@inproceedings{singh-etal-2025-findings-mmloso,
    title = "Findings of the {MML}o{S}o 2025 Shared Task on Machine Translation into Tribal Languages",
    author = "Singh, Pooja  and
      Chatterjee, Sandeep  and
      Cheema, Gullal S.  and
      Bedi, Amrit Singh  and
      Chakraborty, Tanmoy  and
      Kumar, Sandeep  and
      Shukla, Ankita",
    editor = "Shukla, Ankita  and
      Kumar, Sandeep  and
      Bedi, Amrit Singh  and
      Chakraborty, Tanmoy",
    booktitle = "Proceedings of the 1st Workshop on Multimodal Models for Low-Resource Contexts and Social Impact (MMLoSo 2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.mmloso-1.14/",
    pages = "121--129",
    ISBN = "979-8-89176-311-1",
    abstract = "This paper presents the findings of the MMLoSo Shared Task on Machine Translation. The competition features four tribal languages from India: Bhili, Mundari, Gondi, and Santali, each with 20,000 high-quality parallel sentence pairs and a 16,000-sentence evaluation set. A total of 18 teams submitted across all language pairs. The shared task addresses the challenges of translating India{'}s severely low-resource tribal languages, which, despite having millions of speakers, remain digitally marginalized due to limited textual resources, diverse scripts, rich morphology, and minimal publicly available parallel corpora. Systems were ranked using a weighted composite score combining BLEU (60{\%}) and chrF (40{\%}) to balance structural accuracy and character-level fluency. The best-performing system leveraged IndicTrans2 with directional LoRA adapters and reverse-model reranking. This work establishes the first reproducible benchmark for machine translation in these tribal languages. All datasets, baseline models, and system outputs are publicly released to support continued research in India{'}s tribal language technologies."
}