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.- Anthology ID:
- D18-1533
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4929–4934
- Language:
- URL:
- https://aclanthology.org/D18-1533
- DOI:
- 10.18653/v1/D18-1533
- Cite (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.
- Cite (Informal):
- Recovering Missing Characters in Old Hawaiian Writing (Shillingford & Parker Jones, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/D18-1533.pdf