There is a reluctance across many LSPs to adopt machine translation. Many believe it will decrease the quality of work and will weaken the industry by taking away jobs. As someone who has been in the localization and translation industry for several decades, I can tell you that this resistance is not new.
In 1993, when I introduced CAT tools to the agency I was working with at the time, I saw the same resistance to adopting new technology. Translators worried that relying on translation memory would make their work more repetitive, and decrease the pay and amount of work available. Twenty-five years later, the same arguments are being made about machine translation.
Of course, machine translation is not a new concept. Its rising popularity now is due to recent advancements in the technology. Newer neural machine engines are remarkably much better than rule-based engines that were cutting-edge twenty years ago.
When someone argues that MT engines produce poor results, the first thing I ask is when they last tested machine translation. Many in the industry are still basing their opinion on results from years ago, which are no longer valid. The reality is that machine translation is cheaper, faster, more secure, and increasingly better quality. LSPs that do not adopt this quickly dominating technology will not be able to compete in this new market.
In this article, we’ll look at the arguments against machine translation and how they are changing with new advancements in machine translation. We’ll also outline a process for LSPs to adopt a machine translation process.
Argument #1: When you factor in post-editing, machine translation isn’t faster or cheaper
Even though newer MT engines are producing results that, in some scenarios, come close to matching human translations, there is still a warranted reluctance to make use of unedited machine translations. Most MT output requires post-translation editing.
There are conflicting opinions regarding the efficiency of post-editing. Some translators make the case that translating from scratch can sometimes be faster than post-editing although this perception is being altered by higher quality MT output.
Even when human post-editing is performed, the cost still tends to be at least 50% cheaper than the traditional translation process.
Argument #2: MT is only appropriate in specific situations
There’s a misconception that machine translation is only useful for getting the gist of a text when, in reality, it can be an extensive part of nearly any translation project no matter what source documents you are using.
In most situations, it’s not a matter of whether machine translation is appropriate, but rather how much post-editing work will need to be done in addition to the MT. With the appropriate process in place, machine translation can be used to respond to most translation requirements.
Without post-editing: MT can be used to help in content analysis like eDiscovery in the legal industry or for basic comprehension for internal communication.
With minimal or no post-editing: it can also be used to process large non-critical customer-facing content repositories, such as support forums or eCommerce applications.
With post-editing and additional QA steps: it can be used in most scenarios where translation is needed.
There are still cases where machine translation is not recommended.
- For example, the technology will not work well in situations where translations need to be highly adapted for a specific cultural message, like marketing materials.
- Machine translation might also not be advisable for the translation of complicated legal and commercial contracts without a thorough review of the output.
- In addition, machine translation without post editing and a rigorous QA process might not be appropriate for documents in critical sectors like biotech, pharmaceuticals, and equipment where lives could be endangered.
Argument #3: MT engines are replacing translators
As we see in other industries that are being disrupted by technology, jobs are not necessarily being lost as much as they are evolving.
“The life of a freelance translator, once based on securing projects and clients, and establishing a reputation for quality, has now shifted to offering a variety of services.” Elanna Mariniello and Afaf Steiert explained in tcworld.
Technology is creating new positions and requiring new skill sets. In our industry, translators are moving into the role of post editors, correcting the output of machine translations as opposed to completing translations from scratch.
Translators have voiced concern over decreased pay and workload. However, this issue is much larger than the impact of machine translation. There is increasing pressure to lower the costs of most processes in the localization industry. Competition from lower-cost locales has had effected translators’ income, as well. To address these issues, the industry needs to strengthen its perspective on the value of trained linguistic professionals.
Translators are not the only ones impacted by machine translation. The technology changes the pricing and language service capabilities which impacts clients, as well. When an LSP adopts an MT process, they need to be prepared to educate translators and clients alike. What follows is a step-by-step guide for implementing an MT process.
How to implement a machine translation process
We recommend four steps in implementing a machine translation process with your translation team:
- Research and evaluate different engines
- Choose a CAT tool that works with the engines
- Update your SOP and educate your translators
- Revise your pricing structure and educate your customers
With many MT providers on the market, choosing the most appropriate engine for each project can be a confusing process.
There are three types of machine translation engines on the market: rule-based, statistical, and neural. However, the majority of providers are moving to neural machine translation engines (NMT), which is widely considered the most advanced and fastest improving. NMT uses an artificial neural network to predict word sequences based off of a text corpora. This is similar to how statistical engines work, but NMT requires significantly less memory and learns more efficiently.
However, there are cases in which NMT is still not available for specific language pairs. When this is the situation, in most cases, a statistical engine will be your best option.
The option to use an engine that can be trained either interactively or in batches should be explored. In certain cases, they can improve the output and increase post-editing productivity — for example, when your source documents contain very specific terminology. For many LSPs, generic engines will be sufficient.
Evaluate the quality of the engines
We suggest researching popular engines and selecting three or four that work with the language pairs you need.
