A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer
Sarah Moeller, Ghazaleh Kazeminejad, Andrew Cowell, Mans Hulden
Abstract
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).- Anthology ID:
- W18-4802
- Volume:
- Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Venue:
- PYLO
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12–20
- Language:
- URL:
- https://aclanthology.org/W18-4802
- DOI:
- Cite (ACL):
- Sarah Moeller, Ghazaleh Kazeminejad, Andrew Cowell, and Mans Hulden. 2018. A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer. In Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages, pages 12–20, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer (Moeller et al., PYLO 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-4802.pdf