Shay Cohen,\u00a0Jingyi Yang,\u00a0Gal Rabinovitz,\u00a0Sonam Choden,\u00a0Ofir Shtrosberg,\u00a0Nicola Bajetta,\u00a0Goody Ben Horin,\u00a0Rebecca Sund\u00e9n,\u00a0Omri Drori,\u00a0Sonam Jamtsho,\u00a0Dorji Wangchuk,\u00a0Kfir Bar,\u00a0Orna Almogi,\u00a0Shai Fine, 2026.<\/p>\n\n\n\n
Identifying intertextual parallels is a central task in textual philology, traditionally requiring labor-intensive manual analysis. While digitized historical corpora enable automated approaches using semantic sentence embeddings, training such models requires large annotated datasets, which are scarce for low-resource historical languages.<\/p>\n\n\n\n
We address this challenge by introducing a scalable, automated annotation pipeline to train semantic embedding models for Classical Tibetan. Our method combines unsupervised contrastive bootstrapping with iterative pair mining, generating silver-standard similarity labels through two complementary annotation strategies: (1) a representation ensemble of embedding models and rerankers, and (2) an LLM-as-a-judge committee using best\u2013worst scaling. When combined with a domain-specific gold dataset for sequential fine-tuning, the resulting model achieves a state-of-the-art Spearman correlation of 0.864, enabling effective semantic search in Classical Tibetan and offering a general framework for synthetic supervision in low-resource digital humanities settings.<\/p>\n\n\n\n
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https:\/\/aclanthology.org\/2026.nlp4dh-1.15\/<\/a><\/p>\n\n\n\n