EVIDENCE-GRADED TOOLS FOR READING CHINESE AI RESEARCH
TOOLS
The Name Engine · pf-names

A Chinese name engine, done properly

Match a person across the scripts and dialects a Chinese name travels through, and get back not a yes or no but a weight of evidence and a reason you can read.

Most tools treat a name as a string. They either merge two different people or split one person into many.


The Problem

A Chinese name reaches an English-language database through a lossy pipeline. 王伟 and 王薇 both become "Wang Wei". One person can appear as 张家欣, "Jiaxin Zhang", "Chia-Hsin Chang" (Taiwan, Wade-Giles), "Cheung Ka Yan" (Hong Kong, Cantonese) or "Teo Kah Hin" (Singapore, Teochew). The surname order flips, and English given names get bolted on ("Jasper Lee Po Lung"). A tool that compares these as text will get it wrong in both directions.


The Approach

Every name is parsed into all the readings it could have, and each comparison returns three things: a level (identical characters, a reading that fits, initials that fit, uncertain, partial, or a conflict); a weight of evidence, a log-likelihood ratio the identity matcher can add to other evidence; and a reason a reviewer can read. Three rules keep it honest.

Romanisation Systems Covered
SystemWhereExample
Hanyu PinyinMainland China张家欣 → Zhang Jiaxin
Wade-GilesTaiwan, older literature张家欣 → Chang Chia-Hsin
Cantonese (HK government style)Hong Kong, Macau鄧樹榮 → Tang Shu-wing
Hokkien, Teochew, HakkaSingapore, Malaysia陈文庆 → Tan Boon Keng

Cantonese readings come from the Unicode Han Database (Jyutping for 30,096 characters), converted to the Hong Kong government spellings and their common variants. The Singapore and Malaysia dialect table was learned from people with both an English and a Chinese name in Wikidata, then reviewed row by row.

How Well It Works

Measured on the held-out half of the data: people whose dialect spellings the engine never saw while it was built. The chart is recall, the rate at which the same person is matched. Two additions moved these numbers: the dialect table cut Singapore and Malaysia misses from 12.5% and 15.2% to 5% or less, and the Cantonese readings raised Hong Kong matches from 13% to 37%.

Same-person match rate by region: mainland China 82%, Taiwan 56%, Hong Kong 37%, Singapore 25%, Malaysia 14%, with at most 0.3% false matches

Known Limits

See it run on your own names, or get code access.

Parallel Frontier is written and built by Caroline Swartz. Chinese language & politics (NYU), Russian politics MSc (KCL), Mandarin SIGINT spanning defensive and offensive cyber operations, security operations leadership, short- and long-form reporting in five languages, AI safety research, and an MA in computational linguistics in progress (UCL). parallelfrontier.ai