain Question Answering

Lin, Y., Ji, H., Liu, Z., & Sun, M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1161 · Cited by 143

Semi-Supervised QA with Generative Domain-Adaptive Nets

Yang, Z., Hu, J., Salakhutdinov, R., & Cohen, W. W. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1096 · Cited by 151

JEC-QA: A Legal-Domain Question Answering Dataset

Zhong, H., Xiao, C., Tu, C., Zhang, T., Liu, Z., & Sun, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6519 · Cited by 166

LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning

Jian, L., Cui, L., Liu, H., Huang, D., Wang, Y., & Zhang, Y. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/501 · Cited by 431

Gated-Attention Architectures for Task-Oriented Language Grounding

Chaplot, D. S., Sathyendra, K. M., Pasumarthi, R. K., Rajagopal, D., & Salakhutdinov, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11832 · Cited by 130

Anatomically Constrained Deep Learning for Automating Dental CBCT Segmentation and Lesion Detection

Zheng, Z., Yan, H., Setzer, F. C., Shi, K. J., Mupparapu, M., & Li, J. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.3025871 · Cited by 129

Multi-modal advanced deep learning architectures for breast cancer survival prediction

Arya, N., & Saha, S. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106965 · Cited by 127

Multilogue-Net: A Context-Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation

Shenoy, A., & Sardana, A. (2020) · doi:10.18653/v1/2020.challengehml-1.3 · Cited by 124

Framework for Deep Learning-Based Language Models Using Multi-Task Learning in Natural Language Understanding: A Systematic Literature Review and Future Directions

Samant, R. M., Bachute, M. R., Gite, S., & Kotecha, K. (2022) · IEEE Access · doi:10.1109/access.2022.3149798 · Cited by 123

Biomedical Question Answering: A Survey of Approaches and Challenges

Jin, Q., Yuan, Z. P., Xiong, G., Yu, Q., Ying, H., Tan, C., Chen, M., Huang, S., Liu, X., & Yu, S. (2022) · ACM Computing Surveys · doi:10.1145/3490238 · Cited by 145

Large-scale Cloze Test Dataset Created by Teachers

Xie, Q., Lai, G., Dai, Z., & Hovy, E. H. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1257 · Cited by 144

MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering

Longpre, S., Lu, Y., & Daiber, J. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00433 · Cited by 150

SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge

Ostermann, S., Roth, M., Modi, A., Thater, S., & Pinkal, M. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1119 · Cited by 116

Who did What: A Large-Scale Person-Centered Cloze Dataset

Onishi, T., Wang, H., Bansal, M., Gimpel, K., & McAllester, D. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1241 · Cited by 122

NumNet: Machine Reading Comprehension with Numerical Reasoning

Ran, Q., Lin, Y., Li, P., Zhou, J., & Liu, Z. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1251 · Cited by 108

Neural Approaches to Conversational AI

Gao, J., Galley, M., & Li, L. (2019) · now publishers, Inc. eBooks · doi:10.1561/9781680835533 · Cited by 125

Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog

Gan, Z., Cheng, Y., Kholy, A. E., Li, L., Liu, J., & Gao, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1648 · Cited by 112

A Co-Matching Model for Multi-choice Reading Comprehension

Wang, S., Yu, M., Jiang, J., & Chang, S. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2118 · Cited by 106

What Makes Reading Comprehension Questions Easier?

Sugawara, S., Inui, K., Sekine, S., & Aizawa, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1453 · Cited by 111

Automatic Judgment Prediction via Legal Reading Comprehension

Long, S., Tu, C., Liu, Z., & Sun, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32381-3_45 · Cited by 110

Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis

Chauhan, D. S., Akhtar, M. S., Ekbal, A., & Bhattacharyya, P. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1566 · Cited by 97

XQA: A Cross-lingual Open-domain Question Answering Dataset

Liu, J., Lin, Y., Liu, Z., & Sun, M. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1227 · Cited by 103

A Survey on Machine Reading Comprehension Systems

Baradaran, R., Ghiasi, R., & Amirkhani, H. (2022) · Natural Language Engineering · doi:10.1017/s1351324921000395 · Cited by 108

Dialogue Act Recognition via CRF-Attentive Structured Network

Chen, Z., Yang, R., Zhao, Z., Cai, D., & He, X. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3209997 · Cited by 93

Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference

Ghaeini, R., Fern, X., & Tadepalli, P. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1537 · Cited by 91

A Joint Model for Question Answering and Question GenerationPreprint

Wang, T., Yuan, X., & Trischler, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.01450 · Cited by 103

Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension

Yin, Y., Song, Y., & Ming, Z. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1217 · Cited by 86

Multi-step ahead forecasting of regional air quality using spatial-temporal deep neural networks: A case study of Huaihai Economic Zone

