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

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works

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 · see citing works

Efficient Estimation of Word Representations in Vector Space

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works

Semi-supervised classification with graph convolutional networks

Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works

Distributed Representations of Words and Phrases and their Compositionality

Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works

Language Models are Few-Shot Learners

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · see citing works

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 · see citing works

Convolutional Neural Networks for Sentence Classification

Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831 · see citing works

Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics

Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works

Indexing by latent semantic analysis

Deerwester, S. C., Dumais, S., Furnas, G. W., Landauer, T. K., & Harshman, R. A. (1990) · Journal of the American Society for Information Science · doi:10.1002/(sici)1097-4571(199009)41:6 · Cited by 12,758 · see citing works

Graph Attention Networks

Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1710.10903 · Cited by 12,107

Graph Attention NetworksPreprint

Veličković, P., Casanova, A., Liò, P., Cucurull, G., Romero, A., & Bengio, Y. (2017) · arXiv (Cornell University) · doi:10.17863/cam.48429 · Cited by 12,099 · see citing works

Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · see citing works

A Comprehensive Survey on Graph Neural Networks

Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000 · see citing works

XLNet: Generalized Autoregressive Pretraining for Language Understanding

Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687 · see citing works

SQuAD: 100,000+ Questions for Machine Comprehension of Text

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208 · see citing works

Hierarchical Attention Networks for Document Classification

Yang, Z., Yang, D., Dyer, C., He, X., Smola, A. J., & Hovy, E. H. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1174 · Cited by 4,874 · see citing works

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01108 · Cited by 4,653 · see citing works

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019) · arXiv · doi:10.48550/arxiv.1909.11942 · Cited by 4,308 · see citing works

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5446 · Cited by 4,222 · see citing works

Dynamic Routing Between Capsules

Sabour, S., Frosst, N., & Hinton, G. E. (2017) · arXiv · doi:10.48550/arxiv.1710.09829 · Cited by 4,167 · see citing works

Universal Language Model Fine-tuning for Text Classification

Howard, J., & Ruder, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1031 · Cited by 4,100 · see citing works

TextRank: Bringing Order into Text

Mihalcea, R. F., & Tarau, P. (2004) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · Cited by 3,631 · see citing works

A Convolutional Neural Network for Modelling Sentences

Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1062 · Cited by 3,601 · see citing works

A Neural Probabilistic Language Model

Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C., Jaz, K., Hofmann, T., Poggio, T., & Shawe-Taylor, J. S. (2000) · Cited by 3,451 · see citing works

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

Tai, K. S., Socher, R., & Manning, C. D. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1150 · Cited by 3,081 · see citing works

Graph Convolutional Networks for Text Classification

Yao, L., Mao, C., & Luo, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017370 · Cited by 2,171 · see citing works

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., & Levy, O. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00300 · Cited by 1,827 · see citing works

Pre-trained models for natural language processing: A survey

Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., & Huang, X. (2020) · Science China Technological Sciences · doi:10.1007/s11431-020-1647-3 · Cited by 1,591 · see citing works

Document Modeling with Gated Recurrent Neural Network for Sentiment Classification

Tang, D., Qin, B., & Liu, T. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1167 · Cited by 1,538 · see citing works

Training Very Deep NetworksPreprint

Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.06228 · Cited by 1,453 · see citing works

Transforming Auto-Encoders

Hinton, G. E., Krizhevsky, A., & Wang, S. D. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-21735-7_6 · Cited by 1,360 · see citing works

Unified Language Model Pre-training for Natural Language Understanding and Generation

Dong, L., Yang, N., Wang, W., Wei, F., Liu, X., Wang, Y., Gao, J., Zhou, M., & Hon, H.-W. (2019) · arXiv · doi:10.48550/arxiv.1905.03197 · Cited by 1,225 · see citing works

Recurrent Neural Network for Text Classification with Multi-Task LearningPreprint

Liu, P., Qiu, X., & Huang, X. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.05101 · Cited by 1,209 · see citing works

An overview of multi-task learning

Zhang, Y., & Yang, Q. (2017) · National Science Review · doi:10.1093/nsr/nwx105 · Cited by 1,120 · see citing works

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2019) · arXiv · doi:10.48550/arxiv.1905.00537 · Cited by 1,051 · see citing works

