Questionnaires for Everyone: Streamlining Cross-Cultural Questionnaire Adaptation with GPT-Based Translation Quality Evaluation
July 30, 2024 Β· Declared Dead Β· π arXiv.org
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Authors
Otso Haavisto, Robin Welsch
arXiv ID
2407.20608
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CL
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Adapting questionnaires to new languages is a resource-intensive process often requiring the hiring of multiple independent translators, which limits the ability of researchers to conduct cross-cultural research and effectively creates inequalities in research and society. This work presents a prototype tool that can expedite the questionnaire translation process. The tool incorporates forward-backward translation using DeepL alongside GPT-4-generated translation quality evaluations and improvement suggestions. We conducted two online studies in which participants translated questionnaires from English to either German (Study 1; n=10) or Portuguese (Study 2; n=20) using our prototype. To evaluate the quality of the translations created using the tool, evaluation scores between conventionally translated and tool-supported versions were compared. Our results indicate that integrating LLM-generated translation quality evaluations and suggestions for improvement can help users independently attain results similar to those provided by conventional, non-NLP-supported translation methods. This is the first step towards more equitable questionnaire-based research, powered by AI.
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