A few weeks ago I wrote a post on LinkedIn entitled: More proof why IMO machine translation is clearly not ready to replace professional translators. You can see the full post here.
This was a simple test that we ran after receiving an order from one of our clients. We ran the original German sentence through a number of free online machine translation tools and compared the results against our own translation. Our translation was done by our professional team without using any MT software.
The results were conclusive and all of the MTs botched up the translation. Now, we fed the original sentence into Amazon Translate, another NMT which is nowhere near as well-known as Google Translate.
Original sentence in German:
Herr Smith ist eine Person mit klaren Konturen, die schnell greifbar wird. Seine Verhaltensmerkmale sind deutlich ausgeprägt.
Amazon Translate result:
Mr. Smith is a person with clear contours that quickly becomes tangible. His behavioral features are clearly pronounced.
Mr. Smith is somebody with a clear sense of purpose who is readily available. His behavioral characteristics are very distinct.
Here too the results are conclusive and Amazon botched up the translation as well. Which is a bit surprising since in his introductory video from 2018, Amazon’s Yoni Friedman states the following:
… over the last year we had productized (sic) proprietary state-of-the-art neural machine translation engines and that was a leap ahead for us in terms of quality …. in essence statistical engines which are the ones that we used to have are basically a fancy lookup algorithm that makes decisions based on the probability of a certain word mapping in the source language mapping to the word to a different word in the target language. The problem with these statistical models is that predominantly they don’t understand context …. but that’s no longer the case with neural engines. …. generally speaking neural engines are built such that they mimic the way they’re inspired by the way that the human brain learns and processes information and that means a ton in terms of performance they understand quality and they understand context they understand the focus of the sentence and they understand morphology.
You can watch the entire presentation here:
If the Amazon NMT mimics the human brain and can understand context within a sentence, then why did it fail our translation test? If NMT understands context, then how did it translate the word Konturen as contours. Clearly this out of context.
The bottom line is that NMT, with all of its advances, is still not ready to replace professional translators. And any use of NMT in a professional environment will need close post-editing.
The state-of-the-art Neural Machine Translation (NMT) systems available today provide a fast, excellent basis for high quality translations. Spelling, grammar and general formulations have a low error rate and therefore the post-editing (PEMT) requires little effort.
For post-editing of technical texts, however, specialist knowledge must be available in both theory and practice in order to correctly reproduce or correct the terminology and functional descriptions of complex content. My own experience in my career puts me in a good position to perform efficient PEMT for technical translations: this includes project management in the construction of automatic production plants: mechanical engineering, electrical engineering, electronics, hydraulics, pneumatics. I have also served as a technical manager in the field of measurement and control technology: computer interface cards, and software development.
Google Translate is my best choice for technical PEMT
I use Google Translate for PEMT after I had tested different MTs. I found Google to be the software that translates with the least amount of errors. Currently, I work in combination with SDL Trados Studio 2015. I have also worked with other CAT tools as preferred by the translation agency who orders the work.
In principle, all file types that can be processed by SDL Studio are suitable for PEMT. Readable PDF files are converted to MS Word and often require rework. This also includes conversion of InDesign files into IDML file format for translation.
The quality of MT after post-editing is the same as human translation. The throughput depends on the content and context. 4000 words per day is typical.
DeepL is rated very positively and is considered to be better than Google Translate by renowned German newspapers. The translations are easier to read, more accurate, sound more natural and require less editing. I have tested DeepL on technical texts in my own projects. I have found that DeepL is not superior to Google Translate and that post editing is still required to the same extent as with Google Translator, if not more. This also applies to a review of technical elements within general body texts. See examples in the attached table.
The efficient MT supported by AI with the post editing, based on my technical knowledge and experience, and the integration in SDS Trados Studio 2019, results in a powerful combination that will allow me to offer a very competitive price point and significantly lower than the classic human translation rates.
Artificial Intelligence (AI) is the next big thing in the world of technology. It is nothing less than a revolution in the field of modern software. As any piece of technology before, AI too has posed certain threats to the traditional industries and setups. One particular threat that we will be discussing today is whether AI translators can take over the human translating business around the world?
