Neural Structural Correspondence Learning for Domain Adaptation

Distributed Representations of Words and Phrases and their Compositionality

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Auto-Encoding Variational Bayes

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Domain-Adversarial Training of Neural Networks

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Extracting and composing robust features with denoising autoencoders

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Unsupervised Domain Adaptation by BackpropagationPreprint

Ganin, Y., & Lempitsky, V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.7495 · Cited by 5,360 · see citing works

Greedy layer-wise training of deep networks

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Stochastic Backpropagation and Approximate Inference in Deep Generative ModelsPreprint

Rezende, D. J., Mohamed, S., & Wierstra, D. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1401.4082 · Cited by 3,679 · see citing works

A theory of learning from different domains

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Correcting Sample Selection Bias by Unlabeled Data

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Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach

Glorot, X., Bordes, A., & Bengio, Y. (2011) · Cited by 1,569 · see citing works

Domain adaptation with structural correspondence learning

Blitzer, J., McDonald, R., & Pereira, F. (2006) · Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06 · doi:10.3115/1610075.1610094 · Cited by 1,543 · see citing works

Frustratingly Easy Domain AdaptationPreprint

Daumé, H. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0907.1815 · Cited by 1,471 · see citing works

A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data

Ando, R. K., & Zhang, T. (2005) · doi:10.5555/1046920.1194905 · Cited by 1,319 · see citing works

Domain Adaptation for Statistical Classifiers

Daume III, H., & Marcu, D. (2006) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1872 · Cited by 816 · see citing works

Cross-domain sentiment classification via spectral feature alignment

Pan, S. J., Ni, X., Sun, J.-T., Yang, Q., & Chen, Z. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772767 · Cited by 791 · see citing works

Instance Weighting for Domain Adaptation in NLP

Jiang, J., & Zhai, C. (2007) · Cited by 780 · see citing works

Marginalized Denoising Autoencoders for Domain AdaptationPreprint

Chen, M., Xu, Z., Weinberger, K. Q., & Sha, F. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.4683 · Cited by 742 · see citing works

Some statistical issues in the comparison of speech recognition algorithms

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Frustratingly Easy Domain Adaptation

Daum, H. (2007) · Cited by 605 · see citing works

Untitled work

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Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification

Yu, J., & Jiang, J. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1023 · Cited by 215 · see citing works

Topic-bridged PLSA for cross-domain text classification

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Adaptation of maximum entropy capitalizer: Little data can help a lot

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The Variational Fair AutoencoderPreprint

Louizos, C., Swersky, K., Li, Y., Welling, M., & Zemel, R. S. (2015) · UvA-DARE (University of Amsterdam) · doi:10.48550/arxiv.1511.00830 · Cited by 178 · see citing works

Automatic Domain Adaptation for Parsing

McClosky, D., Eugene Charniak, D., & Johnson, M. (2010) · Cited by 155 · see citing works

Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-Domain Sentiment Classification

Bollegala, D., Weir, D. J., & Carroll, J. M. (2011) · Figshare · Cited by 141 · see citing works

Adaptation of Maximum Entropy Capitalizer: Little Data Can Help a Lo.

Chelba, C. I., & Acero, A. (2004) · Empirical Methods in Natural Language Processing · Cited by 105 · see citing works

Unsupervised Cross-Domain Word Representation Learning

Bollegala, D., Maehara, T., & Kawarabayashi, K. (2015) · doi:10.3115/v1/p15-1071 · Cited by 97 · see citing works

Supervised and unsupervised PCFG adaptation to novel domains

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Self-Training for Enhancement and Domain Adaptation of Statistical Parsers Trained on Small Datasets

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Automatic Feature Decomposition for Single View Co-training

Chen, M., Chen, Y., & Weinberger, K. Q. (2011) · Cited by 86 · see citing works

A Domain Adaptation Regularization for Denoising Autoencoders

Clinchant, S., Csurka, G., & Chidlovskii, B. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2005 · Cited by 40 · see citing works

Fast Easy Unsupervised Domain Adaptation with Marginalized Structured Dropout

Yang, Y., & Eisenstein, J. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-2088 · Cited by 29 · see citing works

Relation Adaptation: Learning to Extract Novel Relations with Minimum Supervision

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2012) · Cited by 19 · see citing works