Zhang, K., Thé, J. V. G., Xie, G., & Yu, H. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.123231 · Cited by 83

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

Representation Learning for Natural Language Processing

Liu, Z., Lin, Y., & Sun, M. (2020) · Springer eBooks · doi:10.1007/978-981-15-5573-2 · Cited by 103

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

Spatio-Temporal Memory Attention for Image Captioning

Ji, J., Xu, C., Zhang, X., Wang, B., & Song, X. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3004729 · Cited by 79

Tripping through time: Efficient Localization of Activities in Videos

Hahn, M., Kadav, A., Rehg, J. M., & Graf, H. P. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.135 · Cited by 76

R$^3$: Reinforced Reader-Ranker for Open-Domain Question AnsweringPreprint

Wang, S., Yu, M., Guo, X., Wang, Z., Klinger, T., Zhang, W., Chang, S., Tesauro, G. J., Zhou, B., & Jiang, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.00023 · Cited by 75

Hierarchical Attention Flow for Multiple-Choice Reading Comprehension

Zhu, H., Wei, F., Qin, B., & Liu, T. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12040 · Cited by 73

ScienceQA: a novel resource for question answering on scholarly articles

Saikh, T., Ghosal, T., Mittal, A., Ekbal, A., & Bhattacharyya, P. (2022) · International Journal on Digital Libraries · doi:10.1007/s00799-022-00329-y · Cited by 86

Convolution-Based Neural Attention With Applications to Sentiment Classification

Du, J., Gui, L., He, Y., Xu, R., & Wang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2900335 · Cited by 68

Words or Characters? Fine-grained Gating for Reading ComprehensionPreprint

Yang, Z., Dhingra, B., Yuan, Y., Hu, J., Cohen, W. W., & Salakhutdinov, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01724 · Cited by 66

Chinese text classification based on attention mechanism and feature-enhanced fusion neural network

Xie, J., Hou, Y., Wang, Y., Wang, Q., Li, B., Lv, S., & Vorotnitsky, Y. I. (2019) · Computing · doi:10.1007/s00607-019-00766-9 · Cited by 61

Multi-hop Question Answering via Reasoning ChainsPreprint

Chen, J., Lin, S.-T., & Durrett, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.02610 · Cited by 97

ChID: A Large-scale Chinese IDiom Dataset for Cloze Test

Zheng, C., Huang, M., & Sun, A. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1075 · Cited by 82

Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question AnsweringPreprint

Li, P., Li, W., He, Z., Wang, X., Cao, Y. S., Zhou, J., & Xu, W. H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.06275 · Cited by 103

Simple and Effective Semi-Supervised Question Answering

Dhingra, B., Danish, D., & Rajagopal, D. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2092 · Cited by 58

MEMEN: Multi-layer Embedding with Memory Networks for Machine ComprehensionPreprint

Pan, B., Li, H., Zhao, Z., Cao, B., Cai, D., & He, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.09098 · Cited by 58

Breast cancer prediction using gated attentive multimodal deep learning

Kayıkçı, Ş., & Khoshgoftaar, T. M. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00749-w · Cited by 59

Medical Exam Question Answering with Large-scale Reading Comprehension

Zhang, X., Wu, J. Y., He, Z., Liu, X., & Su, Y. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11970 · Cited by 55

DeepCPU: serving RNN-based deep learning models 10x faster

Zhang, M., Rajbhandari, S., Wang, W., & He, Y. (2018) · USENIX Annual Technical Conference · Cited by 56

Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension

Golub, D., Huang, P.-S., He, X., & Deng, L. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1087 · Cited by 54

Embracing data abundance: BookTest Dataset for Reading ComprehensionPreprint

Bajgar, O., Kadlec, R., & Kleindienst, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.00956 · Cited by 52

Question Directed Graph Attention Network for Numerical Reasoning over Text

Chen, K., Xu, W., Cheng, X., Xiaochuan, Z., Zhang, Y., Song, L., Wang, T., Qi, Y., & Chu, W. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.549 · Cited by 56

Cross-Lingual Machine Reading Comprehension

Cui, Y., Che, W., Liu, T., Qin, B., Wang, S., & Hu, G. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1169 · Cited by 50

Sentiment Classification Based on Part-of-Speech and Self-Attention Mechanism

Cheng, K., Yue, Y., & Song, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2967103 · Cited by 49

Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension

Jiang, Y., Joshi, N., Chen, Y.-C., & Bansal, M. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1261 · Cited by 47

A Unified Query-based Generative Model for Question Generation and Question AnsweringPreprint

Song, L., Wang, Z., & Hamza, W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.01058 · Cited by 55

Training a Ranking Function for Open-Domain Question Answering

Htut, P. M., Bowman, S. R., & Cho, K. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/n18-4017 · Cited by 50

Stochastic Answer Networks for Natural Language InferencePreprint

Liu, X., Duh, K., & Gao, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.07888 · Cited by 48

