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2022 SemEval SemEval 2022

YNU-HPCC at SemEval-2022 Task 8: Transformer-based Ensemble Model for Multilingual News Article Similarity

Abstract

AbstractThis paper describes the system submitted by our team (YNU-HPCC) to SemEval-2022 Task 8: Multilingual news article similarity. This task requires participants to develop a system which could evaluate the similarity between multilingual news article pairs. We propose an approach that relies on Transformers to compute the similarity between pairs of news. We tried different models namely BERT, ALBERT, ELECTRA, RoBERTa, M-BERT and Compared their results. At last, we chose M-BERT as our System, which has achieved the best Pearson Correlation Coefficient score of 0.738.

Authors

Zihan Nai, Jin Wang, Xuejie Zhang

Topics

Artificial Intelligence > Core AI > Multimodal Learning Machine Learning > Core Methods > Regression Machine Learning > Core Methods > Embedding Learning Machine Learning > Optimization & Theory > Statistical Learning

Keywords

semantic similarity pearson correlation multilingual bert text similarity similarity prediction transformer model multilingual similarity pearson correlation coefficient
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