A 35-example few-shot GPT-4-Turbo pipeline reached 48.69% exact-match translation accuracy on held-out Nushu sentences and produced a 98-sentence silver corpus, alongside the first public Nushu-Chinese dataset.
Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In this paper, we examine and analyze the challenges associated with developing and introducing language technologies to low-resource language communities. While doing so, we bring to light the successes and failures of past work in this area, challenges being faced in doing so, and what they have achieved. Throughout this paper, we take a problem-facing approach and describe essential factors which the success of such technologies hinges upon. We present the various aspects in a manner which clarify and lay out the different tasks involved, which can aid organizations looking to make an impact in this area. We take the example of Gondi, an extremely-low resource Indian language, to reinforce and complement our discussion.
fields
cs.CL 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
NushuRescue: Revitalization of the Endangered Nushu Language with AI
A 35-example few-shot GPT-4-Turbo pipeline reached 48.69% exact-match translation accuracy on held-out Nushu sentences and produced a 98-sentence silver corpus, alongside the first public Nushu-Chinese dataset.