Deep multi-task learning with low level tasks supervised at lower layers

Long Short-Term Memory

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Finding structure in time

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Bidirectional recurrent neural networks

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Natural Language Processing (almost) from ScratchPreprint

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A Primer on Neural Network Models for Natural Language Processing

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Introduction to the CoNLL-2000 shared task

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The CoNLL 2007 Shared Task on Dependency Parsing

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Multi-task Sequence to Sequence LearningPreprint

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Opinion Mining with Deep Recurrent Neural Networks

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Rotating your face using multi-task deep neural network

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Improving multiview face detection with multi-task deep convolutional neural networks

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Voting Between Multiple Data Representations for Text Chunking

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CCG Supertagging with a Recurrent Neural Network

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Natural Language Understanding with Distributed RepresentationPreprint

Cho, K. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.07916 · Cited by 40 · see citing works

Human Rademacher Complexity

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Deep Contextualized Word Representations

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An Overview of Multi-Task Learning in Deep Neural NetworksPreprint

Ruder, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.05098 · Cited by 3,195 · see citing works

A Primer on Neural Network Models for Natural Language Processing

Goldberg, Y. (2016) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4992 · Cited by 1,259 · see citing works

Contextual String Embeddings for Sequence Labeling

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A survey of unsupervised deep domain adaptation

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GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask NetworksPreprint

Chen, Z., Badrinarayanan, V., Lee, C., & Rabinovich, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.02257 · Cited by 838 · see citing works

Semi-supervised sequence tagging with bidirectional language models

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

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A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

Hashimoto, K., xiong, C., Tsuruoka, Y., & Socher, R. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1206 · Cited by 535 · see citing works

Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging

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Multi-Task Learning with Deep Neural Networks: A Survey

Crawshaw, M. (2020) · arXiv · doi:10.48550/arxiv.2009.09796 · Cited by 428 · see citing works

Linguistically-Informed Self-Attention for Semantic Role Labeling

Strubell, E., Verga, P. W., Andor, D., Weiss, D., & McCallum, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1548 · Cited by 414 · see citing works

Dynamic Task Prioritization for Multitask Learning

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Semi-Supervised Sequence Modeling with Cross-View Training

Clark, K., Luong, M.-T., Manning, C. D., & Le, Q. V. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1217 · Cited by 366 · see citing works

DyNet: The Dynamic Neural Network ToolkitPreprint

Neubig, G., Dyer, C., Goldberg, Y., Matthews, A., Ammar, W., Anastasopoulos, A., Ballesteros, M., Chiang, D., Clothiaux, D., Cohn, T., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.03980 · Cited by 351 · see citing works

Empower Sequence Labeling with Task-Aware Neural Language Model

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Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling TasksPreprint

Reimers, N., & Gurevych, I. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.06799 · Cited by 321 · see citing works

UniT: Multimodal Multitask Learning with a Unified Transformer

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Cross-type biomedical named entity recognition with deep multi-task learning

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Transfer Learning

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Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential Models

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A neural network multi-task learning approach to biomedical named entity recognition

Crichton, G., Pyysalo, S., Chiu, B., & Korhonen, A. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1776-8 · Cited by 229 · see citing works

Identifying beneficial task relations for multi-task learning in deep neural networks

Bingel, J., & Søgaard, A. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2026 · Cited by 220 · see citing works

Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data

Zhao, W., Wang, L., Shen, K., Jia, R., & Liu, J. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1014 · Cited by 220 · see citing works

BAM! Born-Again Multi-Task Networks for Natural Language Understanding

Clark, K., Luong, M.-T., Khandelwal, U., Manning, C. D., & Le, Q. V. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1595 · Cited by 219 · see citing works

A Hierarchical Multi-Task Approach for Learning Embeddings from Semantic Tasks

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Transitive Transfer Learning

Tan, B., Song, Y., Zhong, E., & Yang, Q. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783295 · Cited by 198 · see citing works

Neural Sequence Learning Models for Word Sense Disambiguation

Raganato, A., Bovi, C. D., & Roberto Navigli, A. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1120 · Cited by 196 · see citing works

