Complementary Strategies for Low Resourced Morphological Modeling

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.