Abstract
This paper introduces the result of Team Grenzlinie’s experiment in CONSTRAINT 2021 shared task. This task has two subtasks. Subtask1 is the COVID-19 Fake News Detection task in English, a binary classification task. This paper chooses RoBERTa as the pre-trained model, and tries to build a graph from news datasets. Finally, our system achieves an accuracy of 98.64% and an F1-score of 98.64% on the test dataset. Subtask2 is a Hostile Post Detection task in Hindi, a multi-labels task. In this task, XLM-RoBERTa is chosen as the pre-trained model. The adapted threshold is adopted to solve the data unbalanced problem, and then Bi-LSTM, LEAM, LaSO approaches are adopted to obtain more abundant semantic information. The final approach achieves the accuracy of 74.11% and weight F1-score of 81.77% on the test dataset.
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Liu, R., Zhou, X. (2021). Extracting Latent Information from Datasets in CONSTRAINT 2021 Shared Task. In: Chakraborty, T., Shu, K., Bernard, H.R., Liu, H., Akhtar, M.S. (eds) Combating Online Hostile Posts in Regional Languages during Emergency Situation. CONSTRAINT 2021. Communications in Computer and Information Science, vol 1402. Springer, Cham. https://doi.org/10.1007/978-3-030-73696-5_7
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