MITRA lexicon links Sanskrit words to English Tibetan and Chinese translations
MITRA-lexicon: Lexica of attested translations from Sanskrit into English, Tibetan and Chinese
Computation and LanguageMachine Learning
Summary
Standard Sanskrit dictionaries list where words appear but don’t show their actual translations or link to digital texts. The authors created MITRA-lexicon, a large digital dictionary connecting Sanskrit words with their real translated equivalents in English, Tibetan, and Chinese. This lexicon is based on millions of examples from Buddhist texts, showing words in context alongside their translations. The authors developed a system using language models to align these translations accurately and provided an online tool to explore them.
What this means in practice
- •For digital humanities teams: Improve research tools by linking Sanskrit words directly to their translations in English, Tibetan, and Chinese within digitized texts.
- •For language technology developers: Develop enhanced multilingual translation systems using aligned Sanskrit translation examples as training data across three languages.
Authors
Sebastian Nehrdich, Kengo Harimoto, Sven Sellmer, Oliver Hellwig
Abstract
The standard Sanskrit lexica are monumental and indispensable, but they cite their attestations as abbreviated references to editions, without links to the digitized texts now widely available and without a translation of the passage, and they naturally do not reflect translations made after them. To address these gaps, we present MITRA-lexicon, a family of corpus-grounded lexica based on span annotations of translation equivalents with Sanskrit as the pivot: Sanskrit-English, derived from the Mitrasamgraha corpus, as well as Sanskrit-Tibetan and Sanskrit-Chinese, derived from the MITRA-parallel corpus of Buddhist literature. In total, the resource comprises 2.18 million headword entries in six directional lexica. Headwords are lemmas and compounds from silver annotations that follow the Digital Corpus of Sanskrit (DCS). Senses are ranked by frequency, and each of the 10.4 million attestations includes its sentence pair, with character offsets of the word and its equivalent. We propose a multi-step pipeline based on grammatical annotation models, fine-tuned large language models, and a teacher-student setup to produce the alignments. We evaluate the lexica against reference dictionaries and manually corrected gold alignments. We release the lexica and the fine-tuned span annotation model, together with a web interface for looking up headwords and their attested senses.