Very Deep Convolutional Networks for Text Classification

Conneau, A., Schwenk, H., Barrault, L., & Lecun, Y. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers · doi:10.18653/v1/e17-1104 · Cited by 1,046 · see citing works

ERNIE: Enhanced Representation through Knowledge IntegrationPreprint

Sun, Y., Wang, S., Li, Y., Feng, S., Chen, X., Zhang, H., Tian, X., Zhu, D., Tian, H., & Wu, H. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.09223 · Cited by 983 · see citing works

Baselines and Bigrams: Simple, Good Sentiment and Topic Classification

Wang, S., & Manning, C. D. (2012) · Cited by 957 · see citing works

FastText.zip: Compressing text classification models

Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., & Mikolov, T. (2016) · arXiv · doi:10.48550/arxiv.1612.03651 · Cited by 887 · see citing works

DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding

Shen, T., Zhou, T., Long, G., Jiang, J., Pan, S., & Zhang, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11941 · Cited by 779 · see citing works

Deep Learning for Extreme Multi-label Text Classification

Liu, J., Chang, W.-C., Wu, Y., & Yang, Y. (2017) · Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3077136.3080834 · Cited by 676 · see citing works

A C-LSTM Neural Network for Text ClassificationPreprint

Zhou, C., Sun, C., Liu, Z., & Lau, F. C. M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.08630 · Cited by 666 · see citing works

Learning Without Forgetting

Li, Z., & Hoiem, D. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_37 · Cited by 659 · see citing works

Adversarial NLI: A New Benchmark for Natural Language Understanding

Nie, Y., Williams, A., Dinan, E., Bansal, M., Weston, J., & Kiela, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.441 · Cited by 656 · see citing works

Multitask Learning

Caruana, R. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_5 · Cited by 577 · see citing works

The Natural Language Decathlon: Multitask Learning as Question Answering

McCann, B., Keskar, N. S., Xiong, C., & Socher, R. (2018) · arXiv · doi:10.48550/arxiv.1806.08730 · Cited by 448 · see citing works

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Lepikhin, D., Lee, H., Xu, Y., Chen, D., Firat, O., Huang, Y., Krikun, M., Shazeer, N., & Chen, Z. (2020) · arXiv · doi:10.48550/arxiv.2006.16668 · Cited by 412 · see citing works

SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment

Marelli, M., Bentivogli, L., Baroni, M., Raffaella Bernardi, B., Menini, S., & Zamparelli, R. (2014) · International Workshop on Semantic Evaluation (SemEval) · doi:10.3115/v1/s14-2001 · Cited by 378 · see citing works

Gated-Attention Readers for Text Comprehension

Dhingra, B., Liu, H., Yang, Z., Cohen, W., & Salakhutdinov, R. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1168 · Cited by 366 · see citing works

Attention-based LSTM Network for Cross-Lingual Sentiment Classification

Zhou, X., Wan, X., & Xiao, J. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1024 · Cited by 302 · see citing works

The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence

Marcus, G. (2020) · arXiv · doi:10.48550/arxiv.2002.06177 · Cited by 297 · see citing works

Long Short-Term Memory Over Recursive Structures

Zhu, X., Sobihani, P., & Guo, H. (2015) · Cited by 292 · see citing works

Learning Natural Language Inference using Bidirectional LSTM model and Inner-AttentionPreprint

Liu, Y., Sun, C., Lin, L., & Wang, X.-L. (2016) · arXiv.org · doi:10.48550/arxiv.1605.09090 · Cited by 287 · see citing works

Ensemble application of convolutional and recurrent neural networks for multi-label text categorization

Chen, G.-B., Ye, D., Xing, Z., Chen, J., & Cambria, E. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966144 · Cited by 261 · see citing works

Improving text classification with weighted word embeddings via a multi-channel TextCNN model

Guo, B., Zhang, C., Liu, J., & Ma, X. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.07.052 · Cited by 250 · see citing works

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 200 · see citing works

Evaluating Natural Language Understanding Services for Conversational Question Answering Systems

Braun, D., Hernandez-Mendez, A., Matthes, F., & Langen, M. (2017) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w17-5522 · Cited by 199 · see citing works

Convolutional Recurrent Neural Networks for Text Classification

Wang, R., Li, Z., Cao, J., Chen, T., & Wang, L. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852406 · Cited by 198 · see citing works

Text classification using capsules

Kim, J., Jang, S., Park, E., & Choi, S. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.033 · Cited by 191 · see citing works

