Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis
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Shrey Desai, Barea Sinno, Alex Rosenfeld, Junyi Jessy Li. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
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The 1,749 articles are from political news articles of Desai et al. (2019) from COHA covering years 1922-1986.
“Articles in this corpus are from political news articles of Desai et al. (2019) from the Corpus of Historical American English (COHA, Davies (2012)) covering years 1922-1986.”
From Political Ideology and Polarization: A Multi-dimensional Approach (2022) · cites this paper as Desai et al., 2019
Section 5 (Datasets; page unavailable) identifies COHA news articles spanning 1922–1986 as the target data, and Section 7.2 describes a political subcorpus extracted from COHA. The paper does not identify a set of 1,749 articles; its reported dataset counts differ. It supports the articles’ stated origin and date range, but not the attributed count. (LLM, conf=0.87)
Experimental — accuracy not yet measuredWe start with the diachronic corpus of political news articles of Desai et al. (2019) which covers years 1922-1986, the longest-spanning dataset to our knowledge.
“We start with the diachronic corpus of political news articles of Desai et al. (2019) which covers years 1922-1986, the longest-spanning dataset to our knowledge.”
From Political Ideology and Polarization: A Multi-dimensional Approach (2022) · cites this paper as Desai et al., 2019
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Cites
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1× · see citing works
“To ease the difficulty of domain adaptation, we exploit the temporal information in our datasets to introduce a curriculum (Bengio et al., 2009).”
MethodNeural Machine Translation in Linear Time
Kalchbrenner, N., Espeholt, L., Simonyan, K., Oord, A. V. D., Graves, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1610.10099 · Cited by 414 · cited 3× · see citing works
“…classification generally use 2D convolutions over the input matrix (Kim, 2014), but architectures us-ing 1D convolutions have also been explored in other contexts, e.g., sequence modeling (Bai et al., 2018), machine translation (Kalchbrenner et al., 2016), and text generation (Yang et al., 2017).”
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Research works printed but not linked: 36
- Martin Arjovsky and Léon Bottou. 2017. Towards principled methods for training generative adversar- ial networks. In International Conference on Learn- ing Representations.
- Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson. 2019. There are many con- sistent explanations of unlabeled data: Why you should average. In International Conference on Learning Representations.
- Shaojie Bai, J Zico Kolter, and Vladlen Koltun. 2018. An empirical evaluation of generic convolu- tional and recurrent networks for sequence model- ing. arXiv preprint arXiv:1803.01271. · arXiv
- Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. 2019. Trellis networks for sequence modeling. In Interna- tional Conference on Learning Representations.
- Delia Baldassarri and Andrew Gelman. 2008. Partisans without constraint: Political polarization and trends in american public opinion. American Journal of Sociology, 114(2):408-446.
- Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, and James Glass. 2019. Iden- tifying and controlling important neurons in neural machine translation. In International Conference on Learning Representations.
- Matthew A Baum and Philip BK Potter. 2008. The re- lationships between mass media, public opinion, and foreign policy: Toward a theoretical synthesis. An- nual Review of Political Science, 11:39-65.
- David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of Ma- chine Learning Research, 3(Jan):993-1022.
- Angus Campbell, Philip E Converse, Warren E Miller, and Donald E Stokes. 1980. The American Voter. University of Chicago Press.
- Minmin Chen, Zhixiang Xu, Kilian Q. Weinberger, and Fei Sha. 2012. Marginalized denoising au- toencoders for domain adaptation. In Proceed- ings of the 29th International Conference on Inter- national Conference on Machine Learning, pages 1627-1634.
- Xilun Chen, Yu Sun, Ben Athiwaratkun, Claire Cardie, and Kilian Weinberger. 2018. Adversarial deep av- eraging networks for cross-lingual sentiment classi- fication. Transactions of the Association for Com- putational Linguistics, 6:557-570.
- Jacob Cohen. 1960. A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1):37-46.
- Geoff French, Michal Mackiewicz, and Mark Fisher. 2018. Self-ensembling for visual domain adapta- tion. In International Conference on Learning Rep- resentations.
- Lisheng Fu, Thien Huu Nguyen, Bonan Min, and Ralph Grishman. 2017. Domain adaptation for re- lation extraction with domain adversarial neural net- work. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 425-429.
- Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Franc ¸ois Lavi- olette, Mario Marchand, and Victor Lempitsky. 2016. Domain-adversarial training of neural net- works. The Journal of Machine Learning Research, 17(1):2096-2030.
- Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Domain adaptation for large-scale sentiment classification: A deep learning approach. In Pro- ceedings of the 28th International Conference on Machine Learning, pages 513-520.
- Robert E Goodin. 2009. The Oxford handbook of po- litical science, volume 11. Oxford University Press.
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735-1780.
- Xiaolei Huang and Michael J Paul. 2018. Examining temporality in document classification. In Proceed- ings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Pa- pers), pages 694-699.
- Yoon Kim. 2014. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Lan- guage Processing, pages 1746-1751.
- Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In International Conference for Learning Representations.
- Wei-Jen Ko, Greg Durrett, and Junyi Jessy Li. 2019. Domain agnostic real-valued specificity prediction. In Proceedings of the AAAI Conference on Artificial Intelligence.
- Samuli Laine and Timo Aila. 2017. Temporal ensem- bling for semi-supervised learning. In International Conference for Learning Representations.
- Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Confer- ence on Computer Vision and Pattern Recognition, pages 3431-3440.
- Scott de Marchi, Spencer Dorsey, and Michael J Ens- ley. 2018. Policy and the structure of roll call voting in the us house. Available at SSRN 3262316.
- Maxwell McCombs. 2018. Setting the agenda: Mass media and public opinion. John Wiley & Sons.
- Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 Con- ference on Empirical Methods in Natural Language Processing, pages 1532-1543.
- Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved techniques for training GANs. In Ad- vances in Neural Information Processing Systems 29, pages 2234-2242.
- Evan Sandhaus. 2008. The New York Times annotated corpus. Linguistic Data Consortium, Philadelphia, 6(12):e26752.
- Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929-1958.
- Antti Tarvainen and Harri Valpola. 2017. Mean teach- ers are better role models: Weight-averaged consis- tency targets improve semi-supervised deep learning results. In Advances in Neural Information Process- ing Systems 30, pages 1195-1204.
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Pro- cessing Systems 30, pages 5998-6008.
- Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, and Taylor Berg-Kirkpatrick. 2017. Improved varia- tional autoencoders for text modeling using dilated convolutions. In Proceedings of the 34th Inter- national Conference on Machine Learning, pages 3881-3890.
- Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchi- cal attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies, pages 1480-1489.
- John R Zaller et al. 1992. The nature and origins of mass opinion. Cambridge University Press.
- Yuan Zhang, Regina Barzilay, and Tommi Jaakkola. 2017. Aspect-augmented adversarial networks for domain adaptation. Transactions of the Association for Computational Linguistics, 5:515-528.
Cited by
Sorted by citations
Political Ideology and Polarization: A Multi-dimensional Approach
Sinno, B., Oviedo, B., Atwell, K., Alikhani, M., & Li, J. J. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.17 · Cited by 12 · see citing works
“Articles in this corpus are from political news articles of Desai et al. (2019) from the Corpus of Historical American English (COHA, Davies (2012)) covering years 1922-1986.”
BackgroundMethodSelf-citation
- Authors
- Desai, S., Sinno, B., Rosenfeld, A., & Li, J. J.
- Affiliations
- The University of Texas at Austin (US)
- Venue
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Published
- 2019
- Pages
- 4717-4729
- OpenAlex
- W2981841623
- Cited by
- 10
- References
- 2
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 3 · 2021: 2 · 2020: 3
- FWCI
- 0.77
- 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)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1850153)
- University of Texas at Austin
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- unsupervised domain adaptation, cross-domain transfer learning
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