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

Abstract
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.
Anthology ID:
2022.semeval-1.167
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1184–1189
Language:
URL:
https://preview.aclanthology.org/icon-24-ingestion/2022.semeval-1.167/
DOI:
10.18653/v1/2022.semeval-1.167
Bibkey:
Cite (ACL):
Zhongan Chen, Weiwei Chen, YunLong Sun, Hongqing Xu, Shuzhe Zhou, Bohan Chen, Chengjie Sun, and Yuanchao Liu. 2022. ITNLP2022 at SemEval-2022 Task 8: Pre-trained Model with Data Augmentation and Voting for Multilingual News Similarity. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1184–1189, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
ITNLP2022 at SemEval-2022 Task 8: Pre-trained Model with Data Augmentation and Voting for Multilingual News Similarity (Chen et al., SemEval 2022)
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