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.- Anthology ID:
- W18-5806
- Volume:
- Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- SIGMORPHON
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 54–65
- Language:
- URL:
- https://aclanthology.org/W18-5806
- DOI:
- 10.18653/v1/W18-5806
- Cite (ACL):
- Alexander Erdmann and Nizar Habash. 2018. Complementary Strategies for Low Resourced Morphological Modeling. In Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 54–65, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Complementary Strategies for Low Resourced Morphological Modeling (Erdmann & Habash, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-5806.pdf