Neural End-to-End Learning for Computational Argumentation Mining

Eger, S., Daxenberger, J., & Gurevych, I. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1002 · Cited by 183 · see citing works

Multitask Learning for Mental Health Conditions with Limited Social Media Data

Benton, A., Mitchell, M., & Hovy, D. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1015 · Cited by 175 · see citing works

Strong Baselines for Neural Semi-Supervised Learning under Domain Shift

Ruder, S., & Plank, B. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1096 · Cited by 168 · see citing works

Multi-Task Learning in Natural Language Processing: An Overview

Chen, S., Zhang, Y., & Yang, Q. (2024) · ACM Computing Surveys · doi:10.1145/3663363 · Cited by 167 · see citing works

A Multi-task Approach for Named Entity Recognition in Social Media Data

Aguilar, G., Maharjan, S., López-Monroy, A. P., & Solorio, T. (2017) · Workshop on Noisy User-generated Text (W-NUT) · doi:10.18653/v1/w17-4419 · Cited by 164 · see citing works

Robust Multilingual Part-of-Speech Tagging via Adversarial Training

Yasunaga, M., Kasai, J., & Radev, D. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1089 · Cited by 161 · see citing works

Learning Sparse Sharing Architectures for Multiple Tasks

Sun, T., Shao, Y., Li, X., Liu, P., Yan, H. R., Qiu, X., & Huang, X. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6424 · Cited by 149 · see citing works

Training Complex Models with Multi-Task Weak Supervision

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Deep Multitask Learning for Semantic Dependency Parsing

Peng, H., Thomson, S., & Smith, N. A. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1186 · Cited by 145 · see citing works

An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models

Tu, L., Lalwani, G., Gella, S., & He, H. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00335 · Cited by 125 · see citing works

All-in-One: Emotion, Sentiment and Intensity Prediction Using a Multi-Task Ensemble Framework

Akhtar, M. S., Ghosal, D., Ekbal, A., Bhattacharyya, P., & Kurohashi, S. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2926724 · Cited by 122 · see citing works

Enhanced Multi-Task Learning and Knowledge Graph-Based Recommender System

Gao, M., Li, J.-Y., Chen, C., Li, Y., Zhang, J., & Zhan, Z. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3251897 · Cited by 122 · see citing works

Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition

Toshniwal, S., Tang, H., Lu, L., & Livescu, K. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1118 · Cited by 118 · see citing works

An overview of biomimetic robots with animal behaviors

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A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization

Zhao, S., Liu, T., Zhao, S., & Wang, F. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301817 · Cited by 115 · see citing works

Multi-Task Learning for Mental Health using Social Media TextPreprint

Benton, A., Mitchell, M., & Hovy, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.03538 · Cited by 114 · see citing works

Sluice networks: Learning what to share between loosely related tasks.Preprint

Ruder, S., Bingel, J., Augenstein, I., & Søgaard, A. (2017) · arXiv (Cornell University) · Cited by 112 · see citing works

A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling

Lin, Y., Yang, S., Stoyanov, V., & Ji, H. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1074 · Cited by 109 · see citing works

Multi-task Domain Adaptation for Sequence Tagging

Peng, N., & Dredze, M. H. (2017) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w17-2612 · Cited by 109 · see citing works

HTML: Hierarchical Transformer-based Multi-task Learning for Volatility Prediction

Yang, L., Ng, T. L. J., Smyth, B., & Dong, R. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380128 · Cited by 109 · see citing works

An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models

Chronopoulou, A., Baziotis, C., & Potamianos, A. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1213 · Cited by 105 · see citing works

Hierarchically-Refined Label Attention Network for Sequence Labeling

Cui, L., & Zhang, Y. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1422 · Cited by 105 · see citing works

Global Inference for Aspect and Opinion Terms Co-Extraction Based on Multi-Task Neural Networks

Yu, J., Jiang, J., & Xia, R. (2018) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2018.2875170 · Cited by 104 · see citing works

GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling

Liu, Y., Meng, F., Zhang, J., Xu, J., Chen, Y., & Zhou, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1233 · Cited by 100 · see citing works