Our partner organization Intento provides integrations between popular machine translation engines and CAT tools. They also conduct and publish studies on the quality of MT engines per language pair.
The quality of an engine varies by language pair, so you will most likely need to use different engines for different projects. There may also be slight variations based on topics; however, with the best-known engines, these variations are limited.
Once you’ve narrowed your list down to your top two to three choices per language pair, take the output of 50 or so pages, and get feedback from your translation team.
If one engine is noticeably better for the current project, then that’s the engine you should use. If two or more engines produce very similar results, go with the cheapest option.
Re-evaluate engines every six to twelve months
Because engines rapidly evolve, the engine that produces the best quality may change. For instance, in the Intento January 2019 report, Konstantin Savenkov mentioned that “for 21 language pairs [that they tested], the best MT provider has changed since July 2018.” Retest different engines to ensure you’re still using the best one for your needs.
Machine translation engines are now integrated into most CAT tools on the market. You want to look for a tool that’s going to give you access to many machine translation systems. Some tools lock you in into one or two engines that may not be ideal for your projects or language pairs.
We also recommend using CAT tools that are optimized for post-editing and combine MT, TM, terminology management, and strong collaboration features to maximize post-editing and team productivity. PEMT tasks generally require large teams of translators, and seamless collaboration is vital.
How you use machine translation may depend on the contracts you’ve signed with your customers. For instance, if they’re paying for a human translation, you could still use machine translation to offer suggestions to your translator that they can choose whether or not to use. (This feature is built into our platform’s CAT tool). MT suggestions can help a translator complete the project faster while leaving them free to translate using their own knowledge and discretion. However, your Quality Assurance team should be on the lookout for translators who are too heavily influenced by the output of the MT engine.
In response to the decreased project costs and turnaround time (and improved quality of machine translation), we believe that the industry will continue to move to a complete machine translation and post-editing process. Having a process in place will prepare you for this fast-approaching future.
There are two defined types of post-editing: light and full.
- In a light post-editing process, the editor makes only minimal changes to increase the comprehension of the text. No stylistic changes or fluency improvements are required.
This type of post-editing is generally limited to content used for internal communication within a company, or for communication with a short lifespan, like forum posts or emails. Light post-editing can also be useful in situations such as legal eDiscoveries where the translation requester only seeks content confirmation or for eCommerce product or service descriptions that will be retired.
- Full post-editing is a more extensive process where the post-editor not only corrects obvious mistranslations but also improves the style and fluency of the MT output.
Full post-editing is especially useful in situations where the MT process is new or for language pairs that have a lower quality score. Industry studies point to productivity improvements around 40% with full post-editing, although these numbers can vary greatly.
Output quality should be negotiated with the client to decide whether additional editing is needed following post-editing.
In 2017, an ISO standard for PEMT was published. Post-editing efficiency can be improved in conjunction with the use of translation memory.
Training your translators to be post-editors
The biggest challenge to post-editing is the resistance to the process from translators. Although I’ve found that the younger generation of trained translators has now been exposed to MT as part of their curriculum and more open to working as post-editors.
This change in job requirements may not work for all translators; however, not all customers may be ready to move to a machine translation process either. In other words, you may still have use for translators resistant to post-editing.
The biggest benefit of machine translation to your customers will be reduced prices for your services. However, even with lower costs, not all clients are readily sold on the new process. You need to be prepared to explain the advantages of the technology, as well as correct their expectations.
Some customers may assume that a machine translation will be perfect and won’t understand the added expense of post-editing, where other customers will assume that machine translation is simply another added cost that will affect the quality of your results.
Machine translation can create new product lines that were not previously cost-effective, such as translating comments on blog posts. In projects like this, expected translation quality may be much lower.
New potential services, pricing structures, and expectations around the quality of a translation will require dialogue and education on the different roles that machine translation and post-editors can play depending on the requirements of a given project.
Updating your prices
When you successfully adopt a machine translation process, you should be able to increase your margin and decrease costs to customers at the same time.
Start with your costs and margins. How one factors the cost of a translation will change with machine translation. In general, post-editing is priced considerably cheaper than traditional human translation.
Pricing post-editing tasks is still a challenge for the industry for lack of updated productivity metrics. Where translators are most-often charged per word, post-editing machine translated content may be billed via time spent, corrections made, or by a review of the quality level of the post-edited content.
The discussion is ongoing about how translators should be paid for post-editing work. It is very similar to the discussion that happened twenty-five years ago when we had to devise new pricing structures based on using translation memory tools.
Updating your quality standards and assurance process
Depending on the expectations of a given project, moving to an MT process may not change your quality assurance process.
However, in some cases, a client may decide that an unedited MT output (at a much lower cost) is acceptable for lower priority content.
As you edit your existing contracts or enter into contracts with new clients, these agreements will continue to be based on a combination of cost and necessary quality standards per project.
If your clients use external reviewers, you will need to align with them around any changes in your quality standard agreements.