Cited by 449Natural Language Processing TechniquesDomain Adaptation and Few-Shot LearningTopic Modeling

The DOI matches its Crossref record; 15 of the 16 references it cites were found in a registry or an index.

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

Open access

Søgaard, Anders, Goldberg, Yoav·2016·Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

DOI 10.18653/v1/p16-2038

Open this paper in CoChat — free account

Abstract

In all previous work on deep multi-task learning we are aware of, all task supervisions are on the same (outermost) layer. We present a multi-task learning architecture with deep bi-directional RNNs, where different tasks supervision can happen at different layers. We present experiments in syntactic chunking and CCG supertagging, coupled with the additional task of POS-tagging. We show that it is consistently better to have POS supervision at the innermost rather than the outermost layer. We argue that this is because “lowlevel” tasks are better kept at the lower layers, enabling the higher-level tasks to make use of the shared representation of the lower-level tasks. Finally, we also show how this architecture can be used for domain adaptation.

Integrity signals

Registry

Crossref · 10.18653/v1/p16-2038

Cites

15 of 15 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Long Short-Term Memory
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Finding structure in time
Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
Natural Language Processing (almost) from ScratchPreprint
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 6,923
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 1,316
Self-citation
Introduction to the CoNLL-2000 shared task
Sang, E. T. K., & Buchholz, S. (2000) · Conference on Computational Natural Language Learning · doi:10.3115/1117601.1117631 · Cited by 713
The CoNLL 2007 Shared Task on Dependency Parsing
Nivre, J., Hall, J., Kübler, S., McDonald, R., Nilsson, J., Riedel, S., & Yüret, D. (2007) · Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) · Cited by 663
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
Irsoy, O., & Cardie, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1080 · Cited by 384 · cited 3×
“We experiment with a multi-task learning (MTL) architecture based on deep bi-directional recurrent neural networks (bi-RNNs) (Schuster and Paliwal, 1997; Irsoy and Cardie, 2014).”
Method
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

