SubmissionNumber#=%=#13
FinalPaperTitle#=%=#NRK at SemEval-2024 Task 1: Semantic Textual Relatedness through Domain Adaptation and Ensemble Learning on BERT-based models
ShortPaperTitle#=%=#
NumberOfPages#=%=#6
CopyrightSigned#=%=#Nguyen Tuan Kiet
JobTitle#==#
Organization#==#University of Information Technology, Ho Chi Minh City, Vietnam
Vietnam National University, Ho Chi Minh City, Vietnam
Abstract#==#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.
Author{1}{Firstname}#=%=#Kiet Tuan
Author{1}{Lastname}#=%=#Nguyen
Author{1}{Username}#=%=#kietnt0603
Author{1}{Email}#=%=#21521042@gm.uit.edu.vn
Author{1}{Affiliation}#=%=#The VNUHCM-University of Information Technology
Author{2}{Firstname}#=%=#Dang Van
Author{2}{Lastname}#=%=#Thin
Author{2}{Username}#=%=#thindv
Author{2}{Email}#=%=#thindv@uit.edu.vn
Author{2}{Affiliation}#=%=#University of Information Technology,Vietnam National University Ho Chi Minh city

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