For many, the pandemic has brought stress to the forefront of our minds: worries of catching the coronavirus, job security, maintaining a healthy work-life balance and home schooling are just a few possible stressors. However, it is important to note that it鈥檚 not always simple to sniff out the cause of stress 鈥� and even if you manage to do so, battling those feelings can feel like an uphill struggle with no end. Especially in the translation industry, with its tight deadlines and the pressure to perform to a very high standard, relaxing and switching off can prove challenging, if not unthinkable.

Despite the fact that stress is hard to completely eliminate, there are a few tried and tested ways to help reduce it. Unlike the abundance of other articles on this subject, I won鈥檛 extoll the virtues of yoga, exercise and taking breaks 鈥� sometimes these just aren鈥檛 feasible, especially if motivation is lacking or you don鈥檛 have the time (or both).

What I would like to do though is take the opportunity of , which runs from 10鈥�16 May 2021, to outline a few methods that I and a couple of colleagues use to lessen our stress, on those days where it might all be a bit too much.

1. Cutting back the caffeine

In the translation industry, it鈥檚 common to find yourself overwhelmed by a heavy workload with a relatively small amount of time to get through it: a situation which can cause stress and anxiety to arise even in the calmest of people. Personally, I鈥檓 aware that if my morning starts off this way, I should in all likelihood limit myself to just the one morning coffee, as any more will cause my heart to race and make me feel jittery. My brain then interprets this as anxiety (as the two sensations are very similar) and tricks me into feeling more stressed than I actually am.

Obviously, it鈥檚 not always easy to reduce your intake of coffee, but limiting your caffeine intake is definitely worth a try if you are anything like me! Switching to a herbal tea could be another good option as well.

2. ? Breaking down your work into smaller chunks

If giving up coffee is not for you, I鈥檝e found that gaining a sense of control over your day can prove equally useful. My favourite (if not slightly convoluted) way to do this is by making a list of all of the things I need to do within a certain time frame, i.e. by the end of the day. I then set a timer on my phone for half an hour and try to complete as much as I possibly can before the alarm rings, after which I treat myself to a cup of tea or a snack, or I go and annoy my pet cat for a minute.

What this does for me is to provide a sense of focus. Instead of panicking and trying to remember all the tasks I need to complete along with their deadlines, the list helps to provide an overview of my work while marking tasks as 鈥渃omplete鈥� and incentivising myself with a treat provides a nice sense of satisfaction.

3. ? Getting away from your screen

Sometimes, it鈥檚 hard to see the bigger picture, especially on busy days when you feel like there is no room to breathe, let alone think. Deadlines, issues with technology and tricky technical texts can all contribute to this feeling of futility, making it feel like there鈥檚 little chance of escape and that the universe is out to get you.

To combat this, Mary-Anna, an Account Linguist at Sandberg, suggests that 鈥渋f you鈥檙e feeling like everything is insane and you have too much on your plate, walking away from your screen for just one minute and simply breathing helps. One minute is not going to bring all of your jobs crashing down but it’ll help you breathe and focus.鈥�

 4. ? Using music to help you focus

Charlotte, another Account Linguist, has a slightly different approach to mitigating stress, involving music: 鈥淲hen I have a particularly heavy workload that requires me to power through and concentrate quite a lot, I have some special playlists that are made up of tracks with no lyrics, quite repetitive stuff that won鈥檛 distract me too much; I play them as a 鈥榯reat鈥�. On the one hand, it gets me motivated to focus on my projects because I鈥檓 looking forward to hearing the music, and on the other hand, my brain is now trained to be in a calm and focused mood when I hear that music.鈥�

It could be that these methods don鈥檛 work for you. Everyone鈥檚 brain processes stimuli differently, and it could take a modicum of fiddling about to find a strategy which matches your way of thinking and your lifestyle. However, it鈥檚 worth giving one or two of them a try, even if only to find out what works for you and what doesn鈥檛.

Stress and the issues it causes can be serious if left unaddressed. This article is intended to help you think about stress you may be experiencing and offer some tips that could help reduce it.

