NRK at SemEval-2024 Task 1: Semantic Textual Relatedness through Domain Adaptation and Ensemble Learning on BERT-based models

被引:0
|
作者
Nguyen Tuan Kiet [1 ,2 ]
Dang Van Thin [1 ,2 ]
机构
[1] Univ Informat Technol, Ho Chi Minh City, Vietnam
[2] Vietnam Natl Univ, Ho Chi Minh City, Vietnam
关键词
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中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper describes the system of the team NRK for Task A in the SemEval-2024 Task 1: Semantic Textual Relatedness (STR). We focus on exploring the performance of ensemble architectures based on the voting technique and different pre-trained transformer-based language models, including the multilingual and monolingual BERTology models. The experimental results show that our system has achieved competitive performance in some languages in Track A: Supervised, where our submissions rank in the Top 3 and Top 4 for Algerian Arabic and Amharic languages. Our source code is released on the GitHub site(1).
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页码:76 / 81
页数:6
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