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

Luong, M.-T., Le, Q. V., Sutskever, I., Vinyals, O., & Kaiser, Ł. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06114 · Cited by 606

Opinion Mining with Deep Recurrent Neural Networks

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Rotating your face using multi-task deep neural network

Yim, J., Jung, H., Yoo, B., Choi, C., Park, D., & Kim, J. (2015) · 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.2015.7298667 · Cited by 279

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

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Keyphrase Extraction from Disaster-related Tweets

Chowdhury, J. R., Caragea, C., & Caragea, D. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313696 · Cited by 37

SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling

Marasović, A., & Frank, A. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1054 · Cited by 36

Detecting Cyber Threat Event from Twitter Using IDCNN and BiLSTM

Fang, Y., Gao, J., Liu, Z., & Huang, C. (2020) · Applied Sciences · doi:10.3390/app10175922 · Cited by 34

Keystroke dynamics as signal for shallow syntactic parsingPreprint

Plank, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.03321 · Cited by 34

Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents

Goyal, A. K., Metallinou, A., & Matsoukas, S. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-3018 · Cited by 35

An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language ModelsPreprint

Chronopoulou, A., Baziotis, C., & Potamianos, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.10547 · Cited by 49

Backpropagating through Structured Argmax using a SPIGOT

Peng, H. M., Thomson, S., & Smith, N. A. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1173 · Cited by 39

Deep Cascade Multi-Task Learning for Slot Filling in Online Shopping Assistant

Gong, Y., Luo, X., Zhu, Y. M., Ou, W., Li, Z., Zhu, M., Zhu, K. Q., Duan, L., & Chen, X. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016465 · Cited by 32

Explainable Natural Language Processing

Søgaard, A. (2021) · Synthesis lectures on human language technologies · doi:10.2200/s01118ed1v01y202107hlt051 · Cited by 32

Unified route representation learning for multi-modal transportation recommendation with spatiotemporal pre-training

Liu, H., Han, J., Fu, Y., Li, Y., Chen, K., & Xiong, H. (2022) · The VLDB Journal · doi:10.1007/s00778-022-00748-y · Cited by 33

A Multi-task Approach to Predict Likability of Books

Maharjan, S., Arévalo, J., Montes, M., Gonzalez, F. A., & Solorio, T. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1114 · Cited by 33

Overton: A Data System for Monitoring and Improving Machine-Learned ProductsPreprint

Ré, C., Niu, F., Gudipati, P., & Srisuwananukorn, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.05372 · Cited by 36

Simplified Neural Unsupervised Domain Adaptation

Miller, T. A. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1039 · Cited by 32

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

Empirical evaluation of multi-task learning in deep neural networks for natural language processing

Li, J., Liu, X., Yin, W., Yang, M., Ma, L., & Jin, Y. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05268-w · Cited by 33

Stacked Residual Recurrent Neural Networks With Cross-Layer Attention for Text Classification

Lan, Y., Hao, Y., Xia, K., Qian, B., & Li, C. (2020) · IEEE Access · doi:10.1109/access.2020.2987101 · Cited by 31

Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments

Abdul-Mageed, M., Zhang, C., Elmadany, A., & Ungar, L. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.472 · Cited by 30

Multi-Task Attentive Residual Networks for Argument Mining

Galassi, A., Lippi, M., & Torroni, P. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3275040 · Cited by 30

DeepHCS + + : Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening

Lee, G., Oh, J.-W., Her, N.-G., & Jeong, W. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101995 · Cited by 31

Personal Knowledge Base Construction from Text-based Lifelogs

Yen, A.-Z., Huang, H., & Chen, H. (2019) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3331184.3331209 · Cited by 32

End-to-End Argument Mining as Biaffine Dependency Parsing

Ye, Y., & Teufel, S. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.55 · Cited by 31

Symbolic Inductive Bias for Visually Grounded Learning of Spoken Language

Chrupała, G. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1647 · Cited by 28

Multitask TSK Fuzzy System Modeling by Jointly Reducing Rules and Consequent Parameters

Wang, J., Lin, D., Deng, Z., Jiang, Y., Zhu, J., Chen, L., Li, Z., Gong, L., & Wang, S. (2019) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2019.2930616 · Cited by 28

End-to-end Argument Mining with Cross-corpora Multi-task Learning

Morio, G., Ozaki, H., Morishita, T., & Yanai, K. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00481 · Cited by 28

Low-resource text classification using domain-adversarial learning

Grießhaber, D., Vu, N. T., & Maucher, J. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2019.101056 · Cited by 29

Will my auxiliary tagging task help? Estimating Auxiliary Tasks Effectivity in Multi-Task Learning

