@inproceedings{moctezuma-etal-2024-ingeotec-semeval,
title = "{INGEOTEC} at {S}em{E}val-2024 Task 1: Bag of Words and Transformers",
author = "Moctezuma, Daniela and
Tellez, Eric and
Graff, Mario",
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-1.168",
doi = "10.18653/v1/2024.semeval-1.168",
pages = "1155--1159",
abstract = "Understanding the meaning of a written message is crucial in solving problems related to Natural Language Processing; the relatedness of two or more messages is a semantic problem tackled with supervised and unsupervised learning. This paper outlines our submissions to the Semantic Textual Relatedness (STR) challenge at SemEval 2024, which is devoted to evaluating the degree of semantic similarity and relatedness between two sentences across multiple languages. We use two main strategies in our submissions. The first approach is based on the Bag-of-Word scheme, while the second one uses pre-trained Transformers for text representation. We found some attractive results, especially in cases where different models adjust better to certain languages over others.",
}
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<abstract>Understanding the meaning of a written message is crucial in solving problems related to Natural Language Processing; the relatedness of two or more messages is a semantic problem tackled with supervised and unsupervised learning. This paper outlines our submissions to the Semantic Textual Relatedness (STR) challenge at SemEval 2024, which is devoted to evaluating the degree of semantic similarity and relatedness between two sentences across multiple languages. We use two main strategies in our submissions. The first approach is based on the Bag-of-Word scheme, while the second one uses pre-trained Transformers for text representation. We found some attractive results, especially in cases where different models adjust better to certain languages over others.</abstract>
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%0 Conference Proceedings
%T INGEOTEC at SemEval-2024 Task 1: Bag of Words and Transformers
%A Moctezuma, Daniela
%A Tellez, Eric
%A Graff, Mario
%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 moctezuma-etal-2024-ingeotec-semeval
%X Understanding the meaning of a written message is crucial in solving problems related to Natural Language Processing; the relatedness of two or more messages is a semantic problem tackled with supervised and unsupervised learning. This paper outlines our submissions to the Semantic Textual Relatedness (STR) challenge at SemEval 2024, which is devoted to evaluating the degree of semantic similarity and relatedness between two sentences across multiple languages. We use two main strategies in our submissions. The first approach is based on the Bag-of-Word scheme, while the second one uses pre-trained Transformers for text representation. We found some attractive results, especially in cases where different models adjust better to certain languages over others.
%R 10.18653/v1/2024.semeval-1.168
%U https://aclanthology.org/2024.semeval-1.168
%U https://doi.org/10.18653/v1/2024.semeval-1.168
%P 1155-1159
Markdown (Informal)
[INGEOTEC at SemEval-2024 Task 1: Bag of Words and Transformers](https://aclanthology.org/2024.semeval-1.168) (Moctezuma et al., SemEval 2024)
ACL