<strong><em>Post-Editing</em> </strong>

When I was growing up, if something was important we’d say “It makes the world go round”. This expression perfectly describes translation. In humanitarian work I’ve seen how translation literally saves lives. But also in commercial, technical, legal, scientific, health and educational fields, it’s translation that literally makes the world go round.

Ironically, it’s our very importance to global communications that is spurring more and more research into voice recognition and machine translation. There is too much to translate and there isn’t always a big budget for it. Translation is so important that Google’s powerful artificial intelligence engine, named “Deep Learning”, is being applied to machine translation quality. Now that Deep Learning has beat the international Go champion, translation is its most important real world challenge.

What this means for us is that post-editing is going to be an even more valuable skill to have.

<em><strong>What is Post-Editing?</strong></em>

At its simplest level, post-editing is similar to what most translators do today when they work with a productivity-enhancing CAT tool such as MemoQ, Déjà Vu or Trados: it involves using your language skills to accept as is or improve a pre-translated segment. The goal is to arrive at the same end quality.

Post-editing machine translation is not meant to generate lower quality (unless it is “light post-editing”; see below). The goal of PEMT (post-edited machine translation) is to achieve the same quality as a fully human translation, in significantly less time.

<em><strong>With MT, Training is Everything</strong></em>

All machine translation is based on human translations through the translation memory (TM) segments and bilingual glossaries that are used to train it. For maximum post-editing productivity (that is, speed and effectiveness), the machine translation engine must be based on good content and be managed by an experienced linguist. This is actually trickier than it seems – which is why an engine trained by an inexperienced linguist or based on bad quality translations can be such a headache to post-edit.

People have told us that Lexcelera’s MT engines are among the best-trained they have ever worked with!

<em><strong>Post-Editing Techniques Depend on the MT Engine</strong></em>

Another factor affecting post-editing productivity is the type of engine that was used. There are three main types of engines: Statistical (SMT), Rule-Based (RBMT) and Hybrid.

A statistical engine such as Google, Moses or Microsoft Bing is trained, ideally, on millions of segments of human translation and monolingual documents. For post-editing, what’s important to know here is that the sentences may be fluid, but wrong: for example, a negative may have been left out, or the terminology may be incorrect for the domain.

With SMT output, then, the post-editor’s job is not to be swayed by those fluid sentences and to verify meaning and terminology.

With a rule-based engine such as Systran, Lucy, Reverso, Promt or Apertium, the post-editing task is the exact opposite. RBMT engines are governed by grammatical rules, to which domain vocabulary is added. If the engines are properly trained the terminology will be spot on, but the sentences will be awkward constructions. In this case the post-editor will have to work on sentence structure to make the translation read fluently, but can treat the terminology with confidence.

A hybrid engine like Systran’s combines RBMT’s reliable terminology with SMT’s fluency.

<em><strong>How MT Engines are Chosen</strong></em>

What engine to use will be determined by how much training data is available and also by language pair: today, morphologically rich languages like German, Russian and Japanese don’t tend to work well in a purely statistical approach. But for the more exotic languages, RBMT engines don’t exist; in this case, SMT, which is based on parsing existing translations rather than grammatical coding, is the only approach possible.

Because your post-editing approach will depend on the MT engine that generated the translation, your Lexcelera project manager will advise you on whether you need to be especially vigilant on terminology or sentence fluidity. And he or she will also tell you what quality is required: top quality, obtained by what is known as “full post-editing”, or understandable quality, obtained by what is known as “light post-editing”. Most of Lexcelera’s work requires full post-editing for your best quality, but it could happen that the customer simply needs a more superficial edit so that the translation is understandable, if not perfect.

Those of you with valuable post-editing expertise should find your skills in greater demand in the very near future as powerful artificial intelligence engines like Google’s and Microsoft’s get better at machine translation. It’s estimated that we are translating just 10% of the content that needs to be translated. Given that “every two days now we create as much information as we did from the dawn of civilization up until  2003”, I believe this figure is closer to 1%. Translation is important and we need more of it, much more. With better MT our work will contribute even more to making the world go round.

<em>Lori Thicke, CEO Lexcelera</em>