AI has taken up a prominent place in the human language translation industry and is predicted to make remarkable achievements in the future. According to market research firm Technavio, AI technology will increase dominance in the global translation market in the coming years. Current trends show that AI will be able to replace as many as 500,000 translators and will be used by thousands of language translating agencies. Moreover, there is historical evidence that if machines do the same amount of work as humans then sophisticated machinery can replace humans.
The Current Scenario of AI Translators
In the current setting, AI translators are seen to be better than Google Translate in the major languages (like German, Italian, French and Spanish). One example of an AI MT system is DeepL, an AI translation system that boasts much better results than Google Translate. However, the big engines like Google are capable of translating many more languages than DeepL. More than 100 languages, out of which 32% are in a conversational mode whereas almost 40% are visual translations.
A relatively new technology being used is neural machine technology (NMT). The machine produces neural connections from the data provided which is used for language translation. The system is generally easy to work with. It can help you achieve translations which are of the same level as humans, in a less complex environment. Another advantage of using these systems is that you don’t need to provide training as in the case of having human translators. One company that is seen as a rising star in the use of NMT is Lilt.
Will AI Replace Humans?
It may sound like AI can take over the human language translation industry, but there are still several loopholes that will affect the industry in many ways. Probably the biggest challenge that the Artificial Intelligence Industry faces currently is the cultural and emotional gap. No matter how sophisticated the AI machinery is, it is not able to understand the human language of emotions neither is it able to bridge cultural barriers. Another issue that arises when using translators instead of humans is when the context is misinterpreted. A small mistake can change the entire meaning of the sentence.
Therefore, in the coming years, AI would have to be able to adapt towards the emotions, tone of voice, and the context in which the sentence is being said along with the cultural aspects. The AI today functions according to the algorithms and codes which are designed by humans. The algorithms are designed and incorporated in web apps using PHP and MySQL web developmentservices.
Undeniably, it is through human efforts that AI has been able to carry out translations in the first place. Therefore, this technology can’t replace humans any time soon. However, it will reduce the number of professionals working in the human translation industry.
Moreover, it is possible that even if AI does reduce the number of professionals in the human translation industry human could still add value to the industry. They can do this by bridging the gap between the human and the machine. This could ultimately help with precisely interpreting the language regardless of any cultural barrier.
It seems that humans will stay play an important role in carrying out translations. For effective communication to take place it is important to keep in view the various aspects that make up a language. A very important part of communication in the context, if the context changes the entire meaning changes of the sentence.
In the present day, machines can’t interpret the languages keeping in view certain aspects. Until then, human professionals will still be considered as the most reliable means of conducting translations.
Arslan Hassan is an electrical engineer with a passion for writing, designing and anything tech-related. His educational background and experience in tech has fueled his passion for writing cutting-edge content. He occasionally writes blog articles for Dynamologic Solutions.
In the translation and localization industry, translation prices are typically quoted on a per-word basis. So if a document or file has one thousand words, we multiply the number of words by the price per word to get the translation price per 1000 words. It is a simple pricing system which seems to work. Translators and LSPs are happy with this pricing scheme (as long as they are getting their price). Buyers are generally also happy with the translation price per word system since it gives them good control over expenses. A fixed price per project can be easily negotiated with no hidden costs or cost overruns.
Translation should be paid on an hourly not word rate according to @JochenHummel who helped introduce the word rate #tc41#Asling
This reality has prevailed for as long as I am in the business. But a few weeks ago I saw this tweet that proposed changing the basis for translation prices to a per-hour basis. This proposal came from none other than the inventor of SDL Trados, Jochen Hummel.
As anyone who uses Trados or any other TM software knows, the software counts the number of words in a file and classifies each text segment as a match/repetition, fuzzy match or no match. This word count is usually the basis for any price negotiation in the localization industry.
Why the need for change? PE(N)MT!
There is a ancient proverb that goes like this: “if it ain’t broke, don’t fix it.” So why would a man who bears major responsibility for the cost structure in today’s translation industry push for such a dramatic shift?
