arXiv cs.CY· Masaru Yamada·· 12 小时前AI 评分31
译者是 AI 的隐形教师：翻译记忆库、版权与语言数据的政治经济学
Translators as Invisible Teachers of AI: Copyright, Translation Memory, and the Political Economy of Linguistic Data
AI 导读
一篇 arXiv 论文指出，翻译记忆库（TM）和平行语料为机器翻译提供了高价值监督训练数据，支撑了 SMT、NMT、Transformer 架构及多语言 LLM 的发展，但译者的劳动被当作合同交付物买断、切分为技术对象，并按版权法作为“信息分析”数据处理，失去署名与归属。
正文 · 原文
Abstract:This paper examines how the labour of translators has been transformed into foundational data capital for the age of artificial intelligence (AI). Translation memories (TM) and parallel corpora preserve a one-to-one correspondence between source and target text and therefore constitute extraordinarily valuable supervised training data for machine translation. The development of statistical machine translation (SMT), neural machine translation (NMT), the Transformer architecture, and multilingual large language models (LLMs) cannot be disentangled from the accumulation of such translation data. And yet, translators' renditions have been bought as deliverables under contract, segmented as technical objects, and processed as "information analysis" data under copyright law -- losing their moral, creative, and economic attribution to the translators who produced them. The paper develops two concepts to capture this process. The first is appropriation without consumption: a mode of use in which works are not read, viewed, or listened to, but only mined for statistical features -- a use that is legitimated under Article 30-4 of the Japanese Copyright Act. The second is the invisible teacherisation of translators: the process by which translators, through the construction of translation memories, post-editing, and quality assessment, have functioned as teachers of AI without recognition as such. Drawing on the data supply chain that runs from translators through language service providers (LSPs) and platforms to model developers, on a comparative reading of Japanese, European, and United States legal frameworks, on the distinction between open and proprietary AI models, and on the premium status that human-generated data has acquired in the era of model collapse, the paper asks what translators are actually afraid of, and points toward concrete directions for redistributive design.
| Comments: | 11 pages; comments welcome. v2: corrected bibliographic errors and misattributed claims; updated U.S. case law (as of October 2026) |
| Subjects: | Computation and Language (cs.CL); Computers and Society (cs.CY) |
| Cite as: | arXiv:2605.24842 [cs.CL] |
| (or arXiv:2605.24842v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24842 arXiv-issued DOI via DataCite |
Submission history
From: Masaru Yamada [view email]
[v1]
Sun, 24 May 2026 03:21:41 UTC (14 KB)
[v2]
Fri, 23 Oct 2026 04:58:45 UTC (15 KB)
来源：arXiv cs.CY · arxiv.org