@inproceedings{cotik-etal-2026-qomlaqtaqa,
    title = "{Q}om{L}{'}aqtaqa: A {Q}om{--}{S}panish Parallel Corpus for Natural Language Processing with Machine Translation Evaluation",
    author = "Cotik, Viviana  and
      Korablev, Aleksei  and
      C{\'u}neo, Paola  and
      Laciana, Pablo",
    editor = "Mager, Manuel  and
      Ebrahimi, Abteen  and
      Bui, Minh Duc  and
      Pugh, Robert  and
      Oncevay, Arturo  and
      Chiruzzo, Luis  and
      Solano, Rolando Coto  and
      Rijhwani, Shruti  and
      Von Der Wense, Katharina",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Indigenous Languages of the {A}mericas ({A}mericas{NLP})",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.americasnlp-6.17/",
    pages = "186--202",
    ISBN = "979-8-89176-415-6",
    abstract = "Qom, a language of the Guaycuruan family, is a low-resource language for NLP and speech processing. We present the first parallel Qom{--}Spanish corpus in a computationally usable format, comprising 33,392 parallel segments, totaling 1,469,905 Qom tokens and 891,344 Spanish tokens. A subset of 2,943 segments excludes Bible-derived content. It includes alignments at different levels: sentences, sentence fragments, and paragraphs, and is compiled from multiple sources, both previously available and newly collected. We also present bidirectional neural machine translation baselines based on NLLB-200, achieving competitive performance in both translation directions on the full dataset, and lower performance on the non-Bible subset. An ablation study shows that training exclusively on biblical data reduces performance on non-biblical text, highlighting the importance of domain diversity in low-resource machine translation."
}