Towards robust cross-domain domain adaptation for part-of-speech tagging

Schnabel, T. (2013) · OPUS Publication Server of the University of Stuttgart (University of Stuttgart) · doi:10.18419/opus-3064 · Cited by 12 · see citing works

Minimally Supervised Novel Relation Extraction Using a Latent Relational Mapping

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.250 · Cited by 12 · see citing works

Learning structural correspondences across different linguistic domains with synchronous neural language models

Gouws, S., & G-J, V. R. (2012) · SUNScholar (Stellenbosch University) · Cited by 7 · see citing works

The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing

Dror, R., Baumer, G., Shlomov, S., & Reichart, R. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1128 · Cited by 384 · see citing works

Transfer Learning

Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773 · Cited by 272 · see citing works

Domain Adaptation: Challenges, Methods, Datasets, and Applications

Singhal, P., Walambe, R. A., Ramanna, S., & Kotecha, K. V. (2023) · IEEE Access · doi:10.1109/access.2023.3237025 · Cited by 253 · see citing works

Neural Unsupervised Domain Adaptation in NLP—A Survey

Ramponi, A., & Plank, B. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.603 · Cited by 253 · see citing works

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

Retrospective Reader for Machine Reading Comprehension

Zhang, Z., Yang, J., & Zhao, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17705 · Cited by 200 · see citing works

Multi-Source Domain Adaptation with Mixture of Experts

Guo, J., Shah, D., & Barzilay, R. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1498 · Cited by 168 · see citing works

Pivot Based Language Modeling for Improved Neural Domain Adaptation

Ziser, Y., & Reichart, R. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1112 · Cited by 139 · see citing works

Named Entity Recognition without Labelled Data: A Weak Supervision Approach

Lison, P., Barnes, J., Hubin, A., & Touileb, S. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.139 · Cited by 112 · 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

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

A data-centric review of deep transfer learning with applications to text data

Bashath, S., Perera, N., Tripathi, S., Manjang, K., Dehmer, M., & Emmert‐Streib, F. (2021) · Information Sciences · doi:10.1016/j.ins.2021.11.061 · Cited by 72 · see citing works

Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets

Dror, R., Baumer, G., Bogomolov, M., & Reichart, R. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00074 · Cited by 63 · see citing works

Does BERT need domain adaptation for clinical negation detection?

Lin, C., Bethard, S. J., Dligach, D., Sadeque, F., Savova, G., & Miller, T. A. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa001 · Cited by 59 · see citing works

Cross-Domain Sentiment Classification by Capsule Network With Semantic Rules

Zhang, B., Xu, X., Yang, M., Chen, X., & Ye, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2874623 · Cited by 56 · see citing works

Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network

Bian, S., Zhao, W. X., Song, Y., Zhang, T., & Wen, J.-R. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1487 · Cited by 56 · see citing works

PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains

Ben-David, E., Oved, N., & Reichart, R. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00468 · Cited by 54 · see citing works

Explainable Misinformation Detection Across Multiple Social Media Platforms

Joshi, G., Srivastava, A., Yagnik, B., Hasan, M. M., Saiyed, Z., Gabralla, L. A., Abraham, A., Walambe, R. A., & Kotecha, K. V. (2023) · IEEE Access · doi:10.1109/access.2023.3251892 · Cited by 60 · see citing works

PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models

Ben-David, E., Rabinovitz, C., & Reichart, R. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00328 · Cited by 52 · see citing works

Transformer Based Multi-Source Domain Adaptation

Wright, D., & Augenstein, I. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.639 · Cited by 48 · see citing works

Learning Word Meta-Embeddings by Autoencoding

Bollegala, D., & Bao, C. (2018) · International Conference on Computational Linguistics · Cited by 42 · see citing works

Knowledge distillation for BERT unsupervised domain adaptation

Ryu, M., Lee, G., & Lee, K. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01736-y · Cited by 48 · see citing works

UDALM: Unsupervised Domain Adaptation through Language Modeling

Karouzos, C., Paraskevopoulos, G., & Potamianos, A. (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.203 · Cited by 45 · see citing works

Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal Guidance

Ziser, Y., & Reichart, R. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1022 · Cited by 40 · see citing works

Dialogue-AMR: Abstract Meaning Representation for Dialogue.

