Fake News Detection System using XLNet model with Topic Distributions

For our Paper on Shared Task: COVID-19 Fake News Detection in English.
Presented at WS5: CONSTRAINT Workshop, AAAI 2021.

Transcript

1. 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
2. Problem Statement • • • • Reference: ABC News
3. Challenges • • •
4. Dataset • • • •
5. Why topic embeddings? • • •
6. Methodology • ◦ ◦ ◦
7. Experiments • • ◦ • xlnet-base-cased • ◦ ◦ ◦ ◦
8. Comparison with other models
9. Error Analysis •
10. Error Analysis ..contd
11. Conclusion • • • •
12. 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