When is multitask learning effective? Semantic sequence prediction under varying data conditionsPreprint

Alonso, H. M., & Plank, B. (2017) · University of Groningen research database (University of Groningen / Centre for Information Technology) · Cited by 99 · see citing works

Sequence Classification with Human Attention

Barrett, M., Bingel, J., Hollenstein, N., Rei, M., & Søgaard, A. (2018) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k18-1030 · Cited by 98 · see citing works

Syntactic Scaffolds for Semantic Structures

Swayamdipta, S., Thomson, S., Lee, K., Zettlemoyer, L., Dyer, C., & Smith, N. A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1412 · Cited by 94 · see citing works

Transfer Learning in Natural Language Processing

Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.020 · Cited by 94 · see citing works

What is the Essence of a Claim? Cross-Domain Claim Identification

Daxenberger, J., Eger, S., Habernal, I., Stab, C., & Gurevych, I. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1218 · Cited by 89 · see citing works

When is multitask learning effective? Semantic sequence prediction under varying data conditions

Alonso, H. M., & Plank, B. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1005 · Cited by 87 · see citing works

Multi-Task Learning of Keyphrase Boundary Classification

Augenstein, I., & Søgaard, A. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2054 · Cited by 84 · see citing works

A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods

Zhang, Z., Yu, W., Yu, M., Guo, Z., & Jiang, M. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.66 · Cited by 84 · see citing works

A Survey on Green Deep LearningPreprint

Xu, J., Zhou, W., Fu, Z., Zhou, H., & Li, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05193 · Cited by 84 · see citing works

Empirical Analysis of Multi-Task Learning for Reducing Identity Bias in Toxic Comment Detection

Vaidya, A., Mai, F., & Ning, Y. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7334 · Cited by 83 · see citing works

Robust Lexical Features for Improved Neural Network Named-Entity RecognitionPreprint

Ghaddar, A., & Langlais, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.03489 · Cited by 81 · see citing works

BridgeTower: Building Bridges between Encoders in Vision-Language Representation Learning

Xiao, X., Wu, C., Rosenman, S., Lal, V., Che, W., & Duan, N. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26263 · Cited by 75 · see citing works

Seq2Edits: Sequence Transduction Using Span-level Edit Operations

Stahlberg, F., & Kumar, S. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.418 · Cited by 74 · see citing works

Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP

van der Goot, R., Üstün, A., Ramponi, A., Sharaf, I., & Plank, B. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-demos.22 · Cited by 73 · see citing works

Multi-Task Learning for Argumentation Mining in Low-Resource Settings

Schulz, C., Eger, S., Daxenberger, J., Kahse, T., & Gurevych, I. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2006 · Cited by 72 · see citing works

Multitask Parsing Across Semantic Representations

Hershcovich, D., Abend, O., & Rappoport, A. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1035 · Cited by 71 · see citing works

Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling

Zhang, Q., Liao, X., Liu, Q., Xu, J., & Zheng, B. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3498479 · Cited by 70 · see citing works

Learning Joint Semantic Parsers from Disjoint Data

Peng, H., Thomson, S., Swayamdipta, S., & Smith, N. A. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1135 · Cited by 69 · see citing works

Multi-modal transportation recommendation with unified route representation learning

Liu, H., Han, J., Fu, Y., Zhou, J., Lu, X., & Xiong, H. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3430915.3430924 · Cited by 65 · see citing works

Multi-Task Learning for Sequence Tagging: An Empirical StudyPreprint

Changpinyo, S., Hu, H., & Sha, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.04151 · Cited by 65 · see citing works

Neural Paraphrase Identification of Questions with Noisy Pretraining

Tomar, G. S., Duque, T. S. P. C., Täckström, O., Uszkoreit, J., & Das, D. (2017) · Workshop on Subword and Character Level Models in NLP (SCLeM) · doi:10.18653/v1/w17-4121 · Cited by 64 · see citing works

Learning what to share between loosely related tasksPreprint

Ruder, S., Bingel, J., Augenstein, I., & Søgaard, A. (2017) · Research at the University of Copenhagen (University of Copenhagen) · Cited by 64 · see citing works

