@inproceedings{shillingford-parker-jones-2018-recovering,
title = "Recovering Missing Characters in Old {H}awaiian Writing",
author = "Shillingford, Brendan and
Parker Jones, Oiwi",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1533",
doi = "10.18653/v1/D18-1533",
pages = "4929--4934",
abstract = "In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops. These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation. However, transliterating between older and newer texts is a laborious task when performed manually. We introduce two related methods to help solve this transliteration problem automatically. One approach is implemented, end-to-end, using finite state transducers (FSTs). The other is a hybrid deep learning approach, which approximately composes an FST with a recurrent neural network language model.",
}
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<abstract>In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops. These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation. However, transliterating between older and newer texts is a laborious task when performed manually. We introduce two related methods to help solve this transliteration problem automatically. One approach is implemented, end-to-end, using finite state transducers (FSTs). The other is a hybrid deep learning approach, which approximately composes an FST with a recurrent neural network language model.</abstract>
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%0 Conference Proceedings
%T Recovering Missing Characters in Old Hawaiian Writing
%A Shillingford, Brendan
%A Parker Jones, Oiwi
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F shillingford-parker-jones-2018-recovering
%X In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops. These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation. However, transliterating between older and newer texts is a laborious task when performed manually. We introduce two related methods to help solve this transliteration problem automatically. One approach is implemented, end-to-end, using finite state transducers (FSTs). The other is a hybrid deep learning approach, which approximately composes an FST with a recurrent neural network language model.
%R 10.18653/v1/D18-1533
%U https://aclanthology.org/D18-1533
%U https://doi.org/10.18653/v1/D18-1533
%P 4929-4934
Markdown (Informal)
[Recovering Missing Characters in Old Hawaiian Writing](https://aclanthology.org/D18-1533) (Shillingford & Parker Jones, EMNLP 2018)
ACL
- Brendan Shillingford and Oiwi Parker Jones. 2018. Recovering Missing Characters in Old Hawaiian Writing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4929–4934, Brussels, Belgium. Association for Computational Linguistics.