This article is a compilation of translation productivity tips.
How can I increase my productivity in the editor?
Don't focus on the suggestions. Typing the translation directly can often be faster than waiting for the suggestions to update. Consider the machine output during pauses while typing.
Ignore the machine suggestions if and when you want to. When you know the translation, and you're a fast typist, the machine assistance will only slow you down.
Post-edit if and when you want to. Use the hotkey Shift+Enter to insert the full suggestion. This technique can be faster when you only want to make minor changes.
Learn the advanced 'hotkey' shortcuts to avoid using the mouse. For more information on the editor shortcuts, see the Introduction to CATv3 article.
Learn the copy-source-to-target hotkey. The MT system will corrupt non-linguistic strings composed of numbers, equations, and punctuation.
For CJK input (Chinese, Japanese, Korean), we recommend the following tools:
- Enable your browser QA plug-in. In addition to Lilt’s internal QA checker, we recommend that you enable your external browser plug-in for spell checking. Please see a GIF below on how to enable your browser’s spell checker plug-in.
How can I improve Lilt's suggestions?
Lilt is an adaptive system, which means that it needs data (specifically, source/target pairs) for adaptation. Here are some rules of thumb for improving translation quality:
- Lilt works best when custom memories are created for each domain. You may have memories for software, legal, medical, etc. A common mistake is to aggregate all data into default memories. This limits the system's ability to adapt to any one domain.
- 10,000 segments / memory is about when you will start to notice increases in translation quality.
- Full-length sentences in memories improve domain suggestions the fastest. Short sentences and word dictionaries are more suitable for the termbase. See TM Size and Content Guidelines for more information.
- Google Translate / Microsoft Translator are broader domain systems trained on more data. Baseline (i.e., unadapted) quality of Lilt is competitive for some domains, better for some domains (e.g., medical), and worse for some domains (e.g., software strings).
- Fine-grained lexical distinctions for common words (e.g., "Party") take time to learn. The training data likely contains a given word millions of times; the system will need to see your preference many times to learn the distinction reliably. You should find that it learns rarer words and phrases faster.
What is the most efficient way to review?
In the editor, confirmed segments are reduced in size so that more document context can fit on the screen. Clicking on a confirmed segment will unconfirm it for editing. You must confirm the segment again to save it. When translating long documents, you may accidentally unconfirm or skip a segment.
To filter out segments, go to the File Menu, select View, and then select the segments you want to view. You can use these filters to quickly locate untranslated/unsaved segments.
The QA Mode is also ideal for reviewing and making quick edits.