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

2022-04-11 03:08:37
Zihang Xu, Ziqing Yang, Yiming Cui, Zhigang Chen
arXiv_CL
       
Abstract

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

Abstract (translated)

URL

https://arxiv.org/abs/2204.04844

PDF

https://arxiv.org/pdf/2204.04844.pdf

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