@inproceedings{khandagale-etal-2022-towards,
    title = "Towards Unsupervised Morphological Analysis of Polysynthetic Languages",
    author = "Khandagale, Sujay  and
      L{\'e}veill{\'e}, Yoann  and
      Miller, Samuel  and
      Pham, Derek  and
      Eskander, Ramy  and
      Lowry, Cass  and
      Compton, Richard  and
      Klavans, Judith  and
      Polinsky, Maria  and
      Muresan, Smaranda",
    editor = "He, Yulan  and
      Ji, Heng  and
      Li, Sujian  and
      Liu, Yang  and
      Chang, Chua-Hui",
    booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2022",
    address = "Online only",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.aacl-short.41/",
    doi = "10.18653/v1/2022.aacl-short.41",
    pages = "334--340",
    abstract = "Polysynthetic languages present a challenge for morphological analysis due to the complexity of their words and the lack of high-quality annotated datasets needed to build and/or evaluate computational models. The contribution of this work is twofold. First, using linguists' help, we generate and contribute high-quality annotated data for two low-resource polysynthetic languages for two tasks: morphological segmentation and part-of-speech (POS) tagging. Second, we present the results of state-of-the-art unsupervised approaches for these two tasks on Adyghe and Inuktitut. Our findings show that for these polysynthetic languages, using linguistic priors helps the task of morphological segmentation and that using stems rather than words as the core unit of abstraction leads to superior performance on POS tagging."
}