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.- Anthology ID:
- 2024.semeval-1.13
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 76–81
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.13
- DOI:
- Cite (ACL):
- Nguyen Tuan Kiet and Dang Van Thin. 2024. NRK at SemEval-2024 Task 1: Semantic Textual Relatedness through Domain Adaptation and Ensemble Learning on BERT-based models. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 76–81, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- NRK at SemEval-2024 Task 1: Semantic Textual Relatedness through Domain Adaptation and Ensemble Learning on BERT-based models (Kiet & Thin, SemEval 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.semeval-1.13.pdf