Bonial, C., Donatelli, L. E., Abrams, M., Lukin, S. M., Tratz, S., Marge, M., Artstein, R., Traum, D. R., & Voss, C. R. (2020) · Language Resources and Evaluation · Cited by 40 · see citing works

A taxonomy and review of generalization research in NLPPreprint

Hupkes, D., Giulianelli, M., Dankers, V., Artetxe, M., Elazar, Y., Pimentel, T., Christodoulopoulos, C., Lasri, K., Saphra, N., Sinclair, A., et al. (2022) · arXiv (Cornell University) · doi:10.3929/ethz-b-000638992 · Cited by 41 · see citing works

Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation

Ziser, Y., & Reichart, R. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1591 · Cited by 36 · see citing works

Wasserstein based transfer network for cross-domain sentiment classification

Du, Y., He, M., Wang, L., & Zhang, H. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106162 · Cited by 36 · see citing works

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 31 · see citing works

Domain Divergences: A Survey and Empirical Analysis

Kashyap, A. R., Hazarika, D., Kan, M., & Zimmermann, R. (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.147 · Cited by 33 · see citing works

PADA: A Prompt-based Autoregressive Approach for Adaptation to Unseen Domains.Preprint

Ben‐David, E., Oved, N., & Reichart, R. (2021) · arXiv (Cornell University) · Cited by 30 · see citing works

Graph Domain Adversarial Transfer Network for Cross-Domain Sentiment Classification

Tang, H., Mi, Y., Xue, F., & Cao, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3061139 · Cited by 29 · see citing works

DILBERT: Customized Pre-Training for Domain Adaptation with Category Shift, with an Application to Aspect Extraction

Lekhtman, E., Ziser, Y., & Reichart, R. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.20 · Cited by 30 · see citing works

Transfer Learning: Domain Adaptation

Kamath, U., Liu, J., & Whitaker, J. (2019) · Springer eBooks · doi:10.1007/978-3-030-14596-5_11 · Cited by 27 · see citing works

DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation

Calderon, N., Ben-David, E., Feder, A., & Reichart, R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.533 · Cited by 27 · see citing works

Domain adaptation for bias mitigation in affective computing: use cases for facial emotion recognition and sentiment analysis systems

Singhal, P., Gokhale, S., Shah, A., Jain, D. K., Walambe, R. A., Ekárt, A., & Kotecha, K. V. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-06659-1 · Cited by 26 · see citing works

Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse DomainsPreprint

Barnes, J., Klinger, R., & Walde, S. S. I. (2018) · arXiv (Cornell University) · Cited by 22 · see citing works

Simultaneous learning of pivots and representations for cross-domain sentiment classification

Li, L., Ye, W., Long, M., Tang, Y., Xu, J., & Wang, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6336 · Cited by 18 · see citing works

Cross-domain sentiment classification based on key pivot and non-pivot extraction

Fu, Y., & Liu, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107280 · Cited by 18 · see citing works

Social Media Cross-Source and Cross-Domain Sentiment Classification

Zola, P., Cortez, P. C., Ragno, C., & Brentari, E. (2019) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622019500305 · Cited by 17 · see citing works

The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention

Ophir, Y., Tikochinski, R., Klomek, A. B., & Reichart, R. (2021) · Clinical Psychological Science · doi:10.1177/21677026211022013 · Cited by 14 · see citing works

Unsupervised Domain Adaptation in Cross-corpora Abusive Language Detection

Bose, T., Illina, I., & Fohr, D. (2021) · International Workshop on Natural Language Processing for Social Media (SocialNLP) · doi:10.18653/v1/2021.socialnlp-1.10 · Cited by 14 · see citing works

Domain Confused Contrastive Learning for Unsupervised Domain Adaptation

Long, Q., Luo, T., Wang, W., & Pan, S. J. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.217 · Cited by 15 · see citing works

Improving sentiment domain adaptation for Arabic using an unsupervised self-labeling framework

Alqahtani, Y., Al-Twairesh, N., & Alsanad, A. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103338 · Cited by 13 · see citing works

Embedding Projection for Targeted Cross-lingual Sentiment: Model Comparisons and a Real-World Study

Barnes, J., & Klinger, R. (2019) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11561 · Cited by 13 · see citing works

Capsule Network With Identifying Transferable Knowledge for Cross-Domain Sentiment Classification

Yin, H., Liu, P., Zhu, Z., Li, W., & Wang, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2948628 · Cited by 11 · see citing works

Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse DomainsPreprint

Barnes, J., Klinger, R., & Walde, S. S. I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.04381 · Cited by 12 · see citing works

