Telem Joyson Singh, Sanabam Boyshana, Sanasam Ranbir Singh, Priyankoo Sarmah · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.29943 · cited by 0 works (OpenAlex) · open-access PDF
Los procedimientos judiciales indios se llevan a cabo en inglés, por lo que los hablantes de idiomas con tecnología de traducción limitada tienen acceso limitado a ellos. Presentamos el primer conjunto de datos paralelo de Mizo en inglés de los juicios judiciales indios. Contiene 13,168 pares de sentencias del Tribunal Superior de Gauhati y el Tribunal Supremo de India. Los traductores profesionales nativos de Mizo traducen documentos completos; los pares de sentencias se alinearon manualmente y dos revisores independientes revisaron los resultados. Nuestro estudio encuentra que las palabras inglesas se toman de forma muy selectiva: los términos oficiales y legales se mantienen casi siempre en inglés, pero las palabras para las cosas cotidianas como acciones, situaciones y resultados generalmente se expresan en Mizo nativo. Las diacríticas de Mizo también se usan
Machine translation (Meta M2M100); the English original follows.
Indian court proceedings are held in English, so speakers of languages with limited translation technology have limited access to them. We present the first English Mizo parallel dataset of Indian court judgments. It contains 13,168 sentence pairs from the Gauhati High Court and the Supreme Court of India. Professional native Mizo translators translated complete documents; sentence pairs were manually aligned, and two independent reviewers checked the results. Our study finds that English words are borrowed in a very selective way: official and legal terms are almost always kept in English, but words for everyday things like actions, situations, and results are usually expressed in native Mizo. Mizo diacritics are also used inconsistently across the corpus. When we fine tune the NLLB 200 1.3B model on our training data, it scores 27.47 BLEU on a balanced legal test set of 1,000 sentence pairs, an improvement of 7.16 BLEU over the base model. However, it still uses English words about three times more often than a human translator does.
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Topics: Sentence, Supreme court, Computer science, Natural language processing, Set (abstract data type), Artificial intelligence
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