@inproceedings{erdmann-habash-2018-complementary,
    title = "Complementary Strategies for Low Resourced Morphological Modeling",
    author = "Erdmann, Alexander  and
      Habash, Nizar",
    editor = "Kuebler, Sandra  and
      Nicolai, Garrett",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-5806/",
    doi = "10.18653/v1/W18-5806",
    pages = "54--65",
    abstract = "Morphologically rich languages are challenging for natural language processing tasks due to data sparsity. This can be addressed either by introducing out-of-context morphological knowledge, or by developing machine learning architectures that specifically target data sparsity and/or morphological information. We find these approaches to complement each other in a morphological paradigm modeling task in Modern Standard Arabic, which, in addition to being morphologically complex, features ubiquitous ambiguity, exacerbating sparsity with noise. Given a small number of out-of-context rules describing closed class morphology, we combine them with word embeddings leveraging subword strings and noise reduction techniques. The combination outperforms both approaches individually by about 20{\%} absolute. While morphological resources already exist for Modern Standard Arabic, our results inform how comparable resources might be constructed for non-standard dialects or any morphologically rich, low resourced language, given scarcity of time and funding."
}