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 accessAbstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“However, unlike WSD, accuracies using the first biLM layer are higher than the top layer, consistent with results from deep biL-STMs in multi-task training (Søgaard and Goldberg, 2016;Hashimoto et al., 2017) and MT (Belinkov et al., 2017).”
From Deep Contextualized Word Representations (2018) · cites this paper as Søgaard and Goldberg, 2016
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
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-citationIntroduction 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).”
MethodRotating 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
Cited by
Sorted by citations
Items 201–300 of 449
EmoffMeme: identifying offensive memes by leveraging underlying emotions
Kumari, G., Bandyopadhyay, D., & Ekbal, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14807-1 · Cited by 12
DMMP: A distillation-based multi-task multi-tower learning model for personalized recommendation
Yi, Q., Tang, J., Zeng, Y., Zhang, X., & Xu, W. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111236 · Cited by 12
Neural Machine Translation for Bilingually Scarce Scenarios: a Deep Multi-Task Learning Approach
Zaremoodi, P., & Haffari, G. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1123 · Cited by 13
Neural CRF Transducers for Sequence Labeling
Hu, K., Ou, Z., Hu, M. Q., & Feng, J. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682728 · Cited by 12
On the Contributions of Visual and Textual Supervision in Low-Resource Semantic Speech Retrieval
Pasad, A., Shi, B., Kamper, H., & Livescu, K. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-3051 · Cited by 12
Descriptions from the Customers
Novgorodov, S., Guy, I., Elad, G., & Radinsky, K. (2020) · ACM Transactions on Internet Technology · doi:10.1145/3418202 · Cited by 12
Actigraphy-based Sleep/Wake Pattern Detection using Convolutional Neural NetworksPreprint
Granovsky, L., Shalev, G., Yacovzada, N., Frank, Y., & Fine, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.07945 · Cited by 17
Syntactic Scaffolds for Semantic StructuresPreprint
Swayamdipta, S., Thomson, S., Lee, K., Zettlemoyer, L., Dyer, C., & Smith, N. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.10485 · Cited by 12
Multi-Task Learning in Deep Neural Network for Sentiment Polarity and Irony classification.
Mattei, L. D., Cimino, A., & Dell’Orletta⋄, F. (2018) · Cited by 11
Clinical QA 2.0- Multi-Task Learning for Answer Extraction and Categorization
Pattnayak, P., Patel, H., Agarwal, A., Panda, S., Kumar, B., & Kumar, T. (2025) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit64391.2025.11103631 · Cited by 17
Joint learning-based causal relation extraction from biomedical literature
Li, D., Wu, P., Dong, Y., Gu, J., Qian, L., & Zhou, G. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104318 · Cited by 11
On the Use of Knowledge Transfer Techniques for Biomedical Named Entity Recognition
Mehmood, T., Serina, I., Lavelli, A., Putelli, L., & Gerevini, A. (2023) · Future Internet · doi:10.3390/fi15020079 · Cited by 12
Timely Fusion of Surround Radar/Lidar for Object Detection in Autonomous Driving Systems
Xie, W., Hu, T., Ling, N., Xing, G., Xue, C. J., & Guan, N. (2024) · IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA) · doi:10.1109/rtcsa62462.2024.00014 · Cited by 13
Killing Four Birds with Two Stones: Multi-Task Learning for Non-Literal Language Detection
Dinh, E.-L. D., Eger, S., & Gurevych, I. (2018) · TUbilio (Technical University of Darmstadt) · Cited by 11
Deep multi-task learning with flexible and compact architecture search
Zhao, J., Lv, W., Du, B., Ye, J., Sun, L., & Xiong, G. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00274-0 · Cited by 11
Multi-Relational Graph based Heterogeneous Multi-Task Learning in Community Question Answering
Lin, Z., Ke, H., Wong, N.-Y., Bai, J., Song, Y., Zhao, H., & Ye, J. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482279 · Cited by 11
Training Complex Models with Multi-Task Weak SupervisionPreprint
Ratner, A., Hancock, B. J., Dunnmon, J., Sala, F., Pandey, S., & Ré, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.02840 · Cited by 11
Multi-Task Deep Learning Methods for Determining Railway Major Technical Standards
Pu, H., Wang, G., Song, T., Schonfeld, P. M., & Zhang, H. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3338146 · Cited by 10
Pezzelle, S., Sorodoc, I.-T., & Bernardi, R. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1039 · Cited by 10
Lexicon information in neural sentiment analysis: a multi-task learning approach.
