Complementary Strategies for Low Resourced Morphological Modeling

Alexander Erdmann, Nizar Habash

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageUndefined
Title of host publicationProceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology
Place of PublicationBrussels, Belgium
PublisherAssociation for Computational Linguistics (ACL)
Number of pages12
StatePublished - Oct 1 2018

Cite this