NushuRescue: Revitalization of the Endangered Nushu Language with AI

TL;DR

NushuRescue is an AI framework that uses minimal data to train models for revitalizing endangered languages, demonstrated on the Nushu script with promising translation accuracy and new datasets.

Contribution

The paper introduces NushuRescue, a novel AI-driven framework for low-resource language revitalization, including the first publicly available Nushu dataset and models that require minimal human input.

Findings

Achieved 48.69% translation accuracy on Nushu sentences

Developed NCGold, the first Nushu-Chinese parallel corpus

Generated 98 new translated sentences (NCSilver)

Abstract

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.