The DOI matches its Crossref record; 38 of the 41 references it cites were found in a registry or an index.
Multi-Source Domain Adaptation with Mixture of Experts
Open accessAbstract
We propose a mixture-of-experts approach for unsupervised domain adaptation from multiple sources.The key idea is to explicitly capture the relationship between a target example and different source domains.This relationship, expressed by a point-to-set metric, determines how to combine predictors trained on various domains.The metric is learned in an unsupervised fashion using metatraining.Experimental results on sentiment analysis and part-of-speech tagging demonstrate that our approach consistently outperforms multiple baselines and can robustly handle negative transfer. 1
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
“In addition, we find that training on more diverse data with more product categories tends to improve generalization to unseen categories and reduce the effect of the distribution shift; similar phenomena have also been reported in prior work (Mansour et al., 2009; Guo et al., 2018).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Guo et al., 2018
Citations
Cites
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831
Domain-Adversarial Training of Neural Networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636
Prototypical Networks for Few-shot LearningPreprint
Snell, J., Swersky, K., & Zemel, R. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05175 · Cited by 6,245
Adaptive Mixtures of Local Experts
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991) · Neural Computation · doi:10.1162/neco.1991.3.1.79 · Cited by 5,384
Distance Metric Learning for Large Margin Nearest Neighbor Classification
Weinberger, K. Q., & Saul, L. K. (2009) · Journal of Machine Learning Research · doi:10.5555/1577069.1577078 · Cited by 4,652
(2012) · MPG.PuRe (Max Planck Society) · doi:10.5555/2188385.2188410 · Cited by 3,603
Deep domain confusion Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2014) · arXiv · doi:10.48550/arxiv.1412.3474 · Cited by 2,285
Analysis of Representations for Domain Adaptation
Ben-David, S., Blitzer, J., Crammer, K., & Pereira, F. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0022 · Cited by 2,019
(2007) · Cited by 2,018
Distance Metric Learning for Large Margin Nearest Neighbor Classification
Weinberger, K. Q., Blitzer, J. C., & Saul, L. K. (2005) · Cited by 1,693
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach
Glorot, X., Bordes, A., & Bengio, Y. (2011) · Cited by 1,569
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
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., & Erhan, D. (2016) · arXiv · doi:10.48550/arxiv.1608.06019 · Cited by 1,208
A Universal Part-of-Speech Tagset
Petrov, S., Das, D., & McDonald, R. (2012) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3pjyez8kmkhz · Cited by 934
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., & Dean, J. (2017) · arXiv · doi:10.48550/arxiv.1701.06538 · Cited by 933
Wasserstein Distance Guided Representation Learning for Domain Adaptation
Shen, J. G., Qu, Y., Zhang, W., & Yu, Y. H. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11784 · Cited by 895
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
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 772
Cross-domain video concept detection using adaptive svms
Yang, J. J., Yan, R., & Hauptmann, A. G. (2007) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1291233.1291276 · Cited by 673
Jacobs, R. A., Jordan, M. I., & Barto, A. G. (1991) · Cognitive Science · doi:10.1207/s15516709cog1502_2 · Cited by 514
(2008) · Cited by 436
To transfer or not to transfer
Rosenstein, M. T. (2005) · Cited by 429
Domain adaptation from multiple sources via auxiliary classifiers
Duan, L., Tsang, I. W.-H., Xu, D., & Chua, T. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553411 · Cited by 299
Learning from Multiple Sources
Crammer, K., Kearns, M. J., & Wortman, J. R. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0045 · Cited by 274
Diverse Few-Shot Text Classification with Multiple Metrics
Yu, M., Guo, X., Yi, J., Chang, S., Potdar, S., Cheng, Y., Tesauro, G. J., Wang, H., & Zhou, B. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1109 · Cited by 241
Overview of the 2012 Shared Task on Parsing the Web
Petrov, S., & McDonald, R. (2012) · Cited by 158
Multi-domain sentiment classification
Li, S., & Zong, C. (2008) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1557690.1557765 · Cited by 130
Multinomial Adversarial Networks for Multi-Domain Text Classification
Chen, X., & Cardie, C. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1111 · Cited by 122
Neural Structural Correspondence Learning for Domain Adaptation
Ziser, Y., & Reichart, R. (2017) · Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) · doi:10.18653/v1/k17-1040 · Cited by 122
Transfer learning from multiple source domains via consensus regularization
Luo, P., Zhuang, F., Xiong, H., Xiong, Y., & He, Q. (2008) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1458082.1458099 · Cited by 117
Aspect-augmented Adversarial Networks for Domain Adaptation
Zhang, Y., Barzilay, R., & Jaakkola, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00077 · Cited by 94
Self-citationJacobs, R. A., Jordan, M. I., & Barto, A. (1991) · Cognitive Science · doi:10.1016/0364-0213(91)80006-q · Cited by 75
Parsing Tweets into Universal Dependencies
Liu, Y., Zhu, Y., Che, W., Qin, B., Schneider, N., & Smith, N. A. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1088 · Cited by 74
Domain Attention with an Ensemble of Experts
Kim, Y., Stratos, K., & Kim, D. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1060 · Cited by 61
Multiple Source Domain Adaptation with Adversarial Learning
Zhao, H., Zhang, S., Wu, G., Costeira, J. P., Moura, J. M. F., & Gordon, G. J. (2018) · International Conference on Learning Representations · Cited by 58
Learning from Multiple Sources
Nunes, L. M., & Oliveira, E. (2004) · doi:10.1109/aamas.2004.159 · Cited by 31
Cross-domain Text Classification with Multiple Domains and Disparate Label Sets
Bhatt, H. S., Sinha, M., & Roy, S. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1155 · Cited by 16
Cited by
Sorted by citations
A survey of unsupervised deep domain adaptation
Wilson, G., & Cook, D. J. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3400066 · Cited by 987
Zhang, W., Deng, L., Zhang, L., & Wu, D. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.106004 · Cited by 503
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518
BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains
Labrak, Y., Bazoge, A., Morin, E., Gourraud, P.-A., Rouvier, M., & Dufour, R. (2024) · Findings of the Association for Computational Linguistics ACL 2024 · doi:10.18653/v1/2024.findings-acl.348 · Cited by 269
Zero-Shot Entity Linking by Reading Entity Descriptions
Logeswaran, L., Chang, M.-W., Lee, K., Toutanova, K., Devlin, J., & Lee, H. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1335 · Cited by 275
Metadata
- Authors
- Guo, J., Shah, D., & Barzilay, R.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 4694-4703
- OpenAlex
- W2891691791
- Cited by
- 168
- References
- 41
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 19 · 2023: 24 · 2022: 29 · 2021: 37 · 2020: 27 · 2019: 17
- FWCI
- 8.27
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- multi-source domain adaptation, mixture of experts, unsupervised domain adaptation, negative transfer, sentiment analysis, part-of-speech tagging
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