Lifelong Machine Learning

Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.016 · Cited by 10 · see citing works

Benchmarking Scalable Predictive Uncertainty in Text Classification

Landeghem, J. V., Blaschko, M. B., Anckaert, B., & Moens, M. (2022) · IEEE Access · doi:10.1109/access.2022.3168734 · Cited by 13 · see citing works

Anchored Model Transfer and Soft Instance Transfer for Cross-Task Cross-Domain Learning: A Study Through Aspect-Level Sentiment Classification

Zheng, Y., Zhang, R., Wang, S., Mensah, S., & Mao, Y. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380034 · Cited by 10 · see citing works

Adversarial Transfer Learning.Preprint

Wilson, G., & Cook, D. J. (2018) · arXiv (Cornell University) · Cited by 10 · see citing works

Model Compression for Domain Adaptation through Causal Effect Estimation

Rotman, G., Feder, A., & Reichart, R. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00431 · Cited by 9 · see citing works

SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing

Laparra, E., Su, X., Zhao, Y., Uzuner, Ö., Miller, T. A., & Bethard, S. J. (2021) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2021.semeval-1.42 · Cited by 9 · see citing works

Stance Detection with a Multi-Target Adversarial Attention Network

Sun, Q., Xi, X., Sun, J., Wang, Z., & Xu, H. (2022) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3544490 · Cited by 9 · see citing works

A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification

Alqahtani, Y., Al-Twairesh, N., & Alsanad, A. (2023) · Applied Sciences · doi:10.3390/app13031387 · Cited by 8 · see citing works

TSP: Learning Task-Specific Pivots for Unsupervised Domain Adaptation

Cui, X., Coenen, F., & Bollegala, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71246-8_46 · Cited by 7 · see citing works

WIND: Weighting Instances Differentially for Model-Agnostic Domain Adaptation

Chen, X., Cao, Y., & Wan, X. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.209 · Cited by 7 · see citing works

X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing

Liu, Z., Winata, G. I., Xu, P., & Fung, P. (2021) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/2021.repl4nlp-1.13 · Cited by 7 · see citing works

Soft prompt enhanced joint learning for cross-domain aspect-based sentiment analysis

Shi, J., Li, W., Bai, Q., Yang, Y., & Jiang, J. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200292 · Cited by 7 · see citing works

Machine Reading of Historical Events

Honovich, O., Hennigen, L. T., Abend, O., & Cohen, S. B. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.668 · Cited by 6 · see citing works

A Comparison of Strategies for Source-Free Domain Adaptation

Su, X., Zhao, Y., & Bethard, S. J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.572 · Cited by 6 · see citing works

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 · see citing works

Rant or rave: variation over time in the language of online reviews

Ziser, Y., Webber, B., & Cohen, S. B. (2023) · Language Resources and Evaluation · doi:10.1007/s10579-023-09652-5 · Cited by 5 · see citing works

Balance-Subsampled Stable Prediction Across Unknown Test Data

Kuang, K., Zhang, H., Wu, R., Wu, F. Q., Zhuang, Y., & Zhang, A. (2021) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3477052 · Cited by 5 · see citing works

Towards Explainable Fact CheckingPreprint

Augenstein, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.10274 · Cited by 13 · see citing works

Big Data Based Transfer Learning for Sentiment Classification with Multiple Source Domains

Li, Q., Wang, G., & Yang, G. (2022) · doi:10.2991/978-94-6463-034-3_26 · Cited by 4 · see citing works

Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases

T.y.s.s, S., Ichim, O., & Grabmair, M. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.44 · Cited by 10 · see citing works

Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation

Mohiuddin, T., Nguyen, T.-T., & Joty, S. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1134 · Cited by 4 · see citing works

Multi-Source Domain Adaptation with Mixture of ExpertsPreprint

Guo, J., Shah, D., & Barzilay, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.02256 · Cited by 4 · see citing works

Understanding Domain Learning in Language Models Through Subpopulation Analysis

Zhao, Z., Ziser, Y., & Cohen, S. B. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.16 · Cited by 3 · see citing works

A Systematic Literature Review on Cross Domain Sentiment Analysis Techniques: PRISMA Approach

SHARMA, R. R., & Lakhwani, K. (2024) · Annals of Emerging Technologies in Computing · doi:10.33166/aetic.2024.04.002 · Cited by 3 · see citing works

