@inproceedings{nai-etal-2022-ynu,
title = "{YNU}-{HPCC} at {S}em{E}val-2022 Task 8: Transformer-based Ensemble Model for Multilingual News Article Similarity",
author = "Nai, Zihan and
Wang, Jin and
Zhang, Xuejie",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.172",
doi = "10.18653/v1/2022.semeval-1.172",
pages = "1215--1220",
abstract = "This 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.",
}
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%0 Conference Proceedings
%T YNU-HPCC at SemEval-2022 Task 8: Transformer-based Ensemble Model for Multilingual News Article Similarity
%A Nai, Zihan
%A Wang, Jin
%A Zhang, Xuejie
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F nai-etal-2022-ynu
%X This 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.
%R 10.18653/v1/2022.semeval-1.172
%U https://aclanthology.org/2022.semeval-1.172
%U https://doi.org/10.18653/v1/2022.semeval-1.172
%P 1215-1220
Markdown (Informal)
[YNU-HPCC at SemEval-2022 Task 8: Transformer-based Ensemble Model for Multilingual News Article Similarity](https://aclanthology.org/2022.semeval-1.172) (Nai et al., SemEval 2022)
ACL