Multi-Timescale Long Short-Term Memory Neural Network for Modelling Sentences and Documents

Liu, P., Qiu, X., Chen, X., Wu, S., & Huang, X. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1280 · Cited by 182 · see citing works

Efficient Character-level Document Classification by Combining Convolution and Recurrent LayersPreprint

Xiao, Y., & Cho, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.00367 · Cited by 166 · see citing works

The NLP Cookbook: Modern Recipes for Transformer Based Deep Learning Architectures

Singh, S. K., & Mahmood, A. (2021) · IEEE Access · doi:10.1109/access.2021.3077350 · Cited by 164 · see citing works

A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence ClassificationPreprint

Zhang, Y., & Wallace, B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.03820 · Cited by 153 · see citing works

Out-of-Domain Detection for Natural Language Understanding in Dialog Systems

Zheng, Y., Chen, G., & Huang, M. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.2983593 · Cited by 141 · see citing works

Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents

Zhang, R., Lee, H., & Radev, D. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1177 · Cited by 131 · see citing works

Dynamic Entity Representations in Neural Language Models

Ji, Y., Tan, C., Martschat, S., Choi, Y., & Smith, N. A. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1195 · Cited by 95 · see citing works

An Intelligent Chatbot System Based on Entity Extraction Using RASA NLU and Neural Network

Jiao, A. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1487/1/012014 · Cited by 86 · see citing works

Compositional coding capsule network with k-means routing for text classification

Ren, H., & Lu, H. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.05.028 · Cited by 69 · see citing works

Natural Language Understanding (NLU, not NLP) in Cognitive Systems

McShane, M. J. (2017) · AI Magazine · doi:10.1609/aimag.v38i4.2745 · Cited by 68 · see citing works

Dynamic Integration of Background Knowledge in Neural NLU SystemsPreprint

Weissenborn, D., Kočiský, T., & Dyer, C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02596 · Cited by 65 · see citing works

Semantic vector learning for natural language understanding

Jung, S. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2018.12.008 · Cited by 54 · see citing works

Hierarchical Regularization Cascade for Joint Learning

Zweig, A., & Weinshall, D. (2013) · Cited by 48 · see citing works

Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling

Liu, L., Ren, X., Shang, J., Gu, X., Peng, J., & Han, J. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1153 · Cited by 45 · see citing works

Dual Learning for Semi-Supervised Natural Language Understanding

Zhu, S., Cao, R., & Yu, K. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3001684 · Cited by 38 · see citing works

Squeezed Very Deep Convolutional Neural Networks for Text Classification

Duque, A. B., Santos, L. L. J., Macêdo, D., & Zanchettin, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30487-4_16 · Cited by 34 · see citing works

Designing a Better Data Representation for Deep Neural Networks and Text Classification

Prusa, J. D., & Khoshgoftaar, T. M. (2016) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2016.61 · Cited by 29 · see citing works

A Dialogue-Act Taxonomy for a Virtual Coach Designed to Improve the Life of Elderly

Montenegro, C., Zorrilla, A. L., Olaso, J. M., Santana, R., Justo, R., Lozano, J. A., & Torres, M. I. (2019) · Multimodal Technologies and Interaction · doi:10.3390/mti3030052 · Cited by 27 · see citing works

Human Annotated Dialogues Dataset for Natural Conversational Agents

Merdivan, E., Singh, D., Hanke, S., Kropf, J., Holzinger, A., & Geist, M. (2020) · Applied Sciences · doi:10.3390/app10030762 · Cited by 24 · see citing works

Auxiliary Task Reweighting for Minimum-data LearningPreprint

Shi, B., Hoffman, J., Saenko, K., Darrell, T., & Xu, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.08244 · Cited by 21 · see citing works

A dynamic goal adapted task oriented dialogue agent

Tiwari, A., Saha, T., Saha, S., Sengupta, S., Maitra, A., Ramnani, R., & Bhattacharyya, P. (2021) · PLoS ONE · doi:10.1371/journal.pone.0249030 · Cited by 16 · see citing works

A Conversational User Interface for Stock Analysis

Lauren, P., & Watta, P. B. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005635 · Cited by 14 · see citing works

Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents

Yao, S., Narasimhan, K., & Hausknecht, M. (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.247 · Cited by 14 · see citing works

