We can “rage against the machine” all we want, but the reality is that MT is here to stay. Forward-thinking linguists, LSPs, and localization buyers have embraced this as the new normal and are exploring ways that MT can help them increase efficiency, lower costs, and yes, improve quality.
However, there is still a lot of stigma around machine translation. Not everybody is fully on board. Rather than having a meaningful conversation about how we can best put this exciting technology to use, we are still stubbornly stuck in a conversation about if we should be using it and when. Many in the industry still prefer the old fashioned approach of human translation, regardless of content type. This poses an interesting philosophical question for translators and for LSPs:
Are providers ethically obligated to inform their clients when they are using machine translation?
The answer to this question really depends on whether you consider localization as a product or a service.
Buyers who view localization as a product are really only concerned with the end result. They don’t need to know how the sausage is made, so to speak. As long as the end product is an on-time, good-quality translation, they are happy. According to this school of thought, providers should be able to use whatever tools they choose in order to get the job done.
If localization is viewed as a product rather than a service, then the conversation can really end here. LSPs are free to use machine translation and can keep any potential cost savings as part of their own margin. The only caveat is of course that this machine translation must be post edited to a point where it is indistinguishable from human translation. Human translation, after all, is the “product” that the client is paying for. So, as long as the LSP (or translator) can deliver the product on time, there’s not much more to talk about.
There are some buyers out there, though, who view localization as a service. They feel that they are not just buying the end result, but rather paying for a process. There are many reasons buyers may want this additional influence over how the process is carried out. Perhaps they have internal security requirements that need to be followed. Maybe they just want to make sure that the process is being carried out as efficiently as possible. One could argue the validity of any reasons given for wanting such control over the process, but ultimately any such arguments are irrelevant. The buyer creates the purchase order, and therefore the buyer can dictate the expectations for the engagement.
In such a relationship, buyers should be clear and upfront with LSPs regarding any expectations they have around machine translation. Is the vendor allowed to use machine translation? Is the vendor required to use machine translation? Does the buyer expect any discounts from using machine translations? If there are costs involved with training engines or running pilots, who pays for these?
Content translated by free MT platforms such as Google Translate and Microsoft Translator is not confidential. It is stored by the platform owners and may be reused for later translations. Responsible LSPs would never use any tool (whether that is MT, or any other technology) that would expose a client’s confidential information. Just as it is the responsibility of the LSP to deliver post-edited “human quality” translations, it is also their responsibility to make sure that the use of machine translation does not expose their client to any potential data security risks. If these risks cannot be completely avoided, then the LSP is absolutely ethically required to inform the customer ahead of time.
So how will these expectations change in the future? Machine translation will be more widely accepted as time goes on. Detractors of MT will soon disappear – they will either come to accept MT and its continued advancements, or their stubborn refusal to embrace the future will put them out of business. Currently however, we are still stuck on the if. Indeed, that is really what this blog is about –
Should we be using MT? What content types can we use it on? What level of post-editing should we use?
At least for now, the how part of the conversation is still the domain of the self-professed-language-technology-geeks (you know who you are). Only once we as an industry fully resolve the if conversation, can we really start focusing on the how.
Either way, twenty years from now, nobody will be talking about if they are using MT. The conversation will only focus on how they are using it. And that is when the fun really begins.
The language services industry is undergoing a profound transformation with the emergence of cutting-edge technologies such as ChatGPT and large language models (LLMs). These powerful language generation models have captivated the attention of businesses and language professionals alike, offering exciting possibilities for translation, localization, and content creation.
It’s already been six years now since Google revealed that Google Translate processes 146 billion words a day — three times more than what all the professional translators in the world combined can do in a month. That was 2016 and things haven’t really slowed down in the machine translation (MT) universe since.
We recently introduced you to the two- (or five-) second rule, which is essentially the reaction or decision-making time a linguist should spend judging whether to post-edit a segment of machine translation (MT) output or to retranslate it. This rule of thumb aims to help increase the linguist’s productivity when working with MT.
If you’re a driver, you’ve probably heard of the two-second rule. Staying at least two seconds behind any vehicle is considered a rule of thumb for drivers wanting to maintain a safe following distance at any speed. The two seconds don’t represent safe stopping distance but rather safe reaction time.