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Few-shot cross-lingual stance detection with sentiment-based pre-training. arXiv preprint arXiv:2109.06050 (2021)."},{"key":"e_1_3_2_2_10_1","unstructured":"Tom\u00e1s Hercig Peter Krejzl Barbora Hourov\u00e1 Josef Steinberger and Ladislav Lenc. 2017. Detecting Stance in Czech News Commentaries.. In ITAT. 176--180. Tom\u00e1s Hercig Peter Krejzl Barbora Hourov\u00e1 Josef Steinberger and Ladislav Lenc. 2017. Detecting Stance in Czech News Commentaries.. In ITAT. 176--180."},{"key":"e_1_3_2_2_11_1","volume-title":"Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton , Oriol Vinyals , and Jeff Dean . 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 ( 2015 ). Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. 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