All Roads Lead to UD: Converting Stanford and Penn Parses to English Universal Dependencies with Multilayer AnnotationsPreprint

Peng, S., & Zeldes, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.00522 · Cited by 14 · see citing works

Submodularity-Inspired Data Selection for Goal-Oriented Chatbot Training Based on Sentence Embeddings

Dimovski, M., Musat, C., Ilievski, V., Hossman, A., & Baeriswyl, M. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/559 · Cited by 11 · see citing works

Evaluating different Natural Language Understanding services in a real business case for the Italian language

Zubani, M., Sigalini, L., Serina, I., & Gerevini, A. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.09.095 · Cited by 11 · see citing works

Transformers aftermath

dos Reis, E. S., da Costa, C. A., da Silveira, D. E., Bavaresco, R. S., Righi, R. D. R., Barbosa, J. L. V., Antunes, R. S., Gomes, M. M., & Federizzi, G. (2021) · Communications of the ACM · doi:10.1145/3430937 · Cited by 11 · see citing works

Faceted Text Segmentation via Multitask Learning

Wu, B., Wei, B., Liu, J., Wu, K., & Wang, M. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015996 · Cited by 8 · see citing works

Natural Language Understanding: Methodological Conceptualization

Shymko, V. (2019) · PSYCHOLINGUISTICS · doi:10.31470/2309-1797-2019-25-1-431-443 · Cited by 6 · see citing works

A Sequential and Intensive Weighted Language Modeling Scheme for Multi-Task Learning-Based Natural Language Understanding

Son, S., Hwang, S., Bae, S., Park, S. J., & Choi, J. (2021) · Applied Sciences · doi:10.3390/app11073095 · Cited by 5 · see citing works

Enhancing Domain-Specific Supervised Natural Language Intent Classification with a Top-Down Selective Ensemble Model

Jenset, G. B., & McGillivray, B. C. (2019) · Machine Learning and Knowledge Extraction · doi:10.3390/make1020037 · Cited by 4 · see citing works

Automatic Labeled Dialogue Generation for Nursing Record Systems

Mairittha, T., Mairittha, N., & Inoue, S. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10030062 · Cited by 4 · see citing works

Predicting Garden Path Sentences Based on Natural Language Understanding System

Du, J., & Yu, P. (2012) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2012.031101 · Cited by 3 · see citing works

Reading and Acting while Blindfolded: The Need for Semantics in Text Game AgentsPreprint

Yao, S., Narasimhan, K., & Hausknecht, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.13552 · Cited by 3 · see citing works

Enriching Language Models with Semantics

Mayer, T. (2020) · Frontiers in artificial intelligence and applications · doi:10.3233/faia200452 · Cited by 3 · see citing works

VirAAL: Virtual Adversarial Active Learning for NLU

Senay, G., Idrissi, B. Y., & Haziza, M. (2021) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt48900.2021.9383576 · Cited by 1 · see citing works

An AI Using Construction Grammar to Understand Text

Kiselev, D. (2020) · International Journal of Cognitive Informatics and Natural Intelligence · doi:10.4018/ijcini.20210401.oa4 · Cited by 1 · see citing works

Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories

Chen, T., Wang, Z., Li, L., Li, D., Li, Z., Chang, X., Bian, P., Liang, G., Wang, Q., & Xie, T. (2025) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.ecoop.2025.6

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805

Gradient Surgery for Multi-Task Learning

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Agentic AI: Autonomous intelligence for complex goals–a comprehensive survey

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A Multitask Cross-Lingual Summary Method Based on ABO Mechanism

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Machine Translation Techniques using AI : A Review

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Sentiment Analysis on Service Quality of an Online Healthcare Mobile Platform Using VADER and Roberta Pretrained Model

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Deep Learning Based Monocular Depth Estimation for Object Distance Inference in 2D Images

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Unveiling the Potential of Machine Learning in Activity Recognition for Industry 4.0

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การพฒนาการประเมนความหลวมของสลกเกลยวจากภาพโครงสรางเหลกสำเรจรปโดยใชโครงขายประสาทเชงลก

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The Future of Medicine: Large Language Models Redefining Healthcare DynamicsPreprint

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Revolutionizing Healthcare through Generative AI: Advancements in Medical Imaging, Drug Discovery, and Data Augmentation

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Improving Semantic Analysis in Visualization with Meta Network Representation and Parsing Algorithm

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