BITS Pilani at SemEval-2024 Task 1: Using text-embedding-3-large and LaBSE embeddings for Semantic Textual Relatedness

Dilip Venkatesh, Sundaresan Raman


Abstract
Semantic Relatedness of a pair of text (sentences or words) is the degree to which theirmeanings are close. The Track A of the Semantic Textual Relatedness shared task aimsto find the semantic relatedness for the English language along with multiple other lowresource languages with the use of pretrainedlanguage models. We proposes a system tofind the Spearman coefficient of a textual pairusing pretrained embedding models like textembedding-3-large and LaBSE.
Anthology ID:
2024.semeval-1.124
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:
865–868
Language:
URL:
https://aclanthology.org/2024.semeval-1.124
DOI:
10.18653/v1/2024.semeval-1.124
Bibkey:
Cite (ACL):
Dilip Venkatesh and Sundaresan Raman. 2024. BITS Pilani at SemEval-2024 Task 1: Using text-embedding-3-large and LaBSE embeddings for Semantic Textual Relatedness. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 865–868, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
BITS Pilani at SemEval-2024 Task 1: Using text-embedding-3-large and LaBSE embeddings for Semantic Textual Relatedness (Venkatesh & Raman, SemEval 2024)
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PDF:
https://aclanthology.org/2024.semeval-1.124.pdf
Supplementary material:
 2024.semeval-1.124.SupplementaryMaterial.txt