Barnes, J. C., Touileb, S., Øvrelid, L., & Velldal, E. (2019) · DSpace repository (University of Tartu) · Cited by 10
Simple Compounded-Label Training for Fact Extraction and Verification
Nie, Y., Bauer, L., & Bansal, M. (2020) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/2020.fever-1.1 · Cited by 10
LATEX-Numeric: Language Agnostic Text Attribute Extraction for Numeric Attributes
Mehta, K., Oprea, I., & Rasiwasia, N. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-industry.34 · Cited by 10
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.016 · Cited by 11
Bootstrapping Generators from Noisy Data
Perez-Beltrachini, L., & Lapata, M. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1137 · Cited by 10
Detecting Personal Life Events from Twitter by Multi-Task LSTM
Yen, A.-Z., Huang, H., & Chen, H. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3186909 · Cited by 10
Kementchedjhieva, Y., Bjerva, J., & Augenstein, I. (2018) · Proceedings of the · doi:10.18653/v1/k18-3011 · Cited by 10
Liu, W., & Strube, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.874 · Cited by 10
Cross-lingual Transfer Learning and Multitask Learning for Capturing Multiword Expressions
Taslimipoor, S., Rohanian, O., & Ha, L. A. (2019) · Workshop on Multiword Expressions (MWE) · doi:10.18653/v1/w19-5119 · Cited by 11
Task’s Choice: Pruning-Based Feature Sharing (PBFS) for Multi-Task Learning
Chen, Y., Yu, J., Zhao, Y., Chen, J., & Du, X. (2022) · Entropy · doi:10.3390/e24030432 · Cited by 10
Distantly Supervised Entity Relation Extraction with Adapted Manual Annotations
Sun, C., & Wu, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017039 · Cited by 9
Emerging Relation Network and Task Embedding for Multi-Task Regression Problems
Schreiber, J., & Sick, B. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412476 · Cited by 9
What’s in Your Head? Emergent Behaviour in Multi-Task Transformer Models
Geva, M., Katz, U., Ben-Arie, A., & Berant, J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.646 · Cited by 9
Leveraging Non-Conversational Tasks for Low Resource Slot Filling: Does it help?
Louvan, S., & Magnini, B. (2019) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w19-5911 · Cited by 9
Chen, Y., & Pan, R. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11010 · Cited by 9
Annotating Online Civic Discussion Threads for Argument Mining
Morio, G., & Fujita, K. (2018) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1109/wi.2018.00-39 · Cited by 9
Investigating NP-Chunking with Universal Dependencies for English
Lacroix, O. (2018) · Workshop on Universal Dependencies (UDW) · doi:10.18653/v1/w18-6010 · Cited by 9
Adaptively Weighted Multi-task Learning Using Inverse Validation Loss
Abbas, W., & Tap, M. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683776 · Cited by 9
Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification
Moore, A., & Barnes, J. (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.227 · Cited by 10
Narayanan, S., Achan, P., Rangan, P. V., & Rajan, S. P. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103898 · Cited by 11
Zhang, Y., Jiang, K., Jiang, W., Wang, N., Wright, A. J., Liu, A., & Wang, J. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4443 · Cited by 8
Learning multi-tasks with inconsistent labels by using auxiliary big task
Feng, Q., & Chen, S. (2023) · Frontiers of Computer Science · doi:10.1007/s11704-022-2251-x · Cited by 8
Gupta, S. S., Lee, S., De‐Arteaga, M., & Lease, M. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583290 · Cited by 10
What can we learn from Semantic Tagging?