If you鈥檙e feeling the burden of stress is too heavy to bear, I would advise speaking to your GP or a mental health professional. There are many concrete ways they can help and guide you. After all, stress is something that鈥檚 going to be ever present in our lives to a greater or lesser degree: the vital thing is that we learn to deal with it as best we can.

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In this final part, Mattia shares his thoughts on how translators can use MT as a tool 鈥� and how STP is going about it.

You mentioned that editing machine translation output is a skill all of its own for a translator. How does it differ from translation?

I鈥檇 say that machine translation post-editing is not really that different from translation these days. Of course it鈥檚 quite different from translating a text from scratch in a word processor, but I think聽sometimes people forget that translators very often work with translation memories (TMs) nowadays. So they don鈥檛 necessarily have a blank slate even without MT.

How does working with machine translation compare to working with translation memories?

It鈥檚 somewhat similar; essentially, you are editing matches in both cases. In the case of TM matches, a tool will suggest translations of similar sentences that have been translated before and stored in a translation memory file attached to the project.

The translator might, for example, have a 95 per cent match where only the punctuation is different to that of the sentence they are looking at 鈥� or perhaps there is just one word that is different. Translators have become used to editing TM matches. An MT match is often much less accurate, but it鈥檚 a starting point.

How does the process of post-editing differ from the process of translation? What does a translator need to know before starting this?

The biggest problem, particularly for inexperienced editors, is bearing in mind that MT output is the work of a machine, not a human. You can鈥檛 trust a machine the same way you can trust a translation memory match from a previous translator.

This seems like a fairly straightforward distinction 鈥� the clue is in the name. But many struggle to make this distinction.

Another thing is the amount of training, because there is very little training and resources available. This is why we recorded webinars for our freelancers, and all our in-house translators have received training too. We can鈥檛 give people MT output and expect them to just deal with it.

Machine translation post-editing (MTPE) is not as intuitive as people think: training, experience and knowledge are necessary. It鈥檚 really helpful to try to understand why the machine produces the output it does 鈥� but this is something that requires an understanding of technology.

From my perspective, it鈥檚 really helpful to have very specific feedback from translators, as training the engine requires precision.

You can and should be able to influence the engine quality 鈥� you can train the engine as well as the translator. If you 鈥減ut yourself in the machine鈥檚 shoes鈥�, things start to fall into place.

STP is certified in MTPE according to the ISO 18587 standard. Why is this?

It shows the amount of effort we鈥檝e put into learning, understanding and using this kind of technology as a company. And this isn鈥檛 just the case for the technology team 鈥� our production teams have put in a lot of work as well.

Adhering to the standard is something we are doing with everyone鈥檚 best interests in mind; we鈥檙e trying to contribute to making a positive difference in the industry.

The standard is basically a set of guidelines 鈥� I would describe them as a collection of best practices. Basically, they raise the bar for everyone in the industry. Companies that care about these standards can promote them and counter the misuse of MT technology.

Do you think there is a lot of deliberate misuse of MT in the industry?

Some, certainly. There are companies trying to pass off raw MT output as translation and sending it out to vendors as regular revision projects, for example. But these agencies know what they are doing 鈥� and the revisers can spot this kind of thing a mile away.

There are some companies that lack information on the MT that they are using 鈥� or that they are expecting their vendors to use. They simply don鈥檛 know how good the MT output is, since they don鈥檛 have in-house people proficient in the relevant languages to check and provide feedback on it. STP only generates output for languages that we can check in-house. That way we know exactly what sort of quality it is.

Would you say that MTPE is faster than translation without MT?

There has been a lot of talk about MT improving productivity, but most of the research on this is done with very few people who are not working with strict deadlines. These circumstances do not really reflect the way in which translators work in the commercial world. The studies often make flawed assumptions too.

AT STP, we can test the effectiveness of MT as a tool internally. We have a lot of information on our translators and they already work with deadlines and under pressure, which makes them ideal test subjects.

How do you measure something like this accurately?

We have data based on edit distance 鈥� how different the final, edited output is from the raw, unedited MT output. In general, it seems that people are more productive with MT than without, though that doesn鈥檛 necessarily mean the quality is good.

How does STP measure machine translation productivity?

Basically, we are making an effort to track productivity gains. We are doing this by recording how much time projects where no MT is used take compared to MTPE tasks. It鈥檚 not the perfect metric, but we need some hard data on MT and how useful it actually is.

