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        <title>NLP_STR_teamS at SemEval-2024 Task1: Semantic Textual Relatedness based on MASK Prediction and BERT Model</title>
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    <abstract>This paper describes our participation in the SemEval-2024 Task 1, “Semantic Textual Relatedness for African and Asian Languages.” This task detects the degree of semantic relatedness between pairs of sentences. Our approach is to take out the sentence pairs of each instance to construct a new sentence as the prompt template, use MASK to predict the correlation between the two sentences, use the BERT pre-training model to process and calculate the text sequence, and use the synonym replacement method in text data augmentation to expand the size of the data set. We participate in English in track A, which uses a supervised approach, and the Spearman Correlation on the test set is 0.809.</abstract>
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%0 Conference Proceedings
%T NLP_STR_teamS at SemEval-2024 Task1: Semantic Textual Relatedness based on MASK Prediction and BERT Model
%A Su, Lianshuang
%A Zhou, Xiaobing
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F su-zhou-2024-nlp
%X This paper describes our participation in the SemEval-2024 Task 1, “Semantic Textual Relatedness for African and Asian Languages.” This task detects the degree of semantic relatedness between pairs of sentences. Our approach is to take out the sentence pairs of each instance to construct a new sentence as the prompt template, use MASK to predict the correlation between the two sentences, use the BERT pre-training model to process and calculate the text sequence, and use the synonym replacement method in text data augmentation to expand the size of the data set. We participate in English in track A, which uses a supervised approach, and the Spearman Correlation on the test set is 0.809.
%U https://aclanthology.org/2024.semeval-1.51
%P 337-341

Markdown (Informal)

[NLP_STR_teamS at SemEval-2024 Task1: Semantic Textual Relatedness based on MASK Prediction and BERT Model](https://aclanthology.org/2024.semeval-1.51) (Su & Zhou, SemEval 2024)

• NLP_STR_teamS at SemEval-2024 Task1: Semantic Textual Relatedness based on MASK Prediction and BERT Model (Su & Zhou, SemEval 2024)

ACL

• Lianshuang Su and Xiaobing Zhou. 2024. NLP_STR_teamS at SemEval-2024 Task1: Semantic Textual Relatedness based on MASK Prediction and BERT Model. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 337–341, Mexico City, Mexico. Association for Computational Linguistics.