Interpreter's Memory

MTPE workflows follow this structure: Machines generate content, and humans step in to fix, review, or approve it. This model assumes the human’s role is to clean up machine output.

But in this setup, the machine still leads. The human “reacts to” the machine. The human’s role is essentially editor and filter, which could lead to issue including

◼Human efficiency is influenced by machine quality.

◼The machine’s phrasing may skew the human’s understanding of the source.

◼Empirical studies confirm that post-editing affects cognitive processing. 

And critically: if the human disappears, content still exists. The human is optional. That tells you something about the design philosophy.

In many real-world cases, especially when users are not linguistic experts, they may use raw machine output—because the system allows that.

But in a truly human-centered workflow, the human is indispensable. When we center the human, machines don’t dictate the flow. They support the human’s thinking and knowledge growth, and streamline retrieval and knowledge management

A useful lens for thinking about this is to ask:

【What are humans good at, and what are machines good at?】

◼ Humans: context, experience, creativity, judgment.

◼ Machines: speed, pattern matching, fuzzy search, structure.

An example of a human-centered use case is, like, a translator’s personal knowledge archive：a knowledge base of past work, auto-tagged, cross-linked, searchable. The point isn’t for AI to evaluate the human’s work. It’s to help the human see their own thinking more clearly, and support future decision-making. And in that case, the machine is not the producer. It’s a tool.