Neural encoder-decoder models are usually applied to morphology learning as an end-to-end process without considering the underlying phonological representations that linguists posit as abstract forms before morphophonological rules are applied. Finite State Transducers for morphology, on the other hand, are developed to contain these underlying forms as an intermediate representation. This paper shows that training a bidirectional two-step encoder-decoder model of Arapaho verbs to learn two separate mappings between tags and abstract morphemes and morphemes and surface allomorphs improves results when training data is limited to 10,000 to 30,000 examples of inflected word forms.
A Translation-Based Approach to Morphology Learning for Low Resource Languages