Bjerva, J. (2017) · University of Groningen research database (University of Groningen / Centre for Information Technology) · Cited by 27

End-to-End Argument Mining for Discussion Threads Based on Parallel Constrained Pointer Architecture

Morio, G., & Fujita, K. (2018) · Workshop on Argument Mining (ArgMining) · doi:10.18653/v1/w18-5202 · Cited by 30

Knowledge-Graph Augmented Word Representations for Named Entity Recognition

He, Q., Wu, L., Yin, Y., & Cai, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6299 · Cited by 29

Multi-task learning with a natural metric for quantitative structure activity relationship learning

Sadawi, N., Olier, I., Vanschoren, J., van Rijn, J. N., Besnard, J., Bickerton, R., Groşan, C., Soldatova, L., & King, R. D. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0392-1 · Cited by 26

Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies

Kurita, S., & Søgaard, A. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1232 · Cited by 26

Hierarchical Multi-Task Word Embedding Learning for Synonym Prediction

Fei, H., Tan, S., & Li, P. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330914 · Cited by 25

Neural Multi-Task Learning for Stance Prediction

Fang, W., Nadeem, M., Mohtarami, M., & Glass, J. R. (2019) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/d19-6603 · Cited by 26

Emotion and sentiment analysis for intelligent customer service conversation using a multi-task ensemble framework

Chen, D., Huang, Z., Yiting, T., Jintao, M., & Khanal, R. (2023) · Cluster Computing · doi:10.1007/s10586-023-04073-z · Cited by 26

Learning from Limited Heterogeneous Training Data: Meta-Learning for Unsupervised Zero-Day Web Attack Detection across Web Domains

Li, P., Wang, Y., Li, Q. L., Liu, Z., Xu, K., Ren, J., Liu, Z., & Lin, R. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3623123 · Cited by 26

Exploring the Syntactic Abilities of RNNs with Multi-task Learning

Enguehard, É., Goldberg, Y., & Linzen, T. (2017) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k17-1003 · Cited by 26

Empower event detection with bi-directional neural language model

Zhang, Y., Xu, G., Wang, Y., Liang, X., Wang, L., & Huang, T. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.01.008 · Cited by 25

Semi-Supervised Semantic Role Labeling with Cross-View Training

Cai, R., & Lapata, M. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1094 · Cited by 26

Exploring Multi-Task Multi-Lingual Learning of Transformer Models for Hate Speech and Offensive Speech Identification in Social Media

Mishra, S., Prasad, S., & Mishra, S. (2021) · SN Computer Science · doi:10.1007/s42979-021-00455-5 · Cited by 25

Structural Guidance for Transformer Language Models

Qian, P., Naseem, T., Lévy, R., & Astudillo, R. F. (2021) · doi:10.18653/v1/2021.acl-long.289 · Cited by 26

Exploring convolutional neural networks and topic models for user profiling from drug reviews

Tutubalina, E. V., & Nikolenko, S. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5336-z · Cited by 27

What do character-level models learn about morphology? The case of dependency parsing

Vania, C., Grivas, A., & Lopez, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1278 · Cited by 24

Unlexicalized Transition-based Discontinuous Constituency Parsing

Coavoux, M., Crabbé, B., & Cohen, S. B. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00255 · Cited by 24

Joint Prediction of Morphosyntactic Categories for Fine-Grained Arabic Part-of-Speech Tagging Exploiting Tag Dictionary Information

Inoue, G., Shindo, H., & Matsumoto, Y. (2017) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k17-1042 · Cited by 23

Syntax-aware Semantic Role Labeling without Parsing

Cai, R., & Lapata, M. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00272 · Cited by 23

Multi-Task Networks with Universe, Group, and Task Feature Learning

Pentyala, S., Liu, M., & Dreyer, M. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1079 · Cited by 36

Polyglot Prompt: Multilingual Multitask Prompt Training

Fu, J., Ng, S.-K., & Liu, P. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.674 · Cited by 33

Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological TagsPreprint

Güngör, O., Üsküdarlı, S., & Güngör, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.06683 · Cited by 24

The Role of Massively Multi-Task and Weak Supervision in Software 2.0.

