A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer
We experiment with training an encoder-decoder neural model for mimicking the behavior of an existing hand-written finite-state morphological grammar for Arapaho verbs, a polysynthetic language with a highly complex verbal inflection system. After adjusting for ambiguous parses, we find that the system is able to generalize to unseen forms with accuracies of 98.68{\%} (unambiguous verbs) and 92.90{\%} (all verbs).
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