Show all 16 references

Sorted by citations

Items 301–400 of 449

Multi-Task Learning for Japanese Predicate Argument Structure Analysis
Omori, H., & Komachi, M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1344 · Cited by 4
Artificially Evolved Chunks for Morphosyntactic Analysis
Anderson, M., Vilares, D., & Gómez‐Rodríguez, C. (2019) · International Workshop on Treebanks and Linguistic Theories (TLT) · doi:10.18653/v1/w19-7815 · Cited by 5
Dependency Parsing as Sequence Labeling with Head-Based Encoding and Multi-Task Learning
Lacroix, O. (2019) · International Conference on Dependency Linguistics (Depling) · doi:10.18653/v1/w19-7716 · Cited by 4
Does Multi-Task Learning Always Help?: An Evaluation on Health Informatics
Joshi, A., Karimi, S., Sparks, R. S., Paris, C. L., & MacIntyre, C. R. (2019) · Cited by 4
Fine-grained Chinese Named Entity Recognition in Entertainment News Using Adversarial Multi-task Learning
Xu, M., & Yang, P. (2019) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc47050.2019.9064233 · Cited by 4
Research on Task Discovery for Transfer Learning in Deep Neural Networks
Akdemir, A. (2020) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/2020.acl-srw.6 · Cited by 5
Time Series Load Forecasting using Multitask Deep Neural Network
Kiruthiga, D., & Manikandan, V. (2021) · IEEE International Conference on Control, Measurement and Instrumentation · doi:10.1109/cmi50323.2021.9362936 · Cited by 4
PD3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection
Eger, S., Rücklé, A., & Gurevych, I. (2018) · Workshop on Argument Mining (ArgMining) · doi:10.18653/v1/w18-5216 · Cited by 4
On the Inductive Bias of Word-Character-Level Multi-Task Learning for Speech RecognitionPreprint
Kremer, J., Borgholt, L., & Maaløe, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.02308 · Cited by 4
Issue Framing in Online Discussion ForaPreprint
Hartmann, M., Jansen, T., Augenstein, I., & Søgaard, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.03969 · Cited by 7
Self-citation
LTG-Oslo Hierarchical Multi-task Network: The importance of negation for document-level sentiment in SpanishPreprint
Barnes, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.07599 · Cited by 4
Hierarchical Multitask Learning Approach for BERTPreprint
Aksoy, Ç., Ahmetoğlu, A., & Güngör, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.04451 · Cited by 4
Energy-Based Models with Applications to Speech and Language Processing
Ou, Z. (2024) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000117 · Cited by 5
Transfer Learning in Reinforcement Learning
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.010 · Cited by 3
Uncertainty Regularized Multi-Task Learning
Meshgi, K., Mirzaei, M. S., & Sekine, S. (2022) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/2022.wassa-1.8 · Cited by 3
Learning to Resolve Conflicts in Multi-Task Learning
Tang, M., Jin, Z., Zou, L., & Liang, S.-N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44213-1_40 · Cited by 3
A Hybrid Contextual Embedding and Hierarchical Attention for Improving the Performance of Word Sense Disambiguation
Habtamu, R., Assefa, B. G., & Belay, E. G. (2025) · IEEE Access · doi:10.1109/access.2025.3536300 · Cited by 3
Combining Cross-lingual and Cross-task Supervision for Zero-Shot Learning
Pikuliak, M., & Šimko, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58323-1_17 · Cited by 3
Seeing Both the Forest and the Trees: Multi-head Attention for Joint Classification on Different Compositional Levels
Pislar, M., & Rei, M. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.335 · Cited by 3
Corpus-Aware Graph Aggregation Network for Sequence Labeling
Chen, H., Ma, Q., Yu, L., Lin, Z., & Yan, J. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3084105 · Cited by 3
Knowledge based natural answer generation via masked-graph transformer
Li, X., Hu, S., & Zou, L. (2021) · World Wide Web · doi:10.1007/s11280-021-00932-0 · Cited by 3
Few-Shot Learning
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.015 · Cited by 3
MaChAmp at SemEval-2022 Tasks 2, 3, 4, 6, 10, 11, and 12: Multi-task Multi-lingual Learning for a Pre-selected Set of Semantic Datasets
Goot, R. (2022) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2022.semeval-1.233 · Cited by 3
A Semi-shared Hierarchical Joint Model for Sequence Labeling
Gongshen, L., Wei, D., Zhou, J., Li, J., & Jie, C. (2023) · Chinese Journal of Electronics · doi:10.23919/cje.2020.00.363 · Cited by 3
An End-to-End Scalable Iterative Sequence Tagging with Multi-Task Learning
Gui, L., Du, J., Zhao, Z., He, Y., Xu, R., & Fan, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99501-4_25 · Cited by 3
Who will Win the Data Science Competition? Insights from KDD Cup 2019 and Beyond
