Complementary Strategies for Low Resourced Morphological Modeling
Alexander Erdmann, Nizar Y. Habash · 2018
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 outof-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.