Is the difference that MT makes reflected in STP’s translation rates?

For us, it鈥檚 really not as simple as that. In terms of efficiency, we want to be sure we know what we are actually getting.

I see a lot of nonsense numbers being thrown around. For example, MTPE is supposedly 50% more efficient than translation. Even if there are time-saving aspects to this, it鈥檚 not realistic to put it in those terms.

The productivity increase needs to be contextualised as well. There are often other aspects that slow the work down, such as special instructions that need to be read and implemented.

At STP, we want to take into account the total effort people put into a project. And, at the end of the day, you still have to do the work 鈥� the engine just provides suggestions.

Based on the feedback we鈥檝e had from our translators, so-called 鈥渉igh fuzzies鈥�, meaning TM matches that are ranked as a 75% match or higher by the CAT tool, are almost always more helpful than MT matches. So when our translators use MT, they are only using it for sentences where there are no 鈥渉igh fuzzies鈥� available. So far, this has been a useful approach for us.

The one thing that is perhaps different at STP is that we have over 70 in-house translators who can help us develop our approach.

How does having a large team of in-house translators help?

They are all professionals who have been trained to post-edit MT output, and they are happy to help us develop the engines further. I can understand how a smaller company might find this harder.

At STP, we work with a small number of languages on a daily basis, so that means fewer engines to worry about than some other companies.

If people are not happy with something, we can try to improve it 鈥� or abandon it if that doesn鈥檛 help. We can go back to the drawing board.

How do you work with the in-house teams in practice?

We have one person for each target language who is our go-to person for MT development. So far, we鈥檝e had this for all the Scandinavian languages and English. I work with these MT 鈥減ower users鈥�, or MT experts, when I need feedback.

It鈥檚 easy to do this with translators who are genuinely interested in the process and the technology. The technology would not really be worth much to us without our translator teams 鈥� their effort is crucial in all stages of the process.

Learn more about聽machine translation here.

The post Making machine translation work for us 鈥� part 3 appeared first on sa国际传媒.

In part 2 today, you can read Mattia鈥檚 thoughts on the newest development within MT technology, which has people predicting the end of translation as we know it: neural machine translation.

So, Mattia, what is neural machine translation? And what鈥檚 with the hype?

Neural machine translation (NMT) is essentially the same as statistical machine translation (SMT), but there is more of a 鈥渂rain鈥� behind it. NMT can potentially improve itself over time and learn on its own.

The vital difference is the amount of data an NMT engine needs 鈥� which is way, way more than a traditional SMT engine.

Essentially you have nodes that establish connections on several levels, such as the context and clause level. This makes NMT more flexible 鈥� it can analyse shorter bits of text, so the flow of the target output tends to be better.

We often joke that when you train a SMT engine, you鈥檙e training a machine. Neural is more like teaching a child a language 鈥� or bringing up a bilingual child! While the engine is learning, it makes plenty of mistakes along the way, of course.

How does NMT output compare to previous technologies?

The first thing is better fluency. The output from an NMT engine tends to be more idiomatic, meaning it reads more like natural language. More often than before, the engines are able to use an appropriate synonym or expression within the context of the sentence at hand.

Adapting to the immediate context helps a lot with languages like German or Danish that have complex syntax. Subclauses separated by commas are interpreted more accurately, for instance.

One key aspect of NMT is that it interprets morphology better. For example, a verb in the first person would usually be rendered as an equivalent verb in the first person. So, if the source says I write in English, the target would be 箩’茅肠谤颈蝉 in French, with the correct ending. If the engine cannot recognise the person, it will give you the next best thing, which is usually the verb in the infinitive (for example 茅肠谤颈谤别). This is then easy to edit manually.

We talked about training MT engines before. How does training NMT engines differ from SMT and RBMT (rule-based machine translation) engines?

NMT needs a lot more data than SMT and RBMT. The biggest hindrance to adopting NMT in the first place is that smaller companies can鈥檛 find enough data. To get started, a NMT engine needs at least 10聽million words of data.

By comparison, an SMT engine can be good as long as the data is good; you can get a decent SMT engine with as few as a million words.