Liu, H., Guo, Q., Zhu, H., Zhuang, F., Yang, S., Dou, D., & Xiong, H. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3511896 · Cited by 3
Word Embedding Algorithms as Generalized Low Rank Models and their Canonical FormPreprint
Kenyon-Dean, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.02639 · Cited by 3
Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)
Gerdes, K., & Kahane, S. (2019) · doi:10.18653/v1/w19-77 · Cited by 3
Multi-Dimensional Assessment of Rehabilitation Exercises with Multi-gate Mixture-of-Experts
He, T. C., Wang, L., & Cheng, H. (2025) · doi:10.1145/3730436.3730498 · Cited by 3
JIT-MTL: Just-in-Time Defect Localization and Prediction with Multi-Task Learning
Shen, Z., Zou, W., Chen, X., Ge, J., Li, C., Cao, S., Zhang, X., Huang, L., & Luo, B. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3797269 · Cited by 2
Sequence Labelling Using Multi-Task Learning with Random Connections
Ou, R. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00349 · Cited by 2
Hydra-Mask-RCNN: An Adaptive HydraNet Architecture for Autonomous Aerial Vehicle Object Detection
Golestani, S., SadeghiBakhi, M., Ma, K. F., & Leung, H. (2024) · IEEE Open Journal of Instrumentation and Measurement · doi:10.1109/ojim.2024.3502886 · Cited by 3
Multi-Task Learning for Semantic Relatedness and Textual Entailment
Zhang, L., & MOLDOVAN, D. I. (2019) · Journal of Software Engineering and Applications · doi:10.4236/jsea.2019.126012 · Cited by 2
Multi-Team: A Multi-attention, Multi-decoder Approach to Morphological Analysis.
Üstün, A. B., van der Goot, R., Bouma, G., & van Noord, G. (2019) · SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology (SIGMORPHON) · doi:10.18653/v1/w19-4206 · Cited by 2
Gated Task Interaction Framework for Multi-task Sequence Tagging
Ampomah, I. K. E., Lin, Z., McClean, S. I., & Hawe, G. I. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851801 · Cited by 2
Person Name Recognition with Fine-grained Annotation
Dai, Y., Zhang, R., & Qi, J. (2020) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/3383583.3398515 · Cited by 2
DeftPunk at SemEval-2020 Task 6: Using RNN-ensemble for the Sentence Classification.
Kaparina, J., & Соболева, А. А. (2020) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2020.semeval-1.91 · Cited by 2
Annotations Matter: Leveraging Multi-task Learning to Parse UD and SUD
Sayyed, Z. A., & Dakota, D. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.305 · Cited by 2
Model-Based Transfer Learning
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.006 · Cited by 2
Transfer Learning Theory
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.012 · Cited by 2
Transfer Learning in Bioinformatics
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.023 · Cited by 2
Q-Learning Scheduler for Multi Task Learning Through the use of Histogram of Task Uncertainty
Meshgi, K., Mirzaei, M. S., & Sekine, S. (2022) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/2022.repl4nlp-1.2 · Cited by 2
Semi-Supervised Semantic Role Labeling with Bidirectional Language Models
Munir, K., Zhao, H., & Li, Z. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3587160 · Cited by 2
SCRE: special cargo relation extraction using representation learning
Reshadat, V., Akçay, A., Zervanou, K., Zhang, Y., & de Jong, E. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08704-9 · Cited by 3
Deep Transformer-based Representation for Text Chunking
Kavehzadeh, P., Pour, M. M. A., & Momtazi, S. (2023) · Journal of Information Systems and Telecommunication (JIST) · doi:10.61186/jist.19894.11.43.176 · Cited by 2
Computer challenges guillotine: how an artificial player can solve a complex language TV game with web data analysis
Squadrone, L. (2018) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.4854 · Cited by 2
CLaC at SemEval-2020 Task 5: Muli-task Stacked Bi-LSTMs
Sung, M., Bagherzadeh, P., & Bergler, S. (2020) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2020.semeval-1.54 · Cited by 2
Transitive Transfer Learning
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.013 · Cited by 2
Improving the Robustness of Loanword Identification in Social Media Texts
Mi, C. (2022) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3572773 · Cited by 2
Japanese Event Factuality Analysis in the Era of BERT
Kameko, H., Murawaki, Y., Matsuyoshi, S., & Mori, S. (2023) · IEEE Access · doi:10.1109/access.2023.3308916 · Cited by 2
Automatic Detection and Classification of Argument Components using Multi-task Deep Neural Network
Mensonides, J.-C., Harispe, S., Montmain, J., & Thireau, V. (2019) · HAL · Cited by 2
Noise processing and multi-task learning for far-field dialect classification
