@inproceedings{pakray-etal-2025-findings,
    title = "Findings of {WMT} 2025 Shared Task on Low-resource {I}ndic Languages Translation",
    author = "Pakray, Partha  and
      Krishna, Reddi  and
      Pal, Santanu  and
      Vetagiri, Advaitha  and
      Dash, Sandeep  and
      Maji, Arnab Kumar  and
      Lyngdoh, Saralin A.  and
      Laitonjam, Lenin  and
      Jamatia, Anupam  and
      Sambyo, Koj  and
      Das, Ajit  and
      Manna, Riyanka",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Tenth Conference on Machine Translation",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.wmt-1.29/",
    pages = "532--553",
    ISBN = "979-8-89176-341-8",
    abstract = "This study proposes the results of the lowresource Indic language translation task organized in collaboration with the Tenth Conference on Machine Translation (WMT) 2025. In this workshop, participants were required to build and develop machine translation models for the seven language pairs, which were categorized into two categories. Category 1 is moderate training data available in languages i.e English{--}Assamese, English{--}Mizo, English-Khasi, English{--}Manipuri and English{--} Nyishi. Category 2 has very limited training data available in languages, i.e English{--}Bodo and English{--}Kokborok. This task leverages the enriched IndicNE-corp1.0 dataset, which consists of an extensive collection of parallel and monilingual corpora for north eastern Indic languages. The participant results were evaluated using automatic machine translation metrics, including BLEU, TER, ROUGE-L, ChrF, and METEOR. Along with those metrics, this year{'}s work also includes Cosine similarity for evaluation, which captures the semantic representation of the sentence to measure the performance and accuracy of the models. This work aims to promote innovation and advancements in low-resource Indic languages."
}