I put this and other questions to Mr. Hummel and this was his response:
In the post that Mr. Hummel wrote last year in the Multilingual magazine blog, he says that human translators will become obsolete and all of the translation work will be done by NMT (Neural Machine Translation) systems. The only human input in the workflow, per Mr. Hummel, will be by multilingual subject matter experts who will review the MT. These people will be paid by the hour.
What do freelance translators think?
Today, many if not most translators refuse to review MT output, which means that a per-hour rate may not be feasible. Here is an excerpt of a chat that I recently had with one of our senior translators, which I find to be typical across the industry:
… as I see it more and more only offer post-edit at xxx rates…. (I don’t work with them) .. and the majority are killing rates … albeit a minority then wants to pay for quality.
On the other hand, some translators are not opposed to move to a per-hour rate: many translators also provide interpretation services and are used to getting a per-hour rate. Interpretation better lends itself to a per-hour rate, as the work is done on-site either in a courtroom, a business office or in a conference setting; or on the phone, and that can also be easily measured in time-units. Indeed it is impossible to bill for interpretation work on any other basis but per-hour. Transcription services, the translation industry’s cousin, is also billed on a per-minute basis.
But even if we ignore the PEMT debate, most freelance translators prefer per-word pricing. They know that their expertise in the use of CAT tools and their control in the languages gives them an advantage-their throughput is high and they can therefore make much more per hour than any employer would agree to pay. The following tweet by Rodrigo Gonzalez supports this popular sentiment.
If you are paid by the hour, you will be paid less over the time, due to progress in MT. Pay the translator by the hour, while bill the client by the word.
For the translation services buyer, going to a per-hour rate is also problematic: how can we know how much time is actually spent on the task? If it is a per-hour rate, does it include coffee breaks? Trips to the bathroom? I don’t mean to get petty here but if we move to a per-hour rate, these questions become relevant. Whereas in a per-word rate, these questions are irrelevant. Let the translator work IN the bathroom for all we care, as long as the work is delivered on time and at the expected level of quality.
Mr, Hummel seems to believe that the entire industry will move to a per-hour rate, and projects will be negotiated in hours instead of words. This will require a high degree of trust between LSPs and buyers, and will also require an accepted scale of how much work can be accomplished in an hour.
Has (N)MT reached human parity?
Mr. Hummel states that NMT has reached human parity. But does this mean that NMT is as good as human translation? Well not exactly. According to Mr. Hummel, in a workflow where every translation is reviewed by a second translator as standard, the source of the first draft is not critical. A reviewer can revise a MT output in the same way that they can review professional human translation. And the final result, after review, will be the same.
Mr. Hummel is not alone in this line of thought. I heard some similar arguments by One Hour Translation‘s Yaron Kaufman in the Slatorcon conference in Amsterdam in November. Yaron also said that NMT will become good enough to replace human translators. And that software will be used to automatically determine which sentences need post-editing. Those sentences, and only those sentences, will be send to a subject matter expert.
In that kind of workflow, it does make sense to pay the reviewer per hour. But is that workflow close to reality? Will translators need to find another line of work?
The current state of the industry
The NMT revolution/vision that Mr. Hummel and others are proposing is still very far from reality. Indeed for someone like myself, who is working in a small LSP, it seems like science fiction. Human translation is still MUCH better than any MT that I have seen. And this probably holds true for the vast majority of all translation tasks.
To the extent that it exists today, the NMT revolution can only be found in large projects of at least several hundreds of thousands of dollars. For projects of such a large scale, training the NMT engines to produce good translation quality can be done cost-effectively, provided that the company/LSP has the resources to do this kind of work (which is far from being technologically simple).
And even in these scenarios, who is doing the post-editing work? It is hard to say. As we have already stated, most freelance translators do not want to do this work. Is it done by in-house staff? Perhaps, but can any company or LSP maintain multilingual subject-matter expert reviewers in all languages and in all fields? It sounds like the job that Noah had in getting all of the animals in the world into one Ark.
