NüshuRescue: 35 sentence pairs teach GPT-4 Turbo a lost script

Nüshu was created four centuries ago by Yao women in Hunan as a secret script, but its use declined in the 1900s once women gained broader access to formal education, and many texts were lost or destroyed. Since the turn of this century, China has sustained an effort to save the script from extinction. Yang's work fits into that longer arc. As a child she learned a few words of Nüshu from her grandmother, and she is now applying AI to the problem. The project began with A Compendium of Chinese Nüshu, the most comprehensive, expert-validated collection of scanned scripts and their Chinese translations. From it, the team worked with annotators trained in computational linguistics to create 500 digitized sentence pairs, including newly mapped words.

The Dartmouth work sits alongside a broader examination of how existing language technologies treat underrepresented languages. Yang's recent paper, to be presented at the Nations of the Americas Chapter of the Association for Computational Linguistics conference, found that Google's LangID does not support most Native American languages and misidentified Navajo sentences as unrelated languages. Separately, linguistics professor Rolando Coto Solano built automatic speech-recognition models for Cook Islands Māori, Bribri, and Cabécar, motivated by a colleague's remark that she might die before finishing transcribing hundreds of hours of recordings.

Vosoughi's caution that these models can carry biases from dominant cultures, distorting or oversimplifying cultural identities, suggests the stakes rest on collaboration. He argues that active participation from native speakers and linguists is essential for linguistic authenticity, and that AI and community expertise are both fundamental. Whether NüshuRescue's low-data approach scales to other low-resource languages may depend on how closely communities shape that work, rather than on the model alone.