Ratner, A., Hancock, B. J., & Ré, C. (2019) · Conference on Innovative Data Systems Research · Cited by 22

A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy

Beryozkin, G., Drori, Y., Gilon, O., Hartman, T., & Szpektor, I. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1014 · Cited by 22

Character-level Supervision for Low-resource POS Tagging

Kann, K., Bjerva, J., Augenstein, I., Plank, B., & Søgaard, A. (2018) · Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo) · doi:10.18653/v1/w18-3401 · Cited by 22

Hierarchical multi-task learning with CRF for implicit discourse relation recognition

Wu, C., Chaowen, H., Li, R., Lin, H., & Su, J. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105637 · Cited by 22

Many Hands Make Light Work: Transferring Knowledge From Auxiliary Tasks for Video-Text Retrieval

Wang, W., Gao, J., Yang, X., & Xu, C. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3149716 · Cited by 23

Digital-twin-enhanced metal tube bending forming real-time prediction method based on multi-source-input MTL

Sun, C., Wang, Z., Zhang, S., Zhou, T., Li, J., & Tan, J. (2022) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-022-03372-0 · Cited by 22

Synergizing Unsupervised and Supervised Learning: A Hybrid Approach for Accurate Natural Language Task Modeling

Talukdar, W., & Biswas, A. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24may2087 · Cited by 26

Hate Speech Detection using Attention-based LSTM

Sarracén, G. L. D. L. P., Gil-Pons, R., Cuza, C. E. M., & Rosso, P. (2018) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.4784 · Cited by 22

Reliability-aware Dynamic Feature Composition for Name Tagging

Lin, Y., Liu, L., Ji, H., Yu, D., & Han, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1016 · Cited by 23

Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling

Zalmout, N., & Habash, N. Y. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1173 · Cited by 25

Exploring Logically Dependent Multi-task Learning with Causal Inference

Chen, W., Tian, J., Xiao, L., He, H., & Jin, Y. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.173 · Cited by 22

Multi-task Learning-Based Spoofing-Robust Automatic Speaker Verification System

Zhao, Y., Togneri, R. B., & Sreeram, V. (2022) · Circuits Systems and Signal Processing · doi:10.1007/s00034-022-01974-z · Cited by 22

Multi-Task Learning for Depression Detection in Dialogs

Li, C., Braud, C., & Amblard, M. (2022) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2022.sigdial-1.7 · Cited by 24

Emotion Detection and Classification in a Multigenre Corpus with Joint Multi-Task Deep Learning

Tafreshi, S., & Diab, M. (2018) · International Conference on Computational Linguistics · Cited by 21

BHIN2vec

Lee, S., Park, C., & Yu, H. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357893 · Cited by 21

Investigating the Role of Argumentation in the Rhetorical Analysis of Scientific Publications with Neural Multi-Task Learning Models

Lauscher, A., Glavaš, G., Ponzetto, S. P., & Eckert, K. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1370 · Cited by 26

Multi-Task Learning with Calibrated Mixture of Insightful Experts

Wang, S., Li, Y., Li, H., Zhu, T., Li, Z., & Ou, W. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00312 · Cited by 20

Segment-Level Neural Conditional Random Fields for Named Entity Recognition

Sato, M., Shindo, H., Yamada, I., & Matsumoto, Y. (2017) · International Joint Conference on Natural Language Processing · Cited by 20

Jointly Modeling Intent Identification and Slot Filling with Contextual and Hierarchical Information

Wen, L., Wang, X., Dong, Z., & Chen, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-73618-1_1 · Cited by 20

Implicit Euler ODE Networks for Single-Image Dehazing

Shen, J., Li, Z., Yu, L., Xia, G.-S., & Yang, W. T. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00117 · Cited by 20

Predicting In-Game Actions from Interviews of NBA Players

Oved, N., Feder, A., & Reichart, R. (2020) · Computational Linguistics · doi:10.1162/coli_a_00383 · Cited by 20

Multi-task Active Learning for Pre-trained Transformer-based Models

Rotman, G., & Reichart, R. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00515 · Cited by 22

Robust Neural Abstractive Summarization Systems and Evaluation against Adversarial InformationPreprint

Fan, L., Yu, D., & Wang, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.06065 · Cited by 20

Multiple Classification with Split Learning

Kim, J., Shin, S., Yu, Y., Lee, J., & Lee, K. (2020) · International Conference on Smart Media and Applications (SMA) · doi:10.1145/3426020.3426131 · Cited by 21

Learning Multi-Task Communication with Message Passing for Sequence Learning

Liu, P., Fu, J., Dong, Y., Qiu, X., & Cheung, J. C. K. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014360 · Cited by 19

Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling

Paul, D., Singh, M., Hedderich, M. A., & Klakow, D. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/n19-3005 · Cited by 20

Issue Framing in Online Discussion Fora

Hartmann, M., Jansen, T., Augenstein, I., & Søgaard, A. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1142 · Cited by 19