Multiple Relational Attention Network for Multi-task Learning

Zhao, J., Du, B., Sun, L., Zhuang, F., Lv, W., & Xiong, H. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330861 · Cited by 62 · see citing works

Multi-task Recurrent Neural Networks and Higher-order Markov Random Fields for Stock Price Movement Prediction

Li, C., Song, D., & Tao, D. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330983 · Cited by 58 · see citing works

Multi-task Character-Level Attentional Networks for Medical Concept Normalization

Niu, J., Yang, Y., Zhang, S., Sun, Z., & Zhang, W. (2018) · Neural Processing Letters · doi:10.1007/s11063-018-9873-x · Cited by 52 · see citing works

Transformer-Based Argument Mining for Healthcare Applications

Mayer, T., Cabrio, E., & Villata, S. (2020) · Frontiers in artificial intelligence and applications · doi:10.3233/faia200334 · Cited by 52 · see citing works

Hierarchical Multitask Learning With CTC

Sanabria, R., & Metze, F. (2018) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639530 · Cited by 50 · see citing works

A multi-task deep learning model for the classification of Age-related Macular Degeneration

Chen, Q., Peng, Y., Keenan, T. D. L., Dharssi, S., Agrón, E., Wong, W. T., Chew, E. Y., & Lu, Z. (2018) · PubMed Central · doi:10.48550/arxiv.1812.00422 · Cited by 48 · see citing works

Proceedings of the First Workshop on Subword and Character Level Models in NLP

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Hierarchical Multi-task Learning for Organization Evaluation of Argumentative Student Essays

Song, W., Song, Z., Liu, L., & Fu, R. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/536 · Cited by 47 · see citing works

Multi-Task Learning for Abstractive and Extractive Summarization

Chen, Y., Ma, Y., Mao, X., & Li, Q. (2019) · Data Science and Engineering · doi:10.1007/s41019-019-0087-7 · Cited by 45 · see citing works

End-to-end aspect-based sentiment analysis with hierarchical multi-task learning

Wang, X., Xu, G., Zhang, Z., Jin, L., & Sun, X. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.100 · Cited by 45 · see citing works

A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural NetworksPreprint

Mao, H. H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00800 · Cited by 45 · see citing works

Multi-task Learning in Deep Neural Networks at EVALITA 2018

Cimino, A., Mattei, L. D., & Dell’Orletta⋄, F. (2018) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.4527 · Cited by 44 · see citing works

Improving sentiment analysis with multi-task learning of negation

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Snorkel MeTaL: Weak Supervision for Multi-Task Learning

Ratner, A., Hancock, B., Dunnmon, J., Goldman, R., & Ré, C. (2018) · Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning · doi:10.1145/3209889.3209898 · Cited by 43 · see citing works

Rethinking modeling Alzheimer's disease progression from a multi-task learning perspective with deep recurrent neural network

Liang, W., Zhang, K., Cao, P., Liu, X., Yang, J., & Zai͏̈ane, O. R. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104935 · Cited by 42 · see citing works

MuMu: Cooperative Multitask Learning-Based Guided Multimodal Fusion

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Semi-Supervised Sequence Modeling with Cross-View TrainingPreprint

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Generating Product Descriptions from User Reviews

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A contextual multi-task neural approach to medication and adverse events identification from clinical text

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Hierarchical Multitask Learning for CTC-based Speech RecognitionPreprint

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A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity PredictionPreprint

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Enhancing evidence-based medicine with natural language argumentative analysis of clinical trials.

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Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem

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Argumentative Link Prediction using Residual Networks and Multi-Objective Learning

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Multitask Learning for Arabic Offensive Language and Hate-Speech Detection

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A unified architecture for natural language processing: Deep neural networks with multitask learning

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Exploring the Syntactic Abilities of RNNs with Multi-task Learning

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A Hierarchical Multi-Task Approach for Learning Embeddings from Semantic Tasks

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A unified architecture for natural language processing: deep neural networks with multitask learning

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Deconstructing Supertagging into Multi-Task Sequence Prediction

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