Wang, H., Qin, C., Zhang, K., Gao, L. C., & Ren, J. (2020) · International Conference on Advanced Cloud and Big Data · doi:10.1109/cbd51900.2020.00034 · Cited by 2
Transfer Learning in Activity Recognition
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.024 · Cited by 2
An End-to-end Hierarchical Multi-task Learning Framework of Sentiment Analysis and Key Entity Identification for Online Financial Texts
Zheng, X., Li, L., & Zhang, W. (2021) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd49262.2021.9437816 · Cited by 2
Transfer Learning in Computer Vision
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.019 · Cited by 2
Multi-task Learning for Japanese Predicate Argument Structure AnalysisPreprint
Omori, H., & Komachi, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.02244 · Cited by 2
Automatic processing of code-mixed social media content
Barman, U. (2019) · Arrow@dit (Dublin Institute of Technology) · Cited by 2
TPG-DNN: A Method for User Intent Prediction Based on Total Probability Formula and GRU Loss with Multi-task LearningPreprint
Jiang, J., Wang, Z., Fang, F., & Zhao, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.02122 · Cited by 2
Neural approaches to discourse coherence: modeling, evaluation and application
Farag, Y. (2020) · Apollo (University of Cambridge) · doi:10.17863/cam.70024 · Cited by 2
Introduction
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.003 · Cited by 3
Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)
(2021) · doi:10.18653/v1/2021.iwpt-1 · Cited by 2
Neural networks regularization through representation learning
Belharbi, S. (2018) · theses.fr (ABES) · doi:10.70675/0e1f98e2zabc7z497bzaf40z433d33a78b60 · Cited by 3
Feature-Based Transfer Learning
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.005 · Cited by 1
Infusing Syntax and Semantics into LLMs
Labate, A. B., & Cozman, F. G. (2026) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-026-01341-9 · Cited by 2
Detecting Falls and Slips of Wheelchair Users Using Low-Resolution Thermal Image Analysis
Nakamura, S., Yamauchi, M., Sugita, M., Aso, Y., Ohsita, Y., & Shimonishi, H. (2025) · IEEE Access · doi:10.1109/access.2025.3591606 · Cited by 1
State-Specific Explainable Machine Learning for Predicting Premature Dropout in Medication for Opioid Use Disorder
Guo, X., Wang, T., Guo, Y., Vivas-Valencia, C., Bauer, C., & Gong, Y. (2025) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1145/3721201.3721390 · Cited by 1
Beyond green words: A natural language processing-driven disclosure-emission gap index for detecting corporate carbon washing in China
Mai, S., Zhang, Z., Zhu, J., Mai, W., & Deng, G. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01075-w · Cited by 1
Aspect-based Sentiment Analysis: X2Check at ABSITA 2018
Rosa, E. D., & Durante, A. (2018) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.4545 · Cited by 1
ItVENSES - A Symbolic System for Aspect-Based Sentiment Analysis
Delmonte, R. (2018) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.4539 · Cited by 1
Multi-task Learning
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.011 · Cited by 1
A Multi-task Framework for Named Entity Recognition
Hu, H., Liu, T., Feng, H., Ruan, Y., & Li, Y. (2021) · International Conference on Computer Information Science and Artificial Intelligence (CISAI) · doi:10.1109/cisai54367.2021.00018 · Cited by 1
Enhance Rnnlms with Hierarchical Multi-Task Learning for ASR
Song, M., & Zhao, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747525 · Cited by 3
Multi-Task Learning with Sequential Dependence Toward Industrial Applications: A Systematic Formulation
Guo, X., Ha, M., Tao, X., Li, S., Li, Y., Zhu, Z., Shen, Z., & Ma, L. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3640468 · Cited by 1
Multa: Enabling Cost-efficient Multi-task Network Traffic Analysis
Guo, C., Zhang, M., Li, G., & Xu, M. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437133 · Cited by 1
Explicitly modeling case improves neural dependency parsing
Vania, C., & Lopez, A. (2018) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w18-5447 · Cited by 1
Multi-Task Learning for Chinese Word Usage Errors Detection
Zhang, J., & Wang, H. (2018) · International Conference on Computational Intelligence and Applications (ICCIA) · doi:10.1109/iccia.2018.00024 · Cited by 1
Transfer Learning in Recommender Systems
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.022 · Cited by 1
Adversarial Semi-supervised Multi-domain Tracking
Meshgi, K., & Mirzaei, M. S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69532-3_37 · Cited by 1
Quasi-Multitask Learning: an Efficient Surrogate for Obtaining Model Ensembles
Kis-Szabó, N., & Berend, G. (2020) · Workshop on Simple and Efficient Natural Language Processing (SustaiNLP) · doi:10.18653/v1/2020.sustainlp-1.13 · Cited by 2