The NMT vision
The vision of people like Mr. Hummel and others is that what today is the privilege of a few large companies, will become available to more and more companies in the future. And cost barriers will be reduced. And the NMT servers will be cloud-based with easy access to all. So even small jobs will be run through the NMT and only require review by a subject-matter expert. Suitable online review tools will need to be developed for the subject-matter experts.
My own opinion?
I have been saying this for years: the NMT revolution may not happen in my own lifetime. It probably will happen in the future, but in how many years? Nobody can predict that.
The effect of the NMT revolution on translation prices
Whether we are talking per word, per page or per whatever: it seems obvious that translation prices are dropping. Is this because of the NMT revolution? That also seems obvious. As more progress is made in the field of NMT, and as the workflows based around PENMT improve, more downward pressure will be made on prices.
The effect of the NMT revolution on translation jobs
Professional translators will become more specialized and more skilled. They will need to become subject-matter experts in order to stay employed in the translation business. But that is not so far from the current state in practice: the good translators today usually stick to one domain (e.g., legal, medical, technical). Translators who lack industry and/or academic focus and depth will find it increasingly harder to get work in the future.
I love Amsterdam. This was probably my fifth time there. Localization conferences I like a bit less and have not gone to one in years. But my love of Amsterdam outweighed any other consideration and I decided to attend the one day Slatorcon Amsterdam 2019 conference on November 28, 2019 which was produced by Slator. And I am glad for this decision, having found the conference to be very good indeed. If you want more details, please continue reading this post.
The conference venue was excellent. The Andaz Amsterdam, Prinsengracht (a concept by Hyatt) is a lovely hotel situated on one of Amsterdam’s canals. This is a great hotel with beautiful rooms and fantastic service. I recommend this hotel highly.
The format of a one-day conference is a great idea. Having attended some LocWorld conferences which lasted for several days, I liked the quick-and-dirty aspect of this one. The talks were brief (about 20-25 minutes each) so my attention span wasn’t challenged too badly. There was ample time for networking and I can honestly say that I met as many of the 80+ delegates that I wanted to. The talks started at 12:30 so there were 1-2 hours of networking before the conference started. There was another round of networking in the middle and a three hour drinks session when the show wrapped up at 6:20 PM.
Food and Beverages
There was plenty of food and drinks during the networking sessions. The food looked very good and was in high demand by the attendees. I myself can’t attest to the quality of the food since I observe the laws of Kashrut and did not eat anything. The drinks session was well stocked with white/red wine and beer.
Conference Schedule Recap
The talks on the whole were good and here is a brief recap:
Andrew Smart got things started as the M.C. of the day and introduced the company that he co-founded, Slator. I found Andrew to be a very nice man with tons of goodwill and industry insight. Definitely a good guy to know.
Slator’s co-founder Florian Faes then took the stage and gave an impressive overview of the translation and localization industry. He covered the main players, the drivers, the industry verticals and spun his vision of the future of our industry. Strong stuff and very insightful.
The next talk was by Jimena Almendares of Intuit who spoke about her company’s foray into Mexico and discussed various aspects of localization of their accounting software. Interesting were the details about the local accounting laws and practices, which made the Mexican localization effort much more than just translating software resource files. Less interesting were details that had no relevance to the localization business (like how they smuggled in PCs for the Mexican employees who could not buy PCs locally for some reason). As an LSP, I got very limited benefit from this talk.
Andrew Bredenkamp of Acrolinx gave a very interesting talk about Artificial Intelligence and machine learning. Most of his talk was not relevant to the localization industry, but gave valuable insights into the current state of AI and where this technology is heading. I think that 25 minutes was way too little for Andrew and I would have welcomed an in-depth talk of several hours. Who knows, maybe it will happen in another time and space.
Patrick Prokesch of i5Invest gave an excellent presentation of Mergers and Acquisition (M&A) practices in general and in the localization business specifically. This was a powerful talk as it had very relevant information both for language industry company buyers and sellers. The information he discussed was particularly relevant to many people in the room as over 30% of the delegates were at the CEO level.
Esther Bond of Slator was short-changed so to speak and only had a few minutes due to scheduling issues. She discussed her activities as head of research at Slator. I had the chance to talk with Esther at the drinks session and heard about some of the exciting projects she is working on.
