@inproceedings{oliveira-etal-2026-bridging,
title = "Bridging Cultural Gaps in Automated Translation of {B}razilian Expressions: A Study on Cultural Adaptation",
author = "Oliveira, Maria Luiza Silva de and
Santos, Andressa Andrade Oliveira dos and
Andrade, Leandro Jose Silva",
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. 2",
month = apr,
year = "2026",
address = "Salvador, Brazil",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.propor-2.30/",
pages = "220--227",
ISBN = "979-8-89176-387-6",
abstract = "Automated translation systems exhibit a tendency toward cultural drift when processing non-literal language, often favoring standardized outputs that diverge from the original pragmatic intent. Although Large Language Models (LLMs) have introduced more sophisticated context-handling capabilities, the transition from literal decoding to effective cultural adaptation remains inconsistent.This study investigates these linguistic detours by evaluating ChatGPT-4o, Gemini 1.5 Pro, and Google Translate using a corpus of 100 Brazilian Portuguese expressions. To ensure contemporary relevance, the expressions were validated through the \textit{Corpus Carolina} and categorized into four groups: classical idioms, regionalisms, metaphors, and intensifiers. Translation quality was assessed using the Multidimensional Quality Metrics (MQM) framework, focusing on adequacy, fluency, and cultural adaptation.The analysis reveals that, even when grammatical accuracy is achieved, automated systems frequently overlook the socio-cultural weight embedded in the source language. Such semantic shifts pose significant challenges in high-stakes professional communication, where nuanced mediation is essential. The findings underscore the limitations of current AI systems in cultural competence and reinforce the ongoing necessity of human intervention to bridge the gap between algorithmic processing and regional identity."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="oliveira-etal-2026-bridging">
<titleInfo>
<title>Bridging Cultural Gaps in Automated Translation of Brazilian Expressions: A Study on Cultural Adaptation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="given">Luiza</namePart>
<namePart type="given">Silva</namePart>
<namePart type="given">de</namePart>
<namePart type="family">Oliveira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andressa</namePart>
<namePart type="given">Andrade</namePart>
<namePart type="given">Oliveira</namePart>
<namePart type="given">dos</namePart>
<namePart type="family">Santos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Leandro</namePart>
<namePart type="given">Jose</namePart>
<namePart type="given">Silva</namePart>
<namePart type="family">Andrade</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-04</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2</title>
</titleInfo>
<name type="personal">
<namePart type="given">Marlo</namePart>
<namePart type="family">Souza</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Iria</namePart>
<namePart type="family">de-Dios-Flores</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Diana</namePart>
<namePart type="family">Santos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Larissa</namePart>
<namePart type="family">Freitas</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jackson</namePart>
<namePart type="given">Wilke</namePart>
<namePart type="given">da</namePart>
<namePart type="given">Cruz</namePart>
<namePart type="family">Souza</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Eugénio</namePart>
<namePart type="family">Ribeiro</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Salvador, Brazil</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-387-6</identifier>
</relatedItem>
<abstract>Automated translation systems exhibit a tendency toward cultural drift when processing non-literal language, often favoring standardized outputs that diverge from the original pragmatic intent. Although Large Language Models (LLMs) have introduced more sophisticated context-handling capabilities, the transition from literal decoding to effective cultural adaptation remains inconsistent.This study investigates these linguistic detours by evaluating ChatGPT-4o, Gemini 1.5 Pro, and Google Translate using a corpus of 100 Brazilian Portuguese expressions. To ensure contemporary relevance, the expressions were validated through the Corpus Carolina and categorized into four groups: classical idioms, regionalisms, metaphors, and intensifiers. Translation quality was assessed using the Multidimensional Quality Metrics (MQM) framework, focusing on adequacy, fluency, and cultural adaptation.The analysis reveals that, even when grammatical accuracy is achieved, automated systems frequently overlook the socio-cultural weight embedded in the source language. Such semantic shifts pose significant challenges in high-stakes professional communication, where nuanced mediation is essential. The findings underscore the limitations of current AI systems in cultural competence and reinforce the ongoing necessity of human intervention to bridge the gap between algorithmic processing and regional identity.</abstract>
<identifier type="citekey">oliveira-etal-2026-bridging</identifier>
<location>
<url>https://aclanthology.org/2026.propor-2.30/</url>
</location>
<part>
<date>2026-04</date>
<extent unit="page">
<start>220</start>
<end>227</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Bridging Cultural Gaps in Automated Translation of Brazilian Expressions: A Study on Cultural Adaptation
%A Oliveira, Maria Luiza Silva de
%A Santos, Andressa Andrade Oliveira dos
%A Andrade, Leandro Jose Silva
%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. 2
%D 2026
%8 April
%I Association for Computational Linguistics
%C Salvador, Brazil
%@ 979-8-89176-387-6
%F oliveira-etal-2026-bridging
%X Automated translation systems exhibit a tendency toward cultural drift when processing non-literal language, often favoring standardized outputs that diverge from the original pragmatic intent. Although Large Language Models (LLMs) have introduced more sophisticated context-handling capabilities, the transition from literal decoding to effective cultural adaptation remains inconsistent.This study investigates these linguistic detours by evaluating ChatGPT-4o, Gemini 1.5 Pro, and Google Translate using a corpus of 100 Brazilian Portuguese expressions. To ensure contemporary relevance, the expressions were validated through the Corpus Carolina and categorized into four groups: classical idioms, regionalisms, metaphors, and intensifiers. Translation quality was assessed using the Multidimensional Quality Metrics (MQM) framework, focusing on adequacy, fluency, and cultural adaptation.The analysis reveals that, even when grammatical accuracy is achieved, automated systems frequently overlook the socio-cultural weight embedded in the source language. Such semantic shifts pose significant challenges in high-stakes professional communication, where nuanced mediation is essential. The findings underscore the limitations of current AI systems in cultural competence and reinforce the ongoing necessity of human intervention to bridge the gap between algorithmic processing and regional identity.
%U https://aclanthology.org/2026.propor-2.30/
%P 220-227
Markdown (Informal)
[Bridging Cultural Gaps in Automated Translation of Brazilian Expressions: A Study on Cultural Adaptation](https://aclanthology.org/2026.propor-2.30/) (Oliveira et al., PROPOR 2026)
ACL