Multi-task and multi-view training for end-to-end relation extraction

Zhang, J., Zhang, Y., Ji, D., & Liu, M. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.06.087 · Cited by 19

Multi-Task Bidirectional Transformer Representations for Irony DetectionPreprint

Zhang, C., & Abdul-Mageed, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03526 · Cited by 19

Multi-Order Networks for Action Unit Detection

Tallec, G., Dapogny, A., & Bailly, K. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3178524 · Cited by 18

Combining Unsupervised Pre-training and Annotator Rationales to Improve Low-shot Text Classification

Melamud, O., Bornea, M. A., & Barker, K. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1401 · Cited by 19

Evaluating Neural Morphological Taggers for Sanskrit

Gupta, A., Krishna, A., Goyal, P., & Hellwig, O. (2020) · SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology (SIGMORPHON) · doi:10.18653/v1/2020.sigmorphon-1.23 · Cited by 18

Gated hierarchical multi-task learning network for judicial decision prediction

Yao, F., Sun, X., Yu, H., Yang, Y., Zhang, W., & Fu, K. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.018 · Cited by 18

A Closer Look at Loss Weighting in Multi-Task LearningPreprint

Lin, B., Ye, F., & Zhang, Y. (2021) · arXiv (Cornell University) · Cited by 18

Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus

Ikhwantri, F., Louvan, S., Kurniawan, K., Abisena, B., Rachman, V., Wicaksono, A. F., & Mahendra, R. (2018) · Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo) · doi:10.18653/v1/w18-3406 · Cited by 17

SC-LSTM: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling

Lu, P., Bai, T., & Langlais, P. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1249 · Cited by 19

Multimodal joint learning for personal knowledge base construction from Twitter-based lifelogs

Yen, A.-Z., Huang, H., & Chen, H. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.102148 · Cited by 18

A Multi-task Learning Model for Daily Activity Forecast in Smart Home

Yang, H., Gong, S., Liu, Y., Lin, Z., & Qu, Y. (2020) · Sensors · doi:10.3390/s20071933 · Cited by 17

Semantic Role Labeling as Syntactic Dependency Parsing

Shi, T., Malioutov, I., & İrsoy, O. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.610 · Cited by 17

Estimating the influence of auxiliary tasks for multi-task learning of sequence tagging tasks

Schröder, F., & Biemann, C. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.268 · Cited by 18

Leveraging label hierarchy using transfer and multi-task learning: A case study on patent classification

Aroyehun, S. T., Angel, J., Majumder, N., Gelbukh, A. F., & Hussain, A. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.057 · Cited by 16

Adversarial Learning for Multi-task Sequence Labeling with Attention Mechanism

Wang, Y., Li, Y., Zhu, Z., Tong, H., & Huang, Y. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3013114 · Cited by 16

Learning Implicit and Explicit Multi-task Interactions for Information Extraction

Sun, K., Zhang, R., Mensah, S., Mao, Y., & Liu, X. (2022) · ACM Transactions on Information Systems · doi:10.1145/3533020 · Cited by 16

Semantics as a Foreign Language

Stanovsky, G., & Dagan, I. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1263 · Cited by 15

Weakly Supervised POS Taggers Perform Poorly on Truly Low-Resource Languages

Kann, K., Lacroix, O., & Søgaard, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6317 · Cited by 16

Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding

Louvan, S., & Magnini, B. (2018) · doi:10.18653/v1/w18-5711 · Cited by 14

Multi-Task Supervised Pretraining for Neural Domain Adaptation

Meftah, S., Semmar, N., Tahiri, M.-A., Tamaazousti, Y., Essafi, H., & Sadat, F. (2020) · International Workshop on Natural Language Processing for Social Media (SocialNLP) · doi:10.18653/v1/2020.socialnlp-1.8 · Cited by 15

ESSR: Evolving Sparse Sharing Representation for Multitask Learning

Zhang, Y., Qian, Y., Ma, G., Liang, X., Liu, G., Zhang, Q., & Tang, K. (2023) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2023.3272663 · Cited by 14

Transfer Hierarchical Attention Network for Generative Dialog System

Zhang, X., & Yang, Q. (2019) · Machine Intelligence Research · doi:10.1007/s11633-019-1200-0 · Cited by 13

Using context information to enhance simple question answering

Li, L., Zhang, M., Chao, Z., & Xiang, J. (2020) · World Wide Web · doi:10.1007/s11280-020-00842-7 · Cited by 14

Transfer and Multi-Task Learning for Noun–Noun Compound Interpretation

Fares, M., Oepen, S., & Velldal, E. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1178 · Cited by 13

Towards Explainable Fact CheckingPreprint

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