Translation Quality Evaluation for AI
Executed fine-grained linguistic evaluation to train and improve machine translation systems. The core task involved assessing translated text segments (from [ Portuguese to English]) against source material for
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I have hands-on experience in AI data training and evaluation through work on platforms like UHRS and OneForma. My tasks included assessing the quality and relevance of search engine results (Side-by-Side evaluation in English and Portuguese), verifying critical business data such as names, addresses, and phone numbers, and performing various data labeling and content annotation tasks. My background in IT support and web development provides me with strong technical reasoning and attention to detail, which I apply to follow complex guidelines meticulously. I am skilled at working independently on structured tasks, consistently aiming to improve the accuracy and reliability of digital information and AI system
Executed fine-grained linguistic evaluation to train and improve machine translation systems. The core task involved assessing translated text segments (from [ Portuguese to English]) against source material for
2AFC: Performed pairwise comparisons to train AI preference models. Surveys: Collected and categorized user feedback data. Deadlink: Identified and validated broken links for data integrity.
Performed quality evaluation of translated text segments and annotated/categorized multilingual content (e.g., critic reviews). Tasks required deep linguistic understanding to assess fluency, accuracy, and cultural appropriateness.
Rated the relevance of answers to specific questions and classified the overall quality/usefulness of web pages (e.g., "Junk or Not"). Applied nuanced criteria to train AI models on content understanding.
Evaluated the relevance and quality of search engine results for text, image, and video queries. Compared result pairs (Side-by-Side) to determine the most helpful one, following complex guidelines to improve ranking algorithms.
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