So, NMT is much more about quantity over quality in this respect! Just to put this into perspective, our Finnish NMT engine has 140聽million words right now.

Another thing is training the engine. NMT engines tend to resolve issues themselves based on data you add 鈥� they come up with rules. You can still add rules if you want, but sometimes this can be counterproductive 鈥� you risk doing too much, being too strict.

For example, a German to English translator at STP was wondering why the German-English engine was translating personal names. It turned out that these specific names were also all meaningful nouns (such as the surname 惭眉濒濒别谤, which means 鈥渕iller鈥�). This means we had to consider the need for a new rule carefully, since the noun 惭眉濒濒别谤聽(capitalised, like all nouns in German) might come up in a text about millers later.

In this case, leaving it alone and replacing the translated name manually each time was the easiest thing to do. It鈥檚 an easy mistake for the translator to spot. You see the error, you check the source and you fix the output accordingly. No one is expecting the output to be perfect.

Will NMT replace human translators?

A hundred times, no! A technology like this is only as good as the use you make of it.

I could imagine a situation where a company with several offices around the world would need internal communications, such as short messages from HR, translated very quickly. These could be run through a specialised engine the company has developed and trained for that purpose. The translation wouldn鈥檛 be high quality, but people would get the gist. But this would be internal communication and nothing customers would ever see 鈥� just for information purposes. Another example is using MT to translate large amounts of survey responses for market research purposes.

But this is not how it鈥檚 been used or how it is perceived by many. Many early adopters of machine translation have misused the technology, which has affected its reputation.

The key thing is to use MT output appropriately. Professional translators can use it as a tool. It has even been suggested that post-editing output produced by a MT engine could be a separate service one provides as a translator, as long as you know what you are doing.

Translators are not being replaced; it just that the way they work is changing.

Does NMT technology work differently with different language pairs?

It seems it has done, for some language pairs. For instance, English-Japanese is working quite well, which I find quite impressive. Nordic languages have not been concentrated on much, as they are smaller.

German output seems to suffer from the syntactic complexity and strictness of the language, and capitalisation is a huge issue. Romance languages seem to be working fairly well; NMT engines seem to cope with their verb paradigms and tenses.

Rather than the language pair, the issue is more the target language itself. Obviously Finnish has been a bit of a headache for us.

Why is Finnish more difficult for NMT?

I think morphology is more important, the grammatical complexity within words. The engine will have a harder time discerning the different parts of a word.

The Finnish case system is a real challenge for the engines. Each case ending is a variable, and you need to consider this variable in every scenario. Finnish has 15 different cases and there are several possible endings for many of those cases, which means there are a lot of potential alternatives.

So far, I have only heard of one company making a Finnish engine work really well in the terms of the morphology and fluency. And that can only be achieved by specialising in one language.

How costly is neural machine translation? Is it worth investing in NMT?

Very costly. You need powerful servers to operate the amount of data we鈥檙e talking about. If SMT is like driving a car, NMT is more like flying a jet 鈥� the fuel costs are much higher. It鈥檚 a lot more affordable now than it was before, though. More and more options are becoming available and prices are falling.

In terms of cost-efficiency, I would say that, if used correctly, MT has the potential to really speed up translation in established workflows.

How secure is MT in general and NMT specifically? How can we be sure that personal data and other data is safe?

It’s as secure as you want it to be. It depends on who deals with your engines and how. We have third-party technology, but we鈥檝e checked their locations and their background.

We also clean the data to keep it secure so that no personal data gets used to train engines. Even Google no longer reuses the data you send back to them. For a while now, they have limited themselves to the data from Google itself rather than using the final output from the translators.

In other words, I think machine translation is very safe.

In part 3 of the interview with Mattia, we will talk to him about the practice of machine translation post-editing and how translators can learn to edit the output from MT engines.

Learn more about聽machine translation here.

The post Making machine translation work for us 鈥� part 2 appeared first on sa国际传媒.

Types of machine translation becoming available freely online has changed most people鈥檚 behaviour (at least online): you can now get the gist of an article or a website written in a language you do not understand with a few clicks.

