| Challenge: | Existing approaches to morphological tagging are limited by the assumption that tag sets overlap . a limited amount of data is available for most languages to learn these morphology taggers. |
| Approach: | They propose a method for cross-lingual morphological tagging that relaxes this assumption . they use factorial conditional random fields with neural network potentials to smooth over superficial differences in the surface forms . |
| Outcome: | The proposed model can smooth over superficial differences in the surface forms and generate unseen or rare tag sets. |
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A Simple Joint Model for Improved Contextual Neural Lemmatization (N19-1)
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Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging (D18-1)
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A Pointer Network Architecture for Joint Morphological Segmentation and Tagging (2020.findings-emnlp)
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