@inproceedings{moeller-etal-2018-neural,
title = "A Neural Morphological Analyzer for {A}rapaho Verbs Learned from a Finite State Transducer",
author = "Moeller, Sarah and
Kazeminejad, Ghazaleh and
Cowell, Andrew and
Hulden, Mans",
editor = "Klavans, Judith L.",
booktitle = "Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-4802",
pages = "12--20",
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).",
}
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%0 Conference Proceedings
%T A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer
%A Moeller, Sarah
%A Kazeminejad, Ghazaleh
%A Cowell, Andrew
%A Hulden, Mans
%Y Klavans, Judith L.
%S Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F moeller-etal-2018-neural
%X 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).
%U https://aclanthology.org/W18-4802
%P 12-20
Markdown (Informal)
[A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer](https://aclanthology.org/W18-4802) (Moeller et al., PYLO 2018)
ACL