A Large-Scale Parallel Corpus and Multilingual Pretrained Language Model for Machine Translation and Semantic Retrieval for Pāli, Sanskrit, Buddhist Chinese, and Tibetan

ACL ARR 2026 January Submission8491 Authors

Keywords: Information retrieval, Sanskrit, Tibetan, Pāli, Chinese, machine translation

Abstract: Ancient Buddhist literature features frequent, yet often unannotated, textual parallels spread across diverse languages: Sanskrit, Pāli, Buddhist Chinese, Tibetan, and more. The scale of this material makes manual examination prohibitive. We present the X (name changed for anonymity purposes) framework, which consists of a novel pipeline for multilingual parallel passage mining, X-parallel, a large-scale corpus of 1.74 million parallel sentence pairs between Sanskrit, Chinese, and Tibetan, and the development of the domain-specific pretrained language model Gemma 2 X. We present Gemma 2 X-MT, a version of this base model fine-tuned on machine translation tasks, reaching state-of-the-art performance for machine translation of these languages into English and outperforming even much larger open-source models. We also present Gemma 2 X-E, a semantic embedding model that shows state-of-the-art performance on a novel, detailed semantic embedding benchmark. We make the parallel dataset, model weights, and semantic similarity benchmark openly available to aid both NLP research and philological studies in Buddhist and classical Asian literature.

Paper Type: Long

Research Area: Language Models

Research Area Keywords: Information Retrieval and Text Mining

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Publicly available software and/or pre-trained models, Data resources

Languages Studied: Sanskrit, Tibetan, Pāli, Buddhist Chinese

Submission Number: 8491