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This paper describes our system designed for SemEval-2022 Task 8:Multilingual News Article Similarity. We proposed a linguistics-inspired modeltrained with a few task-specific strategies. The main techniques of our systemare: 1) data augmentation, 2) multi-label loss, 3) adapted R-Drop, 4) samplesreconstruction with the head-tail combination. We also present a brief analysisof some negative methods like two-tower architecture. Our system ranked 1st onthe leaderboard while achieving a Pearson's Correlation Coefficient of 0.818 onthe official evaluation set.

HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity