Ibanag Translator

An Ibanag-English translation tool that supports bidirectional text translation between Ibanag and other languages. It automates language detection when a source language is not provided and accepts explicit source_lang codes when accuracy is critical. The system emphasizes glossary-driven terminology retention and tone alignment to support content localization, customer communication, and learning tasks. It is designed for writers, localization engineers, support teams, educators, and researchers who require fast turnaround and consistent term usage. Example workflows include translating product descriptions for a regional audience or converting user feedback into English for analysis, while preserving domain terms such as 'delivery' and 'warranty'.
Core processing uses a neural translation model augmented by a translation memory and user-supplied glossaries. It tokenizes input text, applies model-based translation, then performs post-processing: normalization, punctuation handling, and capitalization. If a glossary term exists, it replaces model output with glossary entries to maintain consistency. Optional tone controls adjust formality, politeness, or technical style. Outputs include translated_text, detected_source_lang, and a confidence score, enabling downstream quality checks. The tool can operate in batch mode via API while also supporting real-time, form-based translation for single passages.
Use cases span content localization, multilingual support, education, and research. It differentiates itself by strong Ibanag support, glossary-driven accuracy, and API-ready outputs suitable for integration. It is beneficial for teams that need repeatable terminology and cross-language workflows, with explicit handling for orthographic variants and dialect considerations. In professional contexts, users should apply post-editing for critical content and maintain internal glossaries to further improve consistency across translations.