Other machine translation engines are now being used by professional translators as well. The latest development is using artificial intelligence to help make the engines more accurate, which has led some to predict that the machines will take over the translation tasks performed by humans.

We sat down with the machine translation (MT) specialist in STP鈥檚 technology team, Mattia Ruaro, to discuss MT in the industry and at STP. Mattia is a translator by training and has become a key part of STP鈥檚 technology team after starting out in a project management role.

In this first part, we鈥檒l talk to Mattia about what machine translation is and how machine translation engines can be used 鈥� and trained.

So, Mattia, how does machine translation work?

Machine translation is the technology that allows an engine to translate from one natural language to another. Thus far, natural language has basically also meant written language. Machine translation has been around for decades, but there has been a lot of progress in the last 20 years.

There are several types of MT engines; the rule-based ones came first, then the statistical ones and after that the more recent neural machine translation. Every new type of MT has followed the same pattern: the technology has been developed, it鈥檚 been trialled and used with a lot of enthusiasm 鈥� and then people have discovered its limitations.

While there is a lot of hype about the latest technology, neural MT, even replacing human translators, it has limitations, too. This cycle seems to be there for all the different technologies 鈥� none of them are actually quite the miracle solution they are hyped up to be at the start.

What are the differences between statistical machine translation (SMT) and rule-based machine translation (RBMT)?

In essence, rule-based machine translation does what it says on the tin; the engine operates according to a set of rules, which are inputted by the developer. Nothing apart from the rules regulates the output from the engine.

The limitations of purely rule-based machine translation were discovered quickly. You need to input all the rules manually and sometimes a long list of exceptions, which is just not viable in a commercial environment, since it takes far too long.

The only exception to this are situations where your source language and your target language are closely related. This means that the languages are very close in terms of their lexicon and the semantics of that lexicon, as well as being structurally similar. Since you don鈥檛 need to input lots of different rules, you save a lot of effort.

Statistical engines are different: they draw on data to create patterns 鈥� this is a more recent approach. It鈥檚 basically about feeding the engine as much data as possible and the engine finding patterns in that data.

At STP, which types of MT engines out of the ones you mention have been used?

All of them. We tried rule-based engines for translating between Scandinavian languages, which are closely related. So, we would use a rule-based engine to produce output to help with a text we were translating from Danish into Swedish, for example.

For the past 4鈥�5 years, statistical engines have been more viable for us business-wise. Lately, we have been experimenting with neural machine translation. We started with only English into Finnish for neural MT, but we are now in the process of trialling it with other language pairs as well. So far, it seems to be working well in terms of the fluency of the output but it still has some difficulties processing terminology, particularly when it comes to specialised areas. Only time 鈥� and extensive testing 鈥� will tell how much better this technology truly is..

Thus far, which languages is machine translation most successful for? What about text domains?

For us at STP, the differences have been bigger between different domains than between different language pairs. The big advantage of statistical engines over rule-based ones has been customisability. It鈥檚 all about the data you feed the engine.

If you only input data for one domain, you can get rather good results, since you are training the engine for a narrow scope of material. This has been successful for software, mechanical engineering, financial and business 鈥� the latter is a bit of a catch-all term for things like website content, newsletters, HR documentation and so on.

But MT has certainly not been successful for all domains. For example, we haven鈥檛 had much success with medical engines. Medical texts are heavily regulated, and machine-translation suggestions can become more of a hindrance than a help when you鈥檙e having to follow multiple glossaries and style guides.

Is it possible to train an engine with the help of glossaries and other resources?

Yes, with glossaries, certainly. Style guides are guidelines and they do not contain absolute rules, most of the time, so they are more difficult to implement. It also has to be said that these resources are only as useful as the client makes them.

Another issue with glossaries and resources is that they are often specific to one client 鈥� creating and training an engine for just one client is a big investment of time, effort and money. So, we need to be sure that it will be of use in the future 鈥� it鈥檚 a risky investment for a language service provider to make.

How do you train an MT engine to give you good-quality output?

By having a lot of good data to begin with. If you鈥檙e looking for material to input, make sure it鈥檚 clean, flowing text and just text. It鈥檚 much better to clean the data than to feed the engine unnecessary clutter.

Once the first batch of data has been inputted, you should start using it and get feedback from translators to see if you can tweak the engine.

