ChinAI #153: The Translator's Dilemma

A Douban Controversy on Machine Translation Sheds Light on China's Translation Dilemma

Greetings from a world where…

A great age of literature is perhaps always a great age of translations

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Feature Translation: Douban’s “One Star Movement” and the Translator’s Dilemma

Context: In my Year Three of ChinAI post, I wanted to make the case that stories about machine translation are newsworthy, and that they provide a needed counterweight to the U.S.-China AI race framing that permeates coverage and analysis on China’s AI development.

This week’s feature translation is an April 2021 article by China News Weekly (中国新闻周刊). It starts with a story about the Chinese translation of Benedetti’s The Truce. On the Douban platform (think: Goodreads), one reviewer criticized the text for exhibiting “heavy marks of machine translation (jifan[机翻]).” This netizen gave the translated text a two-star bad review. The translator replied: “jifan is a matter of professional ethics, and I carefully translated this word by word.”

Things escalated from there. A friend of the translator tracked down the Douban reviewer’s school and sent the administration an email. The school sought out the netizen and talked with them. Later, the netizen issued an “apology statement.” After this came out, other netizens got angry and launched a “One Star Campaign” on Douban to give the Truce translation low ratings. Ultimately, Douban suspended the rating of Truce, and discussions about jifan [机翻] have also disappeared.

Key Takeaways: this mini-drama provides a window into China’s translation dilemma

  • The jifan concept: jifan [机翻] is the abbreviation of machine translation [机器翻译], which generally refers to translation through translation software, as opposed to manual translation. To describe a translation as jifan [机翻] refers to the rigid expression of the translated text.

  • Zhang Butian, a professor at Tsinghua University who has translated many volumes on the science history, defends machine translation as a useful basis for translation. He cautions, “However, after using machine translation, it won’t work if you don’t change it. If machine translation can replace 80%, the remaining 20% ​​will be a test of the translator. The struggle with that 20% will determine if a book is translated well or not.”

  • Zhang gives an example from his own efforts to translate Descartes's Principles of Philosophy from the original Latin. In the process of comparing two English versions and a German version, he noticed that there was a word in Latin — studio — which the two English versions translated as “study” (研究、学习) and “effort” (努力、费力). The German version translated it as “studium” (研究、学习). Zhang went with “effort” after checking the context of the original text, Descartes is talking about the concept of human talent, which means "you can have it easily without effort.” The translations that used “research” were flawed.

  • The bar is high but the translators are few. Why? Three key reasons: low remuneration, translations don’t count in the academic evaluation system, and marketing of translations is not proportional to the quality of translations. Full translation gives great details, including an interview with Li Xia, who is an editor for the Commercial Press publishing house in China. Basically, translation has become an event similar to charity. Publishing houses rely on folks who have the skills and are willing to do it as a hobby.

  • “Most translators can only find their sense of accomplishment at the personal, spiritual level,” the article sums it up. The concluding passage is quite moving: “Not long ago, an article titled ‘Wen Jieruo: 93 years, growing old alone,’ published in a WeChat public account (谷雨实验室), stated: ‘If calculated by the manuscript fee, as one of the most outstanding translators in China, her manuscript fee is 80 RMB for a thousand characters, and a total of 32 RMB is earned for 8 hours of translation this day.’

    ‘It's boring to count money’ is how the 93-year-old translator responded.”

It may be boring to count, but money does help lubricate the translation process. So, now feels like a good time for this reminder: Please please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay support access for all AND compensation for awesome ChinAI contributors).

***FULL TRANSLATION: Douban’s “One Star Movement” and the Translator’s Dilemma

ChinAI Links (Four to Forward)

Must-read: Shifting Narratives and Emergent Trends in Data-Governance Policy

Amba Kak and Samm Sacks synthesize key trends in data governance policy in India, China, and the European Union. On data localization, they write:

Meanwhile, as US policymakers consider new tools to restrict access to American citizens’ data, the Chinese government has signaled that it may be amenable to allowing more flexible data flows out of the country. The draft Data Security Law mentions the free flow of data twice…According to analyst Xiaomeng Lu, the Chinese government may be more amenable, given the economic slowdown occasioned by COVID-19, to allowing more cross-border data flows as part of a broader effort to attract much-needed foreign investment. Broad data-localization requirements remain in place under the Cybersecurity Law regime—and anecdotal conversations with company executives in China suggest the government will continue to require that significant swaths of data be stored on local servers. Nevertheless, it is worth highlighting the paradox that at the very moment when US policymakers may be shifting more toward an acceptance of a form of US data sovereignty, in China, at least at the margins, some voices may be pulling in the opposite direction, primarily driven by a growing recognition of the economic utility of data. These developments prompt us to re-evaluate binary frames of analysis (such as open versus closed) which, over time, produce and sustain their own blind spots. The analysis in the body of this report demonstrates that flattening data policy into the “China model” or the “US model” (or even the European so-called “third way”) obscures both the contradictions within these national policies, and overlooks their inter-dependencies.

Should-read: QbitAI report on AI-based Judgements of Worker Productivity (in Mandarin)

Xsolla, a Russia-based gaming payment provider (used by Steam and Epic Games Store) recently fired 150 employees after conducting an AI-based productivity audit of the company. As this QbitAI report relates, the news became a hot search topic on Weibo. The report also connects this news to developments in China to monitor employees and prevent them from loafing around.

Should-read: The World’s Largest Computer Chip

For The New Yorker, Matthew Hutson profiles Cerebras, a U.S.-based startup that has a unique approach to building AI accelerator chips: make the largest computer chip in the world. The coolest part of this piece is how it distills technical intricacies into language people like me can understand. Here’s a passage on why mega-chip designs handle memory better:

In describing the efficiencies of the wafer-scale chip, Feldman offered an analogy: he asked me to imagine groups of roommates (the cores) in a dormitory (a chip) who want to watch a football game (do computing work). To watch the game, Feldman said, the roommates need beer stored in a fridge (data stored in memory); Cerebras puts a fridge in every room, so that the roommates don’t have to venture to the dorm’s common kitchen or the Safeway. This has the added advantage of allowing each core to work more quickly on different data. “So in my dorm room I can have Bud,” Feldman said. “And in your dorm room you can have Schlitz.”

Should-read: Engrave Danger - An Analysis of Apple Engraving Censorship across Six Regions

In a report published by The Citizen Lab, Jeffrey Knockel and Lotus Ruan investigate Apple’s content control of its product engravings service, which allows customers to print messages on the exteriors of products. They find:

Within mainland China, we found that Apple censors political content including broad references to Chinese leadership, China’s political system, names of dissidents, independent news organizations, and general terms relating to democracy and human rights. Moreover, we found that much of this political censorship bleeds into both Hong Kong and Taiwan. Some of the censorship exceeds Apple’s legal obligations in Hong Kong, and we are aware of no legal justification for the political censorship of content in Taiwan.

Thank you for reading and engaging.

These are Jeff Ding's (sometimes) weekly translations of Chinese-language musings on AI and related topics. Jeff is a PhD candidate in International Relations at the University of Oxford and a researcher at the Center for the Governance of AI at Oxford’s Future of Humanity Institute.

Check out the archive of all past issues here & please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay for a subscription will support access for all).

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