LiteMuL: A Lightweight On-Device Sequence Tagger using Multi-task Learning
Kumari, S., Agarwal, V., Challa, B., Chalamalasetti, K., Ghosh, S., Harshavardhana, H., & Raja, B. R. K. (2021) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc50631.2021.00007 · Cited by 1
UPB at SemEval-2021 Task 7: Adversarial Multi-Task Learning for Detecting and Rating Humor and Offense
Smădu, R.-A., Cercel, D.-C., & Dascălu, M. (2021) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2021.semeval-1.163 · Cited by 1
Privacy-Preserving Transfer Learning
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.018 · Cited by 1
Bidirectional Domain Adaptation Using Weighted Multi-Task Learning
Dakota, D., Sayyed, Z. A., & Kübler, S. (2021) · International Workshop/Conference on Parsing Technologies · doi:10.18653/v1/2021.iwpt-1.10 · Cited by 1
AutoTL: Learning to Transfer Automatically
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.014 · Cited by 1
Multi-task Sequence Classification for Disjoint Tasks in Low-resource Languages
Radom, J., & Kocoń, J. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.08.116 · Cited by 1
Interacting Knowledge Sources, Inspection and Analysis: Case-studies on Biomedical text processing
Bagherzadeh, P., & Bergler, S. (2021) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2021.blackboxnlp-1.35 · Cited by 1
Competing Independent Modules for Knowledge Integration and Optimization
Bagherzadeh, P., & Bergler, S. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.376 · Cited by 1
Relation Representation Learning for Special Cargo Ontology
Reshadat, V., Akçay, A., Zervanou, K., Zhang, Y., & de Jong, E. (2021) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci50451.2021.9660108 · Cited by 1
Relation-Based Transfer Learning
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.007 · Cited by 1
Saliency-Assisted Collaborative Learning Network for Road Scene Semantic Segmentation
Sima, H., Xu, Y., Du, M., Gao, M., & Wang, J. (2023) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2023.03.010 · Cited by 1
Macular: A Multi-Task Adversarial Framework for Cross-Lingual Natural Language Understanding
Wang, H., Wang, Y., Wu, F., Xue, H., & Gao, J. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599864 · Cited by 3
Deep Cascade Multi-task Learning for Slot Filling in Chinese E-commerce Shopping Guide Assistant.Preprint
Gong, Y., Luo, X., Zhu, K. Q., Chen, X., & Ou, W. (2018) · arXiv (Cornell University) · Cited by 1
Multi-Task Learning for Argumentation MiningPreprint
Kahse, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.10162 · Cited by 1
Improving Span-based Question Answering Systems with Coarsely Labeled DataPreprint
Cheng, H., Chang, M.-W., Lee, K., Parikh, A. P., Collins, M., & Toutanova, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.02076 · Cited by 1
Predicting In-game Actions From the Language of NBA PlayersPreprint
Oved, N., Feder, A., & Reichart, R. (2019) · arXiv (Cornell University) · Cited by 1
NameRec*: Highly Accurate and Fine-grained Person Name RecognitionPreprint
Zhang, R., Dai, Y., & Liu, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.11360 · Cited by 1
Recognising Biomedical Names: Challenges and SolutionsPreprint
Dai, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.12230 · Cited by 3
Discontinuous constituency parsing of morphologically rich languages
Coavoux, M. (2017) · theses.fr (ABES) · doi:10.70675/8bb6891eza6f9z4466z8da9zfab12af43750 · Cited by 1
Multi-lingual dependency parsing : word representation and joint training for syntactic analysis
Dehouck, M. (2019) · theses.fr (ABES) · doi:10.70675/1a37f05bzfd63z4cb1zbf35zae267ad7cda7 · Cited by 1
Argument Mining on Clinical Trials
Mayer, T. (2020) · theses.fr (ABES) · doi:10.70675/8c2cf7b5z08cbz40e4za421z0115aa4a8eaa · Cited by 1

Authors: Søgaard, A., & Goldberg, Y.
Affiliations: University of Copenhagen (DK)
ORCID: Søgaard, Anders
Goldberg, Yoav
Venue: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Publisher: Association for Computational Linguistics
Published: 2016
Pages: 231-235
ISSN: 0736-587X (ISSN-L)
DOI: 10.18653/v1/p16-2038
OpenAlex: W2516255829
Cited by: 445
References: 16
Citations by year: 2026: 5 · 2025: 11 · 2024: 12 · 2023: 32 · 2022: 35 · 2021: 42 · 2020: 87 · 2019: 95 · 2018: 86 · 2017: 37 · 2016: 2
FWCI: 39.51
Type: conference paper
Language: en
Open access: bronze
doi.org
Locations: Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion)
Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords: multi-task deep learning, bidirectional RNN, part-of-speech tagging, shared representations, domain adaptation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks