@inproceedings{graichen-etal-2023-enriching,
    title = "Enriching {W}ay{\'u}unaiki-{S}panish Neural Machine Translation with Linguistic Information",
    author = "Graichen, Nora  and
      Van Genabith, Josef  and
      Espa{\~n}a-bonet, Cristina",
    editor = "Mager, Manuel  and
      Ebrahimi, Abteen  and
      Oncevay, Arturo  and
      Rice, Enora  and
      Rijhwani, Shruti  and
      Palmer, Alexis  and
      Kann, Katharina",
    booktitle = "Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.americasnlp-1.9",
    doi = "10.18653/v1/2023.americasnlp-1.9",
    pages = "67--83",
    abstract = "We present the first neural machine translation system for the low-resource language pair Way{\'u}unaiki{--}Spanish and explore strategies to inject linguistic knowledge into the model to improve translation quality. We explore a wide range of methods and combine complementary approaches. Results indicate that incorporating linguistic information through linguistically motivated subword segmentation, factored models, and pretrained embeddings helps the system to generate improved translations, with the segmentation contributing most. In order to evaluate translation quality in a general domain and go beyond the available religious domain data, we gather and make publicly available a new test set and supplementary material. Although translation quality as measured with automatic metrics is low, we hope these resources will facilitate and support further research on Way{\'u}unaiki.",
}

We present the first neural machine translation system for the low-resource language pair Way{\'u}unaiki{--}Spanish and explore strategies to inject linguistic knowledge into the model to improve translation quality. We explore a wide range of methods and combine complementary approaches. Results indicate that incorporating linguistic information through linguistically motivated subword segmentation, factored models, and pretrained embeddings helps the system to generate improved translations, with the segmentation contributing most. In order to evaluate translation quality in a general domain and go beyond the available religious domain data, we gather and make publicly available a new test set and supplementary material. Although translation quality as measured with automatic metrics is low, we hope these resources will facilitate and support further research on Way{\'u}unaiki.
Graichen, Nora Van Genabith, Josef Espa{\~n}a-bonet, Cristina
Enriching Wayúunaiki-Spanish Neural Machine Translation with Linguistic Information