Traditionally, writing, translation, corpus and development belong to different teams or departments, and their operations do not intersect, or they are just one-way tasks passed from upstream to downstream of the process, forming process breakpoints, data silos, and individual experience loss. The reason for this is that data knowledge does not flow smoothly in the process and cannot form a data loop to achieve continuous value-added. This is like storing a large amount of cash in one's home for a long time, which cannot realize the flow and value-added of financial assets. Based on the data-driven model of "industry + language", the process nodes are "lubricated" by data, just like the parts of a car's mechanical transmission system cannot be without lubricant, so that data can flow and be shared smoothly in the process nodes, and through language quality assurance and through language quality assurance and reading experience metric models and tools, the explicit values of technical writing, such as accuracy, readability, shorter TTM, and enhanced product competitiveness, are realized. Specifically, by integrating the knowledge base (such as internal resources of terminology and external resources of encyclopedia) and the writing environment, the traditional check-as-you-write mode is transformed into the knowledge push mode, thus ensuring the accuracy of terminology; by solidifying the rules of style specification into check items and integrating them with the writing environment, the traditional post-checking and interception mode is transformed into the pre-correction mode, thus ensuring the readability of works; the Intelligent writing and Writing for MT, based on controlled language methodology, can shorten TTM and reduce costs. In the translation process, the accuracy and linguistic style of translations can be ensured through shared knowledge base, style specification, and language checking tools.
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