ITNLP2022 at SemEval-2022 Task 8: Pre-trained Model with Data Augmentation and Voting for Multilingual News Similarity
Zhongan Chen | Weiwei Chen | YunLong Sun | Hongqing Xu | Shuzhe Zhou | Bohan Chen | Chengjie Sun | Yuanchao Liu
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
This article introduces a system to solve the SemEval 2022 Task 8: Multilingual News Article Similarity. The task focuses on the consistency of events reported in two news articles. The system consists of a pre-trained model(e.g., INFOXLM and XLM-RoBERTa) to extract multilingual news features, following fully-connected networks to measure the similarity. In addition, data augmentation and Ten Fold Voting are used to enhance the model. Our final submitted model is an ensemble of three base models, with a Pearson value of 0.784 on the test dataset.
- Zhongan Chen 1
- Weiwei Chen 1
- YunLong Sun 1
- Shuzhe Zhou 1
- Bohan Chen 1
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