Evaluating Sequence Alignment for Learning Inflectional Morphology

@inproceedings{King2016EvaluatingSA,
  title={Evaluating Sequence Alignment for Learning Inflectional Morphology},
  author={David L. King},
  booktitle={SIGMORPHON},
  year={2016}
}
This work examines CRF-based sequence alignment models for learning natural language morphology. Although these systems have performed well for a limited number of languages, this work, as part of the SIGMORPHON 2016 shared task, specifically sets out to determine whether these models handle non-concatenative morphology as well as previous work might suggest. Results, however, indicate a strong preference for simpler, concatenative morphological systems. 
Neural sequence-to-sequence models for low-resource morphology
ISI at the SIGMORPHON 2017 Shared Task on Morphological Reinflection
Computational Morphology with Neural Network Approaches
  • Ling Liu
  • Computer Science
  • ArXiv
  • 2021

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