@inproceedings{pakray-etal-2024-findings,
    title = "Findings of {WMT} 2024 Shared Task on Low-Resource {I}ndic Languages Translation",
    author = "Pakray, Partha  and
      Pal, Santanu  and
      Vetagiri, Advaitha  and
      Krishna, Reddi  and
      Maji, Arnab Kumar  and
      Dash, Sandeep  and
      Laitonjam, Lenin  and
      Sarah, Lyngdoh  and
      Manna, Riyanka",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Ninth Conference on Machine Translation",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.wmt-1.54/",
    doi = "10.18653/v1/2024.wmt-1.54",
    pages = "654--668",
    abstract = "This paper presents the results of the low-resource Indic language translation task, organized in conjunction with the Ninth Conference on Machine Translation (WMT) 2024. In this edition, participants were challenged to develop machine translation models for four distinct language pairs: English{--}Assamese, English-Mizo, English-Khasi, and English-Manipuri. The task utilized the enriched IndicNE-Corp1.0 dataset, which includes an extensive collection of parallel and monolingual corpora for northeastern Indic languages. The evaluation was conducted through a comprehensive suite of automatic metrics{---}BLEU, TER, RIBES, METEOR, and ChrF{---}supplemented by meticulous human assessment to measure the translation systems' performance and accuracy. This initiative aims to drive advancements in low-resource machine translation and make a substantial contribution to the growing body of knowledge in this dynamic field."
}