Abdou, M., Kulmizev, A., Ravishankar, V., Abzianidze, L., & Bos, J. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1526 · Cited by 9
Transfer Learning: Scenarios, Self-Taught Learning, and Multitask Learning
Kamath, U., Liu, J., & Whitaker, J. (2019) · Springer eBooks · doi:10.1007/978-3-030-14596-5_10 · Cited by 8
Morio, G., & Fujita, K. (2019) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icosc.2019.8665505 · Cited by 8
Deep Automated Multi-task LearningPreprint
Liang, D., & Shu, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.05554 · Cited by 8
Exploring the Syntactic Abilities of RNNs with Multi-task LearningPreprint
Enguehard, É., Goldberg, Y., & Linzen, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.03542 · Cited by 10
Self-citationLearning to Generalize Compositionally by Transferring Across Semantic Parsing TasksPreprint
Wang, Z., Shaw, P. M., Linzen, T., & Sha, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05013 · Cited by 12
A Markovian Kernel-based Approach for itaLIan Speech acT labEliNg
Croce, D., & Basili, R. (2018) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.4661 · Cited by 7
UKP TU-DA at GermEval 2017: Deep Learning for Aspect Based Sentiment Detection
Lee, J.-U., Eger, S., Daxenberger, J., & Gurevych, I. (2017) · TUbilio (Technical University of Darmstadt) · Cited by 7
Multi-task Learning in Argument Mining for Persuasive Online Discussions
Tran, N., & Litman, D. J. (2021) · Workshop on Argument Mining (ArgMining) · doi:10.18653/v1/2021.argmining-1.15 · Cited by 7
Chinese Named Entity Recognition Model Based on Multi-Task Learning
Fang, Q., Li, Y., Feng, H., & Ruan, Y. (2023) · Applied Sciences · doi:10.3390/app13084770 · Cited by 7
Learning Conflict-Noticed Architecture for Multi-Task Learning
Yue, Z., Zhang, Y., & Liang, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26312 · Cited by 8
A Novel, Cognitively Inspired, Unified Graph-based Multi-Task Framework for Information Extraction
Zheng, Y., & Tuan, L. A. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10163-2 · Cited by 7
Understanding the Representational Power of Neural Retrieval Models Using NLP Tasks
Cohen, D., O'Connor, B., & Croft, W. B. (2018) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3234944.3234959 · Cited by 8
Exploring Segment Representations for Neural Semi-Markov Conditional Random Fields
Liu, Y., Che, W., Qin, B., & Liu, T. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.2964960 · Cited by 9
NDDR-LCS: A Multi-Task Learning Method for Classification of Carotid Plaques
Shen, H., Zhang, W., Wang, H., Ding, G., & Xie, J. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190690 · Cited by 7
Multi-task Learning Model for Detecting Internet Slang Words with Two-Layer Annotation
Seki, Y., & Liu, Y. (2022) · International Conference on Asian Language Processing · doi:10.1109/ialp57159.2022.9961254 · Cited by 7
Meta-Learning Multi-task CommunicationPreprint
Liu, P., & Huang, X. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.09988 · Cited by 8
Joint Multi-Domain Learning for Automatic Short Answer GradingPreprint
Saha, S., Dhamecha, T. I., Marvaniya, S., Foltz, P. W., Sindhgatta, R., & Sengupta, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.09183 · Cited by 10
Understanding character-level models for representing morphology
Vania, C. (2020) · ERA · doi:10.7488/era/49 · Cited by 7
Proceedings of the 5th Workshop on Argument Mining
(2018) · doi:10.18653/v1/w18-52 · Cited by 7
On Identifying Hashtags in Disaster Twitter Data
Chowdhury, J. R., Caragea, C., & Caragea, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5387 · Cited by 6
GIRNet: Interleaved Multi-Task Recurrent State Sequence Models
Gupta, D., Chakraborty, T., & Chakrabarti, S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016497 · Cited by 6
Predicting Decisions in Language Based Persuasion Games
Apel, R., Erev, I., Reichart, R., & Tennenholtz, M. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13510 · Cited by 6
Bag-of-Words Transfer: Non-Contextual Techniques for Multi-Task Learning
Ebner, S., Wang, F., & Durme, B. V. (2019) · Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo) · doi:10.18653/v1/d19-6105 · Cited by 7
Cross-modality deep learning-based prediction of TAP binding and naturally processed peptide
Besser, H., & Louzoun, Y. (2018) · Immunogenetics · doi:10.1007/s00251-018-1054-6 · Cited by 6
Multi-task Stack Propagation for Neural Quality Estimation
Kim, H. J., Lee, J.-H., & Na, S. (2019) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3321127 · Cited by 6
REET: Joint Relation Extraction and Entity Typing via Multi-task Learning
Liu, H., Wang, P., Wu, F., Jiao, P., Wang, W., Xie, X., & Sun, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32233-5_26 · Cited by 6
Label Attention Network for Structured Prediction
