@inproceedings{eskander-etal-2019-unsupervised,
    title = "Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages",
    author = "Eskander, Ramy  and
      Klavans, Judith  and
      Muresan, Smaranda",
    editor = "Nicolai, Garrett  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-4222/",
    doi = "10.18653/v1/W19-4222",
    pages = "189--195",
    abstract = "Polysynthetic languages pose a challenge for morphological analysis due to the root-morpheme complexity and to the word class ``squish''. In addition, many of these polysynthetic languages are low-resource. We propose unsupervised approaches for morphological segmentation of low-resource polysynthetic languages based on Adaptor Grammars (AG) (Eskander et al., 2016). We experiment with four languages from the Uto-Aztecan family. Our AG-based approaches outperform other unsupervised approaches and show promise when compared to supervised methods, outperforming them on two of the four languages."
}