@inproceedings{tyers-howell-2021-survey,
    title = "A survey of part-of-speech tagging approaches applied to K{'}iche'",
    author = "Tyers, Francis  and
      Howell, Nick",
    editor = "Mager, Manuel  and
      Oncevay, Arturo  and
      Rios, Annette  and
      Ruiz, Ivan Vladimir Meza  and
      Palmer, Alexis  and
      Neubig, Graham  and
      Kann, Katharina",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.americasnlp-1.6/",
    doi = "10.18653/v1/2021.americasnlp-1.6",
    pages = "44--52",
    abstract = "We study the performance of several popular neural part-of-speech taggers from the Universal Dependencies ecosystem on Mayan languages using a small corpus of 1435 annotated K{'}iche' sentences consisting of approximately 10,000 tokens, with encouraging results: $F_1$ scores 93{\%}+ on lemmatisation, part-of-speech and morphological feature assignment. The high performance motivates a cross-language part-of-speech tagging study, where K{'}iche'-trained models are evaluated on two other Mayan languages, Kaqchikel and Uspanteko: performance on Kaqchikel is good, 63-85{\%}, and on Uspanteko modest, 60-71{\%}. Supporting experiments lead us to conclude the relative diversity of morphological features as a plausible explanation for the limiting factors in cross-language tagging performance, providing some direction for future sentence annotation and collection work to support these and other Mayan languages."
}