Cui, L., Li, Y., & Zhang, Y. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3145311 · Cited by 6
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain
Lange, L., Adel, H., & Strötgen, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.621 · Cited by 6
Spurious Correlations in Cross-Topic Argument Mining
Jakobsen, T. S. T., Barrett, M., & Søgaard, A. (2021) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/2021.starsem-1.25 · Cited by 6
Self-citationMulti-task Learning over Graph StructuresPreprint
Liu, P., Fu, J., Dong, Y., Qiu, X., & Cheung, J. C. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.10211 · Cited by 7
Multi-Task Learning of Keyphrase Boundary ClassificationPreprint
Augenstein, I., & Søgaard, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.00514 · Cited by 6
Self-citationEmotion and Intensity Prediction Using Hybrid Multi-Task BERT_BiLSTM Deep Learning Model
Parmar, M., & Tiwari, A. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10144-7 · Cited by 5
Instance-Based Transfer Learning
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.004 · Cited by 5
Joint Learning of Named Entity Recognition and Dependency Parsing using Separate Datasets
Akdemir, A., & Güngör, T. (2019) · Computación y Sistemas · doi:10.13053/cys-23-3-3247 · Cited by 5
Integrating Graph-Based and Transition-Based Dependency Parsers in the Deep Contextualized Era
Faleńska, A., Björkelund, A. J., & Kuhn, J. (2020) · International Workshop/Conference on Parsing Technologies · doi:10.18653/v1/2020.iwpt-1.4 · Cited by 5
Kankanampati, Y., Roux, J. L., Tomeh, N., Taji, D., & Habash, N. Y. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.225 · Cited by 6
Joint Learning for Multitasking Models
Chemmanam, A. J., & Jose, B. A. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-4453-6_11 · Cited by 6
Data-efficient Neural Text Compression with Interactive Learning
Avinesh, P. V. S., & Meyer, C. M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1262 · Cited by 5
Upgrading CRFS to JRFS and its Benefits to Sequence Modeling and Labeling
Song, Y., Ou, Z., Liu, Z., & Yang, S. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053817 · Cited by 5
Identifying communicative functions in discourse with content types
Caselli, T., Rachele, S., & Moretti, G. (2021) · Language Resources and Evaluation · doi:10.1007/s10579-021-09554-4 · Cited by 6
Naik, A., Lehman, J. F., & Rosé, C. P. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00500 · Cited by 6
Hierarchical Multilabel Text Classification via Multitask Learning
Yu, Y., Sun, Z., Sun, C. C., & Liu, W. (2021) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai52525.2021.00180 · Cited by 5
Shi, J., Li, D., Chu, X., Yang, J., & Shen, C. (2022) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.16.038504 · Cited by 7
Shared-Private LSTM for Multi-domain Text Classification
Wu, H., Zhang, Y., Jin, X., Xue, Y., & Wang, Z. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32236-6_10 · Cited by 5
Deep Semi-Supervised Learning with Linguistically Motivated Sequence Labeling Task HierarchiesPreprint
Godwin, J., Stenetorp, P., & Riedel, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.09113 · Cited by 5
Learning Functions to Study the Benefit of Multitask LearningPreprint
Bettgenhäuser, G., Hedderich, M. A., & Klakow, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.05561 · Cited by 5
Orthogonalized SGD and Nested Architectures for Anytime Neural NetworksPreprint
Wan, C., Hoffmann, H., Lu, S., & Maire, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.06635 · Cited by 5
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)
de Marneffe, M.-C., Lynn, T., Schuster, S., & 2018), P. O. T. S. W. O. U. D. (. (2018) · doi:10.18653/v1/w18-60 · Cited by 5
Word-Level Loss Extensions for Neural Temporal Relation Classification
Leeuwenberg, A., & Moens, M. (2018) · Lirias · doi:10.48550/arxiv.1808.02374 · Cited by 4
Hybrid contrastive multi-scenario learning for multi-task sequential-dependence recommendation
Yi, Q., Wu, L., Tang, J., Zeng, Y., & Song, Z. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106953 · Cited by 4
Towards Combining Multitask and Multilingual Learning
Pikuliak, M., Šimko, M., & Bieliková, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10801-4_34 · Cited by 4
In Situ Answer Sentence Selection at Web-scale
Zhang, Z., Vu, T., & Moschitti, A. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679946 · Cited by 4
Better, Faster, Stronger Sequence Tagging Constituent Parsers
Vilares, D., Abdou, M., & Søgaard, A. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1341 · Cited by 4
Self-citation
Metadata
- Authors
- Søgaard, A., & Goldberg, Y.
- Affiliations
- University of Copenhagen (DK)
- Venue
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Published
- 2016
- Pages
- 231-235
- ISSN
- 0736-587X (ISSN-L)
- 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
