Learning From Failure: Data Capture in an Australian Aboriginal Community
Éric Le Ferrand, Steven Bird, Laurent Besacier
Abstract
Most low resource language technology development is premised on the need to collect data for training statistical models. When we follow the typical process of recording and transcribing text for small Indigenous languages, we hit up against the so-called "transcription bottleneck." Therefore it is worth exploring new ways of engaging with speakers which generate data while avoiding the transcription bottleneck. We have deployed a prototype app for speakers to use for confirming system guesses in an approach to transcription based on word spotting. However, in the process of testing the app we encountered many new problems for engagement with speakers. This paper presents a close-up study of the process of deploying data capture technology on the ground in an Australian Aboriginal community. We reflect on our interactions with participants and draw lessons that apply to anyone seeking to develop methods for language data collection in an Indigenous community.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 85064ccf-e44e-472c-b7f6-ba0914c99119Cited by top-tier papers3
- The Zeno's Paradox of 'Low-Resource' LanguagesHellina Hailu Nigatu, Atnafu Lambebo Tonja, Benjamin Rosman, Thamar Solorio et al.EMNLP 2024 · 10 citations
- The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource LanguagesJenalea Rajab, Anuoluwapo Aremu, Everlyn Asiko Chimoto, Dale Dunbar et al.ACL 2025 · 3 citations
- Local Languages, Third Spaces, and other High-Resource ScenariosSteven BirdACL 2022
Related papers
- Local Word Discovery for Interactive TranscriptionWilliam Lane, Steven BirdEMNLP 2021
- Situating Automatic Speech Recognition Development within Communities of Under-heard Language SpeakersThomas Reitmaier, Electra Wallington, Ondrej Klejch, Nina Markl et al.CHI 2023 · 9 citations
- Not always about you: Prioritizing community needs when developing endangered language technologyZoey Liu, Crystal Richardson, Richard J. Hatcher, Emily Prud'hommeauxACL 2022 · 36 citations
- Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the SpeakersManuel Mager, Elisabeth Mager, Katharina Kann, Ngoc Thang VuACL 2023 · 15 citations
- Must NLP be Extractive?Steven BirdACL 2024 · 4 citations
