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Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task Akansha Gautam, Venktesh V, Sarah Masud IIIT-D Presented on 8th Feb 2021, CONSTRAINT, AAAI 2021

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Problem Statement ● ● ● ● Reference: ABC News

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Challenges ● ● ●

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Dataset ● ● ● ●

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Why topic embeddings? ● ● ●

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Methodology ● ○ ○ ○

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Experiments ● ● ○ ● xlnet-base-cased ● ○ ○ ○ ○

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Comparison with other models

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Error Analysis ●

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Error Analysis ..contd

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Conclusion ● ● ● ●

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References 1. Patwa, P., Sharma, S., PYKL, S., Guptha, V., Kumari, G., Akhtar, M.S., Ekbal, A., Das, A., Chakraborty, T.: Fighting an infodemic: Covid-19 fake news dataset (2020) 2. Nguyen, D.Q., Vu, T., Nguyen, A.T.: Bertweet: A pre-trained language model for english tweets. arXiv preprint arXiv:2005.10200 (2020) 3. Li Wang, Junlin Yao, Yunzhe Tao, Li Zhong, Wei Liu, and Qiang Du. 2018. A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI) 4. Yuening Hu, Ke Zhai, Vladimir Eidelman, and Jordan Boyd-Graber. 2014. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. 5. Wenhu Chen, Evgeny Matusov, Shahram Khadivi, and Jan-Thorsten Peter. 2016. Guided Alignment Training for Topic-Aware Neural Machine Translation. In Proceedings of AMTA, pages 121–134, Austin, USA