Machine Reading Comprehension: The Role of Contextualized Language Models and BeyondPreprint

Zhang, Z., Zhao, H., & Wang, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.06249 · Cited by 63

Ruminating Reader: Reasoning with Gated Multi-hop Attention

Gong, Y., & Bowman, S. R. (2018) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/w18-2601 · Cited by 46

A Gated Self-attention Memory Network for Answer Selection

Lai, T., Tran, Q. H., Bui, T., & Kihara, D. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1610 · Cited by 42

A Deep Multi-task Contextual Attention Framework for Multi-modal Affect Analysis

Akhtar, M. S., Chauhan, D. S., & Ekbal, A. (2020) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3380744 · Cited by 44

HAS-QA: Hierarchical Answer Spans Model for Open-Domain Question Answering

Pang, L., Lan, Y., Guo, J., Xu, J., Su, L., & Cheng, X. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016875 · Cited by 42

Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension

Wang, Z., Liu, J., Xiao, X., Lyu, Y., & Wu, T. B. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1159 · Cited by 41

Multi-Matching Network for Multiple Choice Reading Comprehension

Tang, M., Cai, J., & Zhuo, H. H. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017088 · Cited by 40

Dynamic Language Models for Continuously Evolving Content

Hombaiah, S. A., Chen, T., Zhang, M., Bendersky, M., & Najork, M. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467162 · Cited by 47

Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog

Ma, K., Jurczyk, T., & Choi, J. D. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1185 · Cited by 38

End-to-End Answer Chunk Extraction and Ranking for Reading ComprehensionPreprint

Yu, Y., Zhang, W., Hasan, K. F., Yu, M., Xiang, B., & Zhou, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09996 · Cited by 38

Multi-Domain Gated CNN for Review Helpfulness Prediction

Chen, C., Qiu, M., Yang, Y., Zhou, J., Huang, J., Li, X., & Bao, F. S. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313587 · Cited by 37

Analyzing and Predicting Emoji Usages in Social Media

Zhao, P., Jia, J., An, Y., Liang, J., Xie, L., & Luo, J. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3186344 · Cited by 36

VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls

Sawhney, R., Khanna, P., Aggarwal, A., Jain, T., Mathur, P., & Shah, R. R. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.643 · Cited by 41

Detecting and explaining unfairness in consumer contracts through memory networks

Ruggeri, F., Lagioia, F., Lippi, M., & Torroni, P. (2021) · Artificial Intelligence and Law · doi:10.1007/s10506-021-09288-2 · Cited by 45

Deep learning-based question answering: a survey

Abdel-Nabi, H., Awajan, A., & Ali, M. Z. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01783-5 · Cited by 44

Protein–protein interaction prediction based on ordinal regression and recurrent convolutional neural networks

Xu, W., Gao, Y., Wang, Y., & Guan, J. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04369-0 · Cited by 35

Proceedings of the 3rd Workshop on Noisy User-generated Text

(2017) · doi:10.18653/v1/w17-44 · Cited by 36

U-Net: Machine Reading Comprehension with Unanswerable QuestionsPreprint

Sun, F., Li, L., Qiu, X., & Liu, Y. (2018) · arXiv.org · doi:10.48550/arxiv.1810.06638 · Cited by 49

Attending to Entities for Better Text Understanding

Cheng, P., & Erk, K. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6254 · Cited by 41

Structural Embedding of Syntactic Trees for Machine Comprehension

Liu, R., Hu, J., Wei, W., Yang, Z., & Nyberg, E. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1085 · Cited by 39

Condensed Memory Networks for Clinical Diagnostic Inferencing

Prakash, A., Zhao, S., Hasan, S. A., Datla, V. V., Lee, K., Qadir, A., Liu, J., & Farri, O. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10964 · Cited by 39

A Question-Focused Multi-Factor Attention Network for Question Answering

Kundu, S., & Ng, H. T. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12065 · Cited by 33

Dependent Gated Reading for Cloze-Style Question AnsweringPreprint

Ghaeini, R., Fern, X. Z., Shahbazi, H., & Tadepalli, P. V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10528 · Cited by 7

EA Reader: Enhance Attentive Reader for Cloze-Style Question Answering via Multi-Space Context Fusion

Fu, C., & Zhang, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016375 · Cited by 5

ATNet: Answering Cloze-Style Questions via Intra-attention and Inter-attention

Fu, C., Li, Y., & Zhang, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16145-3_19 · Cited by 7

Read and Comprehend by Gated-Attention Reader with More Belief

Deng, H., & Tam, Y.-C. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/n18-4012 · Cited by 2

Text Understanding with the Attention Sum Reader Network

Kadlec, R., Schmid, M., Bajgar, O., & Kleindienst, J. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1086 · Cited by 299