@InProceedings{erdmann-habash:2018:SIGMORPHON,
  author    = {Erdmann, Alexander  and  Habash, Nizar},
  title     = {Complementary Strategies for Low Resourced Morphological Modeling},
  booktitle = {Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  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},
  url       = {http://www.aclweb.org/anthology/W18-5806}
}