@inproceedings{aali-etal-2024-ysp,
title = "{YSP} at {S}em{E}val-2024 Task 1: Enhancing Sentence Relatedness Assessment using {S}iamese Networks",
author = "Aali, Yasamin and
Hamidian, Sardar and
Farinneya, Parsa",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.138/",
doi = "10.18653/v1/2024.semeval-1.138",
pages = "959--963",
abstract = "In this paper we present the system for Track A in the SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages (STR). The proposed system integrates a Siamese Network architecture with pre-trained language models, including BERT, RoBERTa, and the Universal Sentence Encoder (USE). Through rigorous experimentation and analysis, we evaluate the performance of these models across multiple languages. Our findings reveal that the Universal Sentence Encoder excels in capturing semantic similarities, outperforming BERT and RoBERTa in most scenarios. Particularly notable is the USE`s exceptional performance in English and Marathi. These results emphasize the importance of selecting appropriate pre-trained models based on linguistic considerations and task requirements."
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%0 Conference Proceedings
%T YSP at SemEval-2024 Task 1: Enhancing Sentence Relatedness Assessment using Siamese Networks
%A Aali, Yasamin
%A Hamidian, Sardar
%A Farinneya, Parsa
%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 aali-etal-2024-ysp
%X In this paper we present the system for Track A in the SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages (STR). The proposed system integrates a Siamese Network architecture with pre-trained language models, including BERT, RoBERTa, and the Universal Sentence Encoder (USE). Through rigorous experimentation and analysis, we evaluate the performance of these models across multiple languages. Our findings reveal that the Universal Sentence Encoder excels in capturing semantic similarities, outperforming BERT and RoBERTa in most scenarios. Particularly notable is the USE‘s exceptional performance in English and Marathi. These results emphasize the importance of selecting appropriate pre-trained models based on linguistic considerations and task requirements.
%R 10.18653/v1/2024.semeval-1.138
%U https://aclanthology.org/2024.semeval-1.138/
%U https://doi.org/10.18653/v1/2024.semeval-1.138
%P 959-963
Markdown (Informal)
[YSP at SemEval-2024 Task 1: Enhancing Sentence Relatedness Assessment using Siamese Networks](https://aclanthology.org/2024.semeval-1.138/) (Aali et al., SemEval 2024)
ACL