Ideally, you would prepare the data to make it easier for the MT engine: you鈥檇 get rid of extra formatting and tags and make it easier for the engine to parse. MT engines will struggle with extremely long segments and fragmented content.

If it鈥檚 possible to get feedback and train the engine based on that, I would recommend this. The cycle of preparing the input, training the engine and asking for feedback should be repeated regularly.

This practice of continuously improving MT engines is actually part of the machine translation post-editing standard ISO 18587 that STP received a certification in in March this year 鈥� you have to make sure that there鈥檚 a constant loop of feedback and improvement!

In part 2, you can read more about Mattia鈥檚 thoughts on neural machine translation and how STP has approached using machine translation as another technology to help translators in their work.

Learn more about聽machine translation here.

The post Making machine translation work for us 鈥� part 1 appeared first on sa国际传媒.

In that time, machine translation (MT) has gone from:

鈥淣ot there鈥� to

鈥淵eah right, ha ha, never going to happen鈥� to

鈥淥h, this client鈥檚 doing it, but it鈥檚 pretty awful鈥� to

鈥淢aybe we should consider doing it?鈥� to

鈥淒oing it, and it鈥檚 not that bad鈥� to

鈥淎ctually, it鈥檚 just another productivity tool鈥�.

These days, we see a lot of MT at STP. Our excellent Technology team develop and maintain a host of MT engines for our internal use. We get MT output from clients and end-clients, and it ranges in quality and type from pure Google Translate to highly customised account-specific engines. What has been interesting is that companies have almost exclusively wanted a product which is full human quality.

If you ask me, the bottom line with MT is that when it鈥檚 used correctly, it allows us to translate more content faster, and within the same budget than before MT. And that鈥檚 great, it means that our target languages aren鈥檛 particularly threatened by English, as companies continue to see the value in producing content in their customers鈥� native tongues. For someone with a degree in Finnish translation, that鈥檚 a nice thought 鈥� there are only 5.5 million of us Finns after all!

What has become abundantly clear in the past few years of STP ramping up our use and development of MT is that our linguists鈥� MT post-editing skills are at the core of our ability to produce that full human quality. And that requires training.

This spring, we were certified to ISO 18587 on machine translation post-editing. This is a new ISO standard that has been developed to address the requirements for post-editing skills and training, rather than the technical development or implementation of MT engines. It鈥檚 not a particularly onerous standard to meet, provided that you are running a legitimate operation.

What the standard does do, though, is put the onus on the language service provider (LSP) to provide appropriate, robust training which ensures that the linguists working on MT output know how MT works, how post-editing is different to editing translation memory matches, how to give feedback and improve the engines efficiently, and how post-editing is best approached. And I think that鈥檚 the least we owe our translators.

And what being certified to the standard does is that it tells not only the outside world but also our clients and translators that we as a company know what we鈥檙e doing with MTPE. It tells them that our linguists are trained and know what they鈥檙e doing with MTPE, and that, essentially, it鈥檚 safe to trust your MT in our hands 鈥� what comes out the other end is another great STP translation.

I am sometimes a bit jealous of our translators who have made my old dream a reality, especially when it comes to figuring out how to use technology in the translation process. That said, I realised a long time ago that I would have at best been a mediocre translator, so I鈥檓 glad I found my calling on the business side of things. I certainly wouldn鈥檛 want to move to another industry, that鈥檚 for sure!

Raisa McNab is STP鈥檚 Learning and Development Manager and the ATC鈥檚 Lead on Standards. She holds an MA in Translation from the University of Turku in Finland.

This article first appeared in the June 2018 edition of STP’s Icebreaker newsletter.

Learn more about聽machine translation here.

The post Making the most of Machine Translation appeared first on sa国际传媒.

One of our in-house translators, William, recorded this video on how to manage stress and avoid the pitfalls of working from home for the benefit of his colleagues. Expect useful tips on nutrition, a demonstration of some exercises to keep fit and a glimpse of some llamas.

The post A Day in the Life of a Translator appeared first on sa国际传媒.

If you enjoy working independently, dislike office distractions and perform best in your own surroundings, home working has few drawbacks.

But if you thrive