HRSGD: High-order recurrent medical image fusion via self-supervised generative distillation

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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 · see citing works

Domain Adaptation for Trauma Mortality Prediction in EHRs with Feature Disparity

Zhang, X., Li, S., Cheng, Z., Callcut, R. A., & Petzold, L. R. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669798 · Cited by 3 · see citing works

A multi-level weighted transformation based neuro-fuzzy domain adaptation technique using stacked auto-encoder for land-cover classification

Chakraborty, S., & Roy, M. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1750735 · Cited by 3 · see citing works

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 · see citing works

Structural Correspondence Learning for Cross-Domain Sentiment Analysis in Brazilian Portuguese

Britto, L. F. S., Lima, R. J., & Pacífico, L. D. S. (2019) · Brazilian Conference on Intelligent Systems · doi:10.1109/bracis.2019.00145 · Cited by 3 · see citing works

Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis

Ouchi, H., Suzuki, J., & Inui, K. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1379 · Cited by 3 · see citing works

Cross-Domain Sentiment Classification with In-Domain Contrastive LearningPreprint

Li, T., Chen, X., Zhang, S., Dong, Z. X., & Keutzer, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.02943 · Cited by 4 · see citing works

Geração Automática de Receitas Culinárias para Pessoas com Restrições Alimentares

Ludermir, T. B., Pacífico, L. D. S., & Britto, L. F. S. (2020) · Revista Eletrônica de Iniciação Científica em Computação · doi:10.5753/reic.2020.1749 · Cited by 2 · see citing works

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 · see citing works

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 · see citing works

Dual-Channel Domain Adaptation Model

Yang, C., Zhou, B., Hu, X., Chen, J., Cai, Q., & Xue, Y. (2021) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3498851.3498984 · Cited by 2 · see citing works

Deep Learning for Medical Prediction in Electronic Health Records

Zhang, X. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i13.26933 · Cited by 2 · see citing works

A Robust Information-Masking Approach for Domain Counterfactual Generation

Hong, P., Bhardwaj, R., Majumder, N., Aditya, S., & Poria, S. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.231 · Cited by 2 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

DILBERT: Customized Pre-Training for Domain Adaptation withCategory Shift, with an Application to Aspect ExtractionPreprint

Lekhtman, E., Ziser, Y., & Reichart, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.00571 · Cited by 2 · see citing works

Cross-Lingual Sentiment Prediction with Various Classifiers for Structural Correspondence Learning and Synonym Augmentation

Meghana, D. K., Kiran, K., Harshala, R., Shenoy, P. D., & Venugopal, K. R. (2024) · IEEE International Conference for Women in Computing (InCoWoCo) · doi:10.1109/incowoco64194.2024.10863776 · Cited by 1 · see citing works

The $$\ell _{2,1}$$-Norm Stacked Robust Autoencoders via Adaptation Regularization for Domain Adaptation

Yang, S., Zhang, Y., Zhu, Y. Q., Ding, H., & Hu, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-24265-7_9 · Cited by 1 · see citing works

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 · see citing works

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 · see citing works

Unsupervised Domain Adaptation for Entity Blocking Leveraging Large Language Models

Wang, Y., & Yan, M. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825234 · Cited by 1 · see citing works

Marginalized Stacked Denoising Autoencoder With Adaptive Noise Probability for Cross Domain Classification

Zhang, Y., Yang, S., Li, P., Hu, X., & Wang, H. (2019) · IEEE Access · doi:10.1109/access.2019.2925811 · Cited by 1 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Location Classification Based on Tweets

Kravi, E., Kanza, Y., Kimelfeld, B., & Reichart, R. (2021) · ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI) · doi:10.1145/3486635.3491075 · Cited by 1 · see citing works

Pivot Based Language Modeling for Improved Neural Domain Adaptation

Ziser, Y., & Reichart, R. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1112 · Cited by 139 · see citing works

A New Method of Selecting Pivot Features for Structural Correspondence Learning in Domain Adaptive Sentiment Analysis

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Neural structural correspondence learning method based on features extending

Runda, Y., Junge, L., Yan, X., Ying, X., & Li, Z. (2020) · International Conference on Artificial Intelligence and Information Systems (ICAIIS) · doi:10.1109/icaiis49377.2020.9194870 · Cited by 1 · see citing works

Domain adaptation with structural correspondence learning

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Unsupervised Domain Adaptation with Feature EmbeddingsPreprint

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