Choose a translation workflow
Learn how project specifications, service requirements and post-editing guidance address different decisions. Start with ISO 11669, then compare ISO 17100 and ISO 18587.
Start with the problem you need to solve
Evaluate a translation
Understand the difference between a quality framework, an assessment procedure and an acceptance threshold. Explore MQM alongside ISO 5060.
Move language resources between tools
Distinguish TMX for translation memories, TBX for terminology and XLIFF for localization content.
Make an interface work across languages
Explore language tags, locale data and dynamic messages.
From LISA to today
Follow the different paths taken by exchange formats, quality models and service standards. Our historical overview separates direct technical lineage from later work on similar problems.
- 1990 onward
- LISA industry cooperation
Shared production and interoperability problems - 2011
- Insolvency and continuation initiatives
A branching history, not a complete reset
Where AI fits
Our approach starts with requirements and evidence. AI may help identify potential translation problems or explain a technical concept, but a fluent response is not proof of conformity. We distinguish an automated suggestion from a confirmed error, and a research method from a published standard.
The guides are educational explanations. Official documents remain the reference for implementation and conformity decisions. Version checks are dated so that a historical specification is not mistaken for a newly maintained one.
MQM: Making Translation-quality Judgments Explicit
Source
“You can cancel until Friday.”
Target meaning
“You must cancel on Friday.”
These examples are original illustrations, not official MQM training cases.
Why this project exists
Association LISA is an independent educational project inspired by the questions that shaped the Localization Industry Standards Association: how can different tools work together, and how can translation quality be described clearly? We bring those questions into today's mix of human translation, machine translation and AI-assisted work.
Standards are easier to use when their purpose, limits and relationships are visible. Read our purpose and editorial commitments.