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NushuRescue: Revitalization of the Endangered Nushu Language with AI

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arxiv 2412.00218 v4 pith:Y4NXGNR2 submitted 2024-11-29 cs.CL cs.LG

classification cs.CLcs.LG
keywords nushurescueendangeredlanguagesnushurevitalizationncgoldchallengedeveloped
verification ladder T0 review T1 audit T2 compute T3 formal
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The preservation and revitalization of endangered and extinct languages is a meaningful endeavor, conserving cultural heritage while enriching fields like linguistics and anthropology. However, these languages are typically low-resource, making their reconstruction labor-intensive and costly. This challenge is exemplified by Nushu, a rare script historically used by Yao women in China for self-expression within a patriarchal society. To address this challenge, we introduce NushuRescue, an AI-driven framework designed to train large language models (LLMs) on endangered languages with minimal data. NushuRescue automates evaluation and expands target corpora to accelerate linguistic revitalization. As a foundational component, we developed NCGold, a 500-sentence Nushu-Chinese parallel corpus, the first publicly available dataset of its kind. Leveraging GPT-4-Turbo, with no prior exposure to Nushu and only 35 short examples from NCGold, NushuRescue achieved 48.69% translation accuracy on 50 withheld sentences and generated NCSilver, a set of 98 newly translated modern Chinese sentences of varying lengths. A sample of both NCGold and NCSilver is included in the Supplementary Materials. Additionally, we developed FastText-based and Seq2Seq models to further support research on Nushu. NushuRescue provides a versatile and scalable tool for the revitalization of endangered languages, minimizing the need for extensive human input.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. N\"ushuVoice: Reviving the Voice of Endangered N\"ushu with Pitch-Aware Text-to-Speech

    cs.CL 2026-06 unverdicted novelty 7.0 of 10

    NüshuVoice releases the first sentence-level Nüshu TTS dataset and shows that an F0-conditioned VITS model using five-level pitch notation outperforms baselines on spectral fidelity, pitch accuracy, and intelligibility.

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