A Finite State Aproach to Interactive Transcription

William Lane, Steven Bird


Abstract
We describe a novel approach to transcribing morphologically complex, local, oral languages. The approach connects with local motivations for participating in language work which center on language learning, accessing the content of audio collections, and applying this knowledge in language revitalization and maintenance. We develop a constraint-based approach to interactive word completion, expressed using Optimality Theoretic constraints, implemented in a finite state transducer, and applied to an Indigenous language. We show that this approach suggests correct full word predictions on 57.9% of the test utterances, and correct partial word predictions on 67.5% of the test utterances. In total, 87% of the test utterances receive full or partial word suggestions which serve to guide the interactive transcription process.
Anthology ID:
2022.fieldmatters-1.1
Volume:
Proceedings of the first workshop on NLP applications to field linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Oleg Serikov, Ekaterina Voloshina, Anna Postnikova, Elena Klyachko, Ekaterina Neminova, Ekaterina Vylomova, Tatiana Shavrina, Eric Le Ferrand, Valentin Malykh, Francis Tyers, Timofey Arkhangelskiy, Vladislav Mikhailov, Alena Fenogenova
Venue:
FieldMatters
SIG:
Publisher:
International Conference on Computational Linguistics
Note:
Pages:
1–10
Language:
URL:
https://aclanthology.org/2022.fieldmatters-1.1
DOI:
Bibkey:
Cite (ACL):
William Lane and Steven Bird. 2022. A Finite State Aproach to Interactive Transcription. In Proceedings of the first workshop on NLP applications to field linguistics, pages 1–10, Gyeongju, Republic of Korea. International Conference on Computational Linguistics.
Cite (Informal):
A Finite State Aproach to Interactive Transcription (Lane & Bird, FieldMatters 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-3/2022.fieldmatters-1.1.pdf