Intelligent content at stc17

This week I’m attending STC Summit 2017, the annual conference of the Society for Technical Communication. These are my notes from one of the sessions at the conference. All credit goes to the presenter, and any mistakes are mine.

Val Swisher presented a session called “The Holy Trifecta of Intelligent Technical Content”. The trifecta comprises structured intelligent technical content, terminology management, and translation memory. With these three, technical writers can efficiently produce content for multiple channels, for an international audience.

Val explained each of the three elements (structured content, source terminology management, and translation memory) and the magic that happens when you use them all together. Using the three together makes content development better, cheaper, and faster.

Structured authoring

Val walked us through the original content development process, where a writer wrote the content, then passed it off for translation and desktop publishing. This process was slow, expensive, and gave the writer little control.

In a structured environment, the author writes smaller chunks of content (sometimes called topics) and checks it into a CMS. The information product (PDF file, web page, book, etc) are a collection of these chunks in a certain order. In theory, you should be able to combine the chunks in different orders and arrangements for different content products.

Structured authoring should therefore produce more deliverables through content reuse, create consistency, and support multichannel publishing.

The content itself is separated from the eventual publication style and medium. Desktop publishing is a thing of the past.

Each individual chunk is independently translated. Each chunk is now in the database with its related translations.

There are a few problems to solve. In particular, terminology. For example, what do you do to a button: Click, Click on, Tap, Select, Hit… We’re not consistent in our use of terminology in our source.

Source terminology

We need to manage our source terminology. People do it in various ways, such as via a document or style guide, via reviews (tribal knowledge), or via a specific tool.

Val emphasised the importance of picking one term for a particular thing or concept. For example, when talking about a dog, choose a word: doc, pooch, hound – it often doesn’t matter which term you pick, provided you’re consistent.

No-one reads style guides! Everyone wants to, because we all want to do a great job. But no-one has the time. Also, it’s hard to know whether the word you’re about to write is a managed term.

We need a way to manage the words we’re using and how we’re using them, that we don’t have to go and look for. The information must be pushed to us.

It’s almost better not to have structured authoring if you don’t manage your terminology. We split the topic development amongst a group of writers, which leads to greater problems with consistency. Val showed us a screenshot from an automated terminology tool, which allows you to define preferred terms, banned terms, etc, and then prompts the authors when they use a deprecated word.

Translation memory

Val asked the audience whether we had translation memory (TM), whether our company owned the translation memory, whether we had more than one translation vendor, and whether those vendors shared the same memory. She stressed the importance of owning your own translation memory.

Translation memory (TM) is one of the automated tools that the translation vendor uses. If something in the source content has already been translated, the tool pops up the translation. This is because the translations are stored in a database called the translation memory. The bits of source content are stored as translation units, which are phrases, usually more than a word.

This makes translation cheaper. If you say the same thing in exactly the same way each time you say it, the tool pulls up the same translation as used the first time. This is called a 100% match. Note that a 100% match doesn’t cost zero dollars. To have no charge, you have to have an in-context match.

Val emphasised that you should make sure that what’s in the TM is pushed to the writers, although she knows of very few companies that are doing this. That way, writers would know what’s already been translated and be able to ensure we use the same terms when developing new content.

Ideally, there’s be an automated link from the translation memory to the terminology management system. But that’s complicated, and doesn’t happen often.

Tying them together

Val discussed the intersection of three technology areas:

  • Structured authoring – write it once, use it many times.
  • Terminology management – say the same thing the same way, every time you say it. Be as boring as you can and as simple as you can.
  • Translation memory – use already-translated terms in your source content.

This takes a lot of setup and maintenance.  But it’s worth it.

Conclusion

Val’s presentation was funny, engaging, and informative. She had the audience nodding and laughing throughout the session. Thanks Val!

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About Sarah Maddox

Technical writer, author and blogger in Sydney

Posted on 8 May 2017, in STC, technical writing and tagged , , , , , , , . Bookmark the permalink. 8 Comments.

  1. I hate the way the terms structured writing and topic-based authoring get conflated. They are really entirely orthogonal to each other. Structured writing is about applying structure to content as you write. You can write entire books using structured writing. (I have). Topic-based writing is about writing small chunks of content that meet a single need. (What those needs are varies.) You can do topic-based writing in an unstructured environment, such as a wiki. (I have done that too.)

    You can, of course, do topic based writing using structured authoring, but they are not the same thing and have no necessary relationship to each other.

  2. Thanks so much for your post, Sarah! It definitely captures my presentation well.

    I’m glad you found it of value!

  3. Excellent summary, Sarah. Great throughts, Val, though I wouldn’t restrict this to intelligent content. These are generally good practices to follow whether creating intelligint, plain, or downright moronic content.

    At the the recent tc-world (tekom) event in Bangalore, I had the pleasure of listening to Dion Wiggins introduce Language Studio, by Omnicien Technologies that runs on neural machine translation. I was really interested in the fact that their translation process first fixes inconsistencies in the source content. This includes spelling, terminology, AND sentence construction. I thought this feature might interest companies even if they didn’t translate to a target language. I don’t know if Omniscien Technologies plans to provide that sort of a service.

    • Hallo Edwin
      It’s nice to “see” you again! The Language Studio sounds interesting. I agree with you that it’d be useful outside the translation use case. In fact, I may be a little cautious about having my docs adjusted as part of the translation process, but would be interested in seeing it done as a separate process. That’d allow us to do the necessary QA and cost assessments independently too.
      Cheers
      Sarah

  4. I just finished delivering a tech comm training today for freshman tech writers and I mentioned the very same things about terminology and also suggested looking into a solution automatically checking content for banned terms. What a coincidence 🙂

  1. Pingback: STC Summit 2017 wrapup | ffeathers

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