@inproceedings{cohen-etal-2026-scaling,
    title = "Scaling Sentence Similarity for {C}lassical {T}ibetan with Automatic Annotations",
    author = "Cohen, Shay  and
      Yang, Jingyi  and
      Rabinovitz, Gal  and
      Choden, Sonam  and
      Shtrosberg, Ofir  and
      Bajetta, Nicola  and
      Horin, Goody Ben  and
      Sund{\'e}n, Rebecca  and
      Drori, Omri  and
      Jamtsho, Sonam  and
      Wangchuk, Dorji  and
      Bar, Kfir  and
      Almogi, Orna  and
      Fine, Shai",
    editor = {Hamilton, Sil  and
      {\"O}hman, Emily  and
      Hicke, Rebecca M. M.  and
      Bizzoni, Yuri  and
      Bax, Axel  and
      Matthews, Jacob A.  and
      H{\"a}m{\"a}l{\"a}inen, Mika},
    booktitle = "Proceedings of the 6th International Conference on Natural Language Processing for the Digital Humanities",
    month = jul,
    year = "2026",
    address = "San Diego, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.nlp4dh-1.15/",
    pages = "150--166",
    ISBN = "979-8-89176-427-9",
    abstract = "Identifying intertextual parallels is central to 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 languages. We address this challenge by introducing a scalable automatic annotation pipeline for training 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) an ensemble of embedding models and rerankers, and (2) an LLM-as-a-judge committee using best{--}worst scaling. When combined with a domain-specific, gold-standard annotated dataset for sequential fine-tuning, the resulting text-similarity model achieves a state-of-the-art Spearman correlation of 0.864 on the STS task. This enables effective semantic search in Classical Tibetan and provides a framework for automatic supervision in low-resource languages used in digital humanities. We will make our code, dataset, and trained model publicly available upon publication."
}