Harmut von Berg of LogMein spoke about the evolution of the 6-person localization team he is heading up. How the team consolidated after several mergers at the corporate level and how they managed to merge various departments in the process. I thought that he started out slow but gradually picked up speed to make a very effective presentation. He then ended with what I thought was a brilliant twist: he outlined some of the challenges that his department is faced with now and invited the conference attendants to propose ideas and help them with these efforts. One of the topics on his list was International SEO, which we will get to in a moment.
Florian got the panel started by discussing how large multinational companies approach the topic of international SEO. This topic seemed to energize the floor and several people had followup questions and comments. The consensus was that this is a very important issue. After all, what good is a localized website if it is not visible on the search engines? Andrea from Kayak said that her team worked with the internal SEO team to provide language support, but that the responsibility was with the SEO team. Vinicius of Bose and Al of Nike also seemed to indicate that this was not a top priority for their departments. Clearly, this is an an issue which requires close collaboration between localization and SEO teams. But ultimately, localization departments are not focused on this activity.
Another topic discussed in the panel was the use of MT in the localization work process. Clearly MT is being adopted and looked at by all of the large multinational companies. But the consensus on the panel was that this adaptation was in the early stages and did not yet go mainstream. My own thoughts on this are clear and it is my feeling that MT will not replace human translation in our lifetime.
I particularly enjoyed hearing Vinicius Britto (Bose) view on the customer-LSP relationship. LSPs should be solution-oriented. Customers should never have to chase the LSP and wait for the results to happen. I liked this no-nonsense approach.
The penultimate speaker was Michal Antczak of Paypal. I was a bit confused at a comment that Michal made at the start of his talk, that the views he is expressing are his own and are not those of Paypal. Nevertheless, Michal gave an interesting, somewhat tongue-in-cheek presentation about the relationship between LSPs and their customers.
The final talk was given by Yaron Kaufman of One Hour Translation. This was an effective talk on the selective use of NMT (Neural MT). Yaron made a compelling argument about the benefits of using NMT in a production environment and how companies can save between 30 and 70% of their translation costs. Yaron provided some metrics that supported his claim and made this workflow sound very real and feasible. What I did gather between the lines is that this approach is geared towards clients with millions of translation dollars in the their budget. One statement by Yaron that I found interesting (even as I disagree with it): MT will eventually, one day take over human translators. When? That Yaron could not predict.
Drinks and After Party
As I mentioned previously, there was a two hour drinks and networking session at the close of the conference. This was a very cool session which ended up lasting well over three hours. After so many talks and networking, we all deserved a relaxing drink. At about 9:30 PM the party relocated to Dante Kitchen and Bar which was a short 5 minute stroll from the Andaz. Did I say cool? Indeed it was. Unfortunately for me I had to cut out quickly and prepare for my early morning flight.
My networking experience
I met with a good number of the conference attendees. My own estimation of the breakdown: about 30% were LSPs, 30% were customers who buy localization services, 20% were tool vendors and the remaining percent were financial people and industry observers/consultants. One thing that stood out in my mind was the focus on website localization. I counted at least four vendors that sell website localization connectors-the magic software boxes that connect between a CMS and translation providers.
Josef Kubovsky is an industry consultant who invited me to visit him in Prague. I actually think I may take him up on his offer as we share numerous professional, personal and cultural interests.
I connected in a meaningful way with Balazc and Peter Farago of Smartling due to the Hungarian connection (my Mother was born in Budapest and Hungarian was my first language as a baby). We promised to set up a video conference soon to review their innovative website localization proxy software.
Lucy Taylor of Bayer had some interesting things to say about the life of a British expat living in Germany and about her work for the pharmaceutical giant. Lucy agreed with the idea that MT would replace human translators, at least for some tasks (like email and internal communications for example).
I enjoyed talking and drinking beers with Andrew Hickson of Ludejo BV, a Netherlands-based translation company. He gave me some interesting facts about life in Holland and the state of the localization industry in that country.
Amsterdam and Slatorcon were a great mix. I really hope that Slator does this in Amsterdam again real soon.
I just got an email from Google announcing that it is shutting down its Google Translator Toolkit (GTT) service. This is pretty big news, although I am not really sure why. Was anyone using GTT? Obviously not very many and the number of users was in decline, because otherwise why would they do that? I personally thought that it is a great service, especially since it is free. But truthfully, we used it very infrequently and never for any work of significant importance.
We are always dismayed when Google terminates a service, even though it is their right to do so. The Lord giveth and Lord taketh away. OK so Google is not the Lord but in the world of the Internet they are.
Perhaps one of the reasons for the shutdown is that ever since Google Translate can translate entire documents, including PDF files, people did not see the use for GTT’s online editor. Especially since the UI was cumbersome anyway. GTT has the ability to upload TMs and dictionaries, but many translation professionals probably felt leery about sharing their TMs with Google.
One company that may be upset by this news is Translated. This was the translation company that Google advertised on GTT for post-editing the Google MT. How many sales did Translated get out of GTT? Nobody knows except the people at Translated, but I am guessing that they got a considerable amount of business out of it.
What do you think about Google’s announcement? Our readers would love to hear about it.
Here is the announcement from Google.
Google Translator Toolkit launched over a decade ago to help our users, translators, and the world create and share translations. When we first launched, there were few web-based options for translation editors, but now there are many great tools available, including Google Translate, which will continue to be available and is unaffected by this. As a result, we’ve seen declining usage for Translator Toolkit over the past few years. So now, after many years and billions of words translated, we’re saying goodbye to Translator Toolkit. A warm thank you to our users around the world.
Download your data
Prior to the shutdown on December 4, 2019, your data can be downloaded directly in Translator Toolkit (see how). Shortly after shutdown, you can download all of your data at Google Takeout.
Delete, share, or unshare your data
If you would like to share or unshare your data, this can be done prior to shutdown directly in Translator Toolkit (see how).
To delete data that you own in Translator Toolkit, simply select the Glossaries, Translation Memories, or Translations you would like to delete and click Delete. For Translations, you also need to click Trash, select translations, and click Empty trash.
Thank you for supporting Translator Toolkit over the years. To learn more, visit our Help Center.
In this famous clip from Goodfellas, the character played by Joe Peschi seems to take offence at Henry’s accusation of him being a funny guy. Normally a gangster like Tommy would have whacked Henry instead of just laughing it off as a big joke.
Why did I put this clip in a blog post? Well first of all I LOVE Goodfellas. It is a classic. As an avid reader, and especially keen of the mafia genre, I read Nicholas Pileggi‘s book Wiseguy way before Martin Scorsese made it into a blockbuster movie. But there is another reason I wanted to put in this clip.
The translation industry is huge and one of the fastest growing industries. The global market for translation services is over 20 Billion USD a year. But let’s face it-the translation business is not exciting and relatively little is known about it to people outside of the business. It lacks sex appeal so to speak. But there is one aspect of the world of translation that garners wide interest: funny translations that come out of machine translation software and that are used in public. Some of these translations are so ridiculous, so outrageous, that people can’t help laughing. If Joe Pesci had a translation business, he would probably be saying “are we here to amuse you?”
Here is a video clip that we have licensed that shows some funny Chinese menu translations. (some of the cooking techniques look cool and the food looks delicious-this restaurant obviously puts more effort into the food than in the translation of their menus).
Here is a YouTube channel called Translator Fails. It has about 1 million subscribes. This channel is dedicated to creating songs and video clips which are parodied by using Google Translate instead of the regular lyrics.
Here are some other funny Internet pages devoted to bad translations:
Since it was introduced in 2006, Google Translate has become the leading online machine translation application. It is estimated that over 100 Billion words are translated each day by Google Translate. It translates into and out of over 100 languages. And the most amazing fact of all: it is free! So with such a valuable tool at their disposal, no wonder so many people are using it.
While having said this, everyone knows that machine translation tools like Google Translate should not be relied on for translation tasks that require a high degree of accuracy. Like medical report translation and legal translation. Critical tasks such as these should be handled by a professional translator or translation services company.
Still, there are a ton of very useful ways to use Google Translate. Here is the top 10 list (actually it is 11 but who’s counting?):
Google Translate allows you to translate files, including PDF files. To use this feature, click the Documents tab in Google Translate, select the file and the languages.
2. Using Google Translate TTS as a proofreading tool
Say that you have written a key sales proposal in English and you are about to email it to your client. You proofread the proposal ten times but you want to be sure that there are no errors and that it sounds just right. Just paste the text into Google Translate and (no need to click Translate), click the text-to-speech button on the English language side. The text will be read back to you, offering you another chance at fine-tuning.
3. Using Google Translate for translation QA
Google translate provide you with the option of performing free back translation of texts in order to check the quality of a translation. The workflow is simple. You received a translation from your translator or translation company. Even though you don’t know the target language, plug it into Google Translate and check the English translation. While the back translation may not be very accurate, it will certainly show you if there are missing words or sentences in the translation.
4. Using Google Translate as a Proxy Server
Sometimes you may want to hide behind anonymity on the web. Or if for example a certain website is blocked by your company. In those situations you can use Google Translate as a proxy server. Here is how to do it:
1. Go to Google Translate. 2. Select the source language as anything but English. 3. Enter the website you want to access. 4. Select the target language to English. 5. The website link is clickable in the target language side. Click the link to surf the website behind Google.
5. Using Google Translator Toolkit to translate entire documents
Google Translator Toolkit (GTT) has been around for about 10 years, yet it is off-the-beaten path so to speak. You can use GTT to get quotes for post-edited machine translation services, to translate entire documents and post-edit the result manually, and to translate documents using your own Translation Memory (TM) and terminology list.
The quickest way to translate an entire document using GTT is:
4. The file you uploaded is displayed in list of documents. Click to open the document. 5. Select File-Translation Complete from the menu. 6. Click OK when asked “Are you sure you want to complete.” 7. Select File-Download to downloaded the translated file.
6. Use Google Translate to get sexy accents
Google Translate can do sexy voice accents. Paste in some English text into Google Translate. Select the source language as French. Then click the sound icon. Voila, a French woman is talking. Now select Italian. Bene. Get the picture?
7. Use Google Translate Phrasebook to store common phrases
You can store translation of phrases in the phrasebook for quick and easy access. After you translate the phrase, click the star on the right-hand side of the translated phrase. To access the phrasebook, click the star at the bottom the Google Translate window.
8. Translate YouTube Videos
YouTube makes it easy to translate videos to the language of your choice by adding subtitles. Once you enable the subtitles feature on videos with voice narration, you will be able to watch your videos while displaying translated subtitles.
1. To translate YouTube videos by adding subtitles:
2. Go to YouTube and open the video you want to translate. Click Settings.
3. Click the Subtitles/CC menu option to enable subtitles.
4. Select the default language (English in our case) to enable subtitles.
5. Now select Settings-Subtitles/CC again. The Auto-translate menu option is now available and you can select any of the languages supported in Google Translate.
9. Translate quickly inside the browser’s address bar
Here is a quick way to translate phrases inside your browser’s address bar. Type in the phrase you want to translate following by “in language.” Here is an example. The translation is displayed and you can play it back using TTS (not available for all languages).
10. Use Google Translate in Whatsapp
Here is a YouTube video that I found that shows you how to integrate Google Translate into Whatsapp and use it to quickly translate Whatsapp messages.
11. Download the Google Translate App and use it as a travel accessory
The Google Translate smartphone app has many uses as a travel accessory in your foreign travel. You can download language packs for offline use (like if you are climbing the Andes and find yourself with no Internet); use the camera for instant translation of signs and menus; use speech-to-speech for conversations. And more. The app is very intuitive so just download it and play around to learn how to use it.
This debate has been going on for years. Will machine translation (MT) become good enough to replace human translators? Will professional translators need to find another line of work? If recent history is any proof, the answer is no. MT will not replace professional human translators in our lifetime. This notion is shared by many people in the industry. Here is a supporting quote from a recent (February 2019) Techcrunch article on MT:
The problem with machine translation, when you really get down to it, is that it’s bad. Sure, it won’t mistake “tomato” for “potato,” but it can’t be trusted to do anything beyond accurately translate the literal meaning of a series of words. In many cases that’s all you need — for instance, on a menu — but for a huge amount of content it simply isn’t good enough.
The simple fact is that as much progress as has been made in the last 10 years, MT is (a) not reliable enough to replace human translators and (b) it is unsuitable for 98% of the mission critical tasks needed by today’s customers.
Brief History of MT
First there was rule-based MT (RBMT), which was deemed as not ready for prime time. Then about 10 years ago, Google started the statistical MT (SMT) revolution which was supposed to bring MT to the next level. Which it did, but the promise did not come to fruition and the models did not deliver human quality translation. Then came neural MT (NMT) which was definitely the greatest MT technology ever invented. Now, companies are touting Deep NMT based on Artificial Intelligence (AI) which will surely replace professional translators.
The MT experts keep on telling us that MT will be ready really soon. But that ‘really soon’ has already stretched out into a long time with no real end in sight. And the demand for professional human translators seems to be growing steadily and outperforming other professions. According to a USA Department of Labor report in 2016, translation jobs are expected to grow by 18% in the next 10 years.
Employment of interpreters and translators is projected to grow 18 percent from 2016 to 2026, much faster than the average for all occupations. Globalization and large increases in the number of non-English-speaking people in the United States will drive employment growth. Job prospects should be best for those who have professional certification.
Drivers of MT
As it has been from the start, MT is a scientific endeavor which combines several advanced fields: computational linguistics, mathematics, computer models, statistics among others. MT technologists are geek scientists who speak in a language of their own. If you suffer from insomnia, go to a conference in MT and you will be put to sleep in no time.
Advances in MT are driven by one thing and one thing only: money. Companies are hoping to capitalize on advances in MT in order to make money.
The companies who are promoting MT as a business are either tech giants (like Amazon, Microsoft, Google and Facebook) or LSPs. The tech giants want to get their hands on cheap translation services since they have gigantic amounts of text that need to be translated. The tech giants also have the resources to train the MT systems to provide good translations for their own purposes. LSPs are using MT so that they can offer low cost translation services to customers with large translation budgets.
The tech giants have peripheral objectives as well. For example, both Google and Microsoft have been developing speech-to-speech translation systems for commercial use. Microsoft and especially Google are monetizing access to their MT APIs. And MT is helpful for search engines who want to gain market share.
What Can and Can’t be done with MT
As stated previously, MT can be improved by training the system with large corpora (plural of corpus) of aligned text. So if a company like Microsoft trains their MT to translate Microsoft’s knowledge base, then the system will yield good results. But since it takes huge resources both financially and technically, almost all companies can’t undertake this kind of work. Likewise, using an MT-savvy LSP for PEMT (post-edited MT) work is also tricky. Firstly, the LSP will need to make a huge investment in their MT which will be passed along to the client. And if you do not have huge amounts of texts then the price may be comparable or even higher than human translation. And even after all of that, a human translator is needed to post-edit the MT.
The following kind of translation work can’t be done with MT:
– Certified translation for official purposes. This kind of work requires a signed statement of accuracy by the translator, something that can’t be done by MT software.
– Books/Novels/Poems. In the 1970s and 80s my uncle, Ivan Sanders, had translated a number of Hungarian novels into English. I remember that these projects involved numerous face-to-face meetings with the author so that that translator (my Uncle) could gain an in-depth understanding behind some of the characters in the novel and to understand the author’s thought process on certain parts of the novel. These projects took about two years to complete. No way a machine can translate a novel at the same quality level.
– Scanned images on PDF files. At GTS, most of the orders for online translation services are for PDF files. Some of them are scanned documents which are not great quality. These kind of files require prep work and getting MT into the loop may not be efficient.
– Critical legal documents. If you were buying a house in France for $5 Million and needed to translate the contract, would you trust a machine translation?
Scientists and engineers funded by the private sector will continue to develop MT systems which will represent breakthroughs in science and technology. Quality will improve but only marginally. Demand for translators will increase due to the increase amount of content that needs to be translated.