Diverse few-shot text classification with multiple metrics

NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference

Volumn 1, Issue , 2018, Pages 1206-1215

  Yu, Mo ^a   Guo, Xiaoxiao ^a   Yi, Jinfeng ^a   Chang, Shiyu ^a   Potdar, Saloni ^a   Cheng, Yu ^a   Tesauro, Gerald ^a   Wang, Haoyu ^a   Zhou, Bowen ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; LEARNING ALGORITHMS; SENTIMENT ANALYSIS;

ADAPTIVE METRIC; CLASSIFICATION DATASETS; META-TRAINING; NATURAL LANGUAGES; PREDICTIVE ACCURACY; QUANTITATIVE EVALUATION; STATE OF THE ART; TEXT CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

EID: 85064455066     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Aviad Barzilai and Koby Crammer. 2015. Convex multi-Task learning by clustering. In AISTATS.
2. John Blitzer, Mark Dredze, and Fernando Pereira. 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In ACL, volume 7, pages 440-447.
3. Emmanuel J Candes and Terence Tao. 2010. The power of convex relaxation: Near-optimal matrix completion. IEEE Transactions on Information Theory, 56(5):2053-2080.
4. Venkat Chandrasekaran, Sujay Sanghavi, Pablo A Parrilo, and Alan S Willsky. 2011. Rank-sparsity incoherence for matrix decomposition. SIAM Journal on Optimization, 21(2):572-596.
5. Yu Cheng, Mo Yu, Xiaoxiao Guo, and Bowen Zhou. 2017. Few-shot learning with meta metric learners. In NIPS 2017 Workshop on Meta-Learning.
6. Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12(Aug):2493-2537.
7. Koby Crammer and Yishay Mansour. 2012. Learning multiple tasks using shared hypotheses. In Advances in Neural Information Processing Systems, pages 1475-1483.
8. Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-Agnostic meta-learning for fast adaptation of deep networks. arXiv preprint arXiv:1703.03400.
9. Rie Johnson and Tong Zhang. 2016. Supervised and semi-supervised text categorization using one-hot lstm for region embeddings. stat, 1050:7.
10. Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2016. Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759.
11. Zhuoliang Kang, Kristen Grauman, and Fei Sha. 2011. Learning with whom to share in multi-Task feature learning. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pages 521-528.
12. Yoon Kim. 2014. Convolutional neural networks for sentence classification. In EMNLP, pages 1746-1751, Doha, Qatar. Association for Computational Linguistics.
13. Gregory Koch. 2015. Siamese neural networks for oneshot image recognition. Ph.D.Thesis, University of Toronto.
14. Abhishek Kumar and Hal Daume III. 2012. Learning task grouping and overlap in multi-Task learning. In Proceedings of the 29th International Conference on Machine Learning (ICML-12).
15. Brenden M Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B Tenenbaum. 2011. One shot learning of simple visual concepts. In CogSci, volume 172, page 2.
16. Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum. 2015. Human-level concept learning through probabilistic program induction. Science, 350(6266):1332-1338.
17. Fei-Fei Li, Rob Fergus, and Pietro Perona. 2006. Oneshot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4):594-611.
18. Erik G Miller, Nicholas E Matsakis, and Paul A Viola. 2000. Learning from one example through shared densities on transforms. In Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on, volume 1, pages 464-471. IEEE.
19. Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel. 2017. A simple neural attentive metalearner. In NIPS 2017 Workshop on Meta-Learning.
20. Tsendsuren Munkhdalai and Hong Yu. 2017. Meta networks. arXiv preprint arXiv:1703.00837.
21. Keerthiram Murugesan, Jaime Carbonell, and Yiming Yang. 2017. Co-clustering for multitask learning. arXiv preprint arXiv:1703.00994.
22. Andrew Y Ng, Michael I Jordan, and YairWeiss. 2002. On spectral clustering: Analysis and an algorithm. In Advances in neural information processing systems, pages 849-856.
23. Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014. Glove: Global vectors for word representation. In EMNLP, volume 14, pages 1532-1543.
24. Sachin Ravi and Hugo Larochelle. 2017. Optimization as a model for few-shot learning. In International Conference on Learning Representations, volume 1, page 6.
25. Jake Snell, Kevin Swersky, and Richard S Zemel. 2017. Prototypical networks for few-shot learning. arXiv preprint arXiv:1703.05175.
26. Eleni Triantafillou, Richard Zemel, and Raquel Urtasun. 2017. Few-shot learning through an information retrieval lens. In Advances in Neural Information Processing Systems, pages 2252-2262.
27. Oriol Vinyals, Charles Blundell, Tim Lillicrap, Daan Wierstra, et al. 2016. Matching networks for one shot learning. In Advances in Neural Information Processing Systems, pages 3630-3638.
28. Yu-Xiong Wang, Deva Ramanan, and Martial Hebert. 2017. Learning to model the tail. In Advances in Neural Information Processing Systems 30, pages 7032-7042.