@inproceedings{takahashi-etal-2024-ozemi,
title = "{OZ}emi at {S}em{E}val-2024 Task 1: A Simplistic Approach to Textual Relatedness Evaluation Using Transformers and Machine Translation",
author = "Takahashi, Hidetsune and
Lu, Xingru and
Ishijima, Sean and
Seo, Deokgyu and
Kim, Yongju and
Park, Sehoon and
Song, Min and
Marante, Kathylene and
Iso, Keitaro-luke and
Tokura, Hirotaka and
Ohman, Emily",
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-1.2",
doi = "10.18653/v1/2024.semeval-1.2",
pages = "7--12",
abstract = "In this system paper for SemEval-2024 Task 1 subtask A, we present our approach to evaluating the semantic relatedness of sentence pairs in nine languages. We use a mix of statistical methods combined with fine-tuned BERT transformer models for English and use the same model and machine-translated data for the other languages. This simplistic approach shows consistently reliable scores and achieves above-average rank in all languages.",
}
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<namePart type="given">Hirotaka</namePart>
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<title>Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)</title>
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<abstract>In this system paper for SemEval-2024 Task 1 subtask A, we present our approach to evaluating the semantic relatedness of sentence pairs in nine languages. We use a mix of statistical methods combined with fine-tuned BERT transformer models for English and use the same model and machine-translated data for the other languages. This simplistic approach shows consistently reliable scores and achieves above-average rank in all languages.</abstract>
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%0 Conference Proceedings
%T OZemi at SemEval-2024 Task 1: A Simplistic Approach to Textual Relatedness Evaluation Using Transformers and Machine Translation
%A Takahashi, Hidetsune
%A Lu, Xingru
%A Ishijima, Sean
%A Seo, Deokgyu
%A Kim, Yongju
%A Park, Sehoon
%A Song, Min
%A Marante, Kathylene
%A Iso, Keitaro-luke
%A Tokura, Hirotaka
%A Ohman, Emily
%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 takahashi-etal-2024-ozemi
%X In this system paper for SemEval-2024 Task 1 subtask A, we present our approach to evaluating the semantic relatedness of sentence pairs in nine languages. We use a mix of statistical methods combined with fine-tuned BERT transformer models for English and use the same model and machine-translated data for the other languages. This simplistic approach shows consistently reliable scores and achieves above-average rank in all languages.
%R 10.18653/v1/2024.semeval-1.2
%U https://aclanthology.org/2024.semeval-1.2
%U https://doi.org/10.18653/v1/2024.semeval-1.2
%P 7-12
Markdown (Informal)
[OZemi at SemEval-2024 Task 1: A Simplistic Approach to Textual Relatedness Evaluation Using Transformers and Machine Translation](https://aclanthology.org/2024.semeval-1.2) (Takahashi et al., SemEval 2024)
ACL
- Hidetsune Takahashi, Xingru Lu, Sean Ishijima, Deokgyu Seo, Yongju Kim, Sehoon Park, Min Song, Kathylene Marante, Keitaro-luke Iso, Hirotaka Tokura, and Emily Ohman. 2024. OZemi at SemEval-2024 Task 1: A Simplistic Approach to Textual Relatedness Evaluation Using Transformers and Machine Translation. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 7–12, Mexico City, Mexico. Association for Computational Linguistics.