@inproceedings{moura-etal-2026-analysis,
title = "Analysis of Machine Translators on Sentences Generated by {P}ortuguese Image Captioning Models",
author = "Moura, Natan and
Gondim, Jo{\~a}o Medrado and
Claro, Daniela Barreiro and
Mane, Babacar",
editor = "Souza, Marlo and
de-Dios-Flores, Iria and
Santos, Diana and
Freitas, Larissa and
Souza, Jackson Wilke da Cruz and
Ribeiro, Eug{\'e}nio",
booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
month = apr,
year = "2026",
address = "Salvador, Brazil",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.propor-1.36/",
pages = "360--368",
ISBN = "979-8-89176-387-6",
abstract = "Recent works in the fields of computer vision and natural language processing have enabled the recognition and identification of objects in images, generating automatic descriptions. Despite these advancements, the main research in this field is primarily related to the English language, requiring some adaptation when dealing with other languages, such as Portuguese. One of these methods is the translate-train approach, which involves translating the training dataset into the desired language. However, there are various translators with different levels of effectiveness available. The primary objective of this work is to evaluate the behavior of image captioning models when trained on datasets translated into Portuguese by different automatic translators, both quantitatively (cost, training time, metrics on the test set) and qualitatively (comparative evaluation form, error analysis). The results indicate that it is possible to obtain valid automatic descriptions in Portuguese from image captioning models trained on translated datasets, and that more robust translators produce more meaningful descriptions."
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%0 Conference Proceedings
%T Analysis of Machine Translators on Sentences Generated by Portuguese Image Captioning Models
%A Moura, Natan
%A Gondim, João Medrado
%A Claro, Daniela Barreiro
%A Mane, Babacar
%Y Souza, Marlo
%Y de-Dios-Flores, Iria
%Y Santos, Diana
%Y Freitas, Larissa
%Y Souza, Jackson Wilke da Cruz
%Y Ribeiro, Eugénio
%S Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1
%D 2026
%8 April
%I Association for Computational Linguistics
%C Salvador, Brazil
%@ 979-8-89176-387-6
%F moura-etal-2026-analysis
%X Recent works in the fields of computer vision and natural language processing have enabled the recognition and identification of objects in images, generating automatic descriptions. Despite these advancements, the main research in this field is primarily related to the English language, requiring some adaptation when dealing with other languages, such as Portuguese. One of these methods is the translate-train approach, which involves translating the training dataset into the desired language. However, there are various translators with different levels of effectiveness available. The primary objective of this work is to evaluate the behavior of image captioning models when trained on datasets translated into Portuguese by different automatic translators, both quantitatively (cost, training time, metrics on the test set) and qualitatively (comparative evaluation form, error analysis). The results indicate that it is possible to obtain valid automatic descriptions in Portuguese from image captioning models trained on translated datasets, and that more robust translators produce more meaningful descriptions.
%U https://aclanthology.org/2026.propor-1.36/
%P 360-368
Markdown (Informal)
[Analysis of Machine Translators on Sentences Generated by Portuguese Image Captioning Models](https://aclanthology.org/2026.propor-1.36/) (Moura et al., PROPOR 2026)
ACL