HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 18 references that are in the map. First posted on arXiv 11 Apr 2022.
Builds on
7 of 18 references.
References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2016Gaussian Error Linear Units (GELUs)cited by 3,431
- 2021R-Drop: Regularized Dropout for Neural Networkscited by 202
- 2020ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corporacited by 63
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.