@inproceedings{goswami-etal-2024-masontigers-semeval,
title = "{M}ason{T}igers at {S}em{E}val-2024 Task 1: An Ensemble Approach for Semantic Textual Relatedness",
author = "Goswami, Dhiman and
Puspo, Sadiya Sayara Chowdhury and
Raihan, Nishat and
Bin Emran, Al Nahian and
Ganguly, Amrita and
Zampieri, Marcos",
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-1.199",
doi = "10.18653/v1/2024.semeval-1.199",
pages = "1380--1390",
abstract = "This paper presents the MasonTigers{'} entry to the SemEval-2024 Task 1 - Semantic Textual Relatedness. The task encompasses supervised (Track A), unsupervised (Track B), and cross-lingual (Track C) approaches to semantic textual relatedness across 14 languages. MasonTigers stands out as one of the two teams who participated in all languages across the three tracks. Our approaches achieved rankings ranging from 11th to 21st in Track A, from 1st to 8th in Track B, and from 5th to 12th in Track C. Adhering to the task-specific constraints, our best performing approaches utilize an ensemble of statistical machine learning approaches combined with language-specific BERT based models and sentence transformers.",
}
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<abstract>This paper presents the MasonTigers’ entry to the SemEval-2024 Task 1 - Semantic Textual Relatedness. The task encompasses supervised (Track A), unsupervised (Track B), and cross-lingual (Track C) approaches to semantic textual relatedness across 14 languages. MasonTigers stands out as one of the two teams who participated in all languages across the three tracks. Our approaches achieved rankings ranging from 11th to 21st in Track A, from 1st to 8th in Track B, and from 5th to 12th in Track C. Adhering to the task-specific constraints, our best performing approaches utilize an ensemble of statistical machine learning approaches combined with language-specific BERT based models and sentence transformers.</abstract>
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%0 Conference Proceedings
%T MasonTigers at SemEval-2024 Task 1: An Ensemble Approach for Semantic Textual Relatedness
%A Goswami, Dhiman
%A Puspo, Sadiya Sayara Chowdhury
%A Raihan, Nishat
%A Bin Emran, Al Nahian
%A Ganguly, Amrita
%A Zampieri, Marcos
%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 goswami-etal-2024-masontigers-semeval
%X This paper presents the MasonTigers’ entry to the SemEval-2024 Task 1 - Semantic Textual Relatedness. The task encompasses supervised (Track A), unsupervised (Track B), and cross-lingual (Track C) approaches to semantic textual relatedness across 14 languages. MasonTigers stands out as one of the two teams who participated in all languages across the three tracks. Our approaches achieved rankings ranging from 11th to 21st in Track A, from 1st to 8th in Track B, and from 5th to 12th in Track C. Adhering to the task-specific constraints, our best performing approaches utilize an ensemble of statistical machine learning approaches combined with language-specific BERT based models and sentence transformers.
%R 10.18653/v1/2024.semeval-1.199
%U https://aclanthology.org/2024.semeval-1.199
%U https://doi.org/10.18653/v1/2024.semeval-1.199
%P 1380-1390
Markdown (Informal)
[MasonTigers at SemEval-2024 Task 1: An Ensemble Approach for Semantic Textual Relatedness](https://aclanthology.org/2024.semeval-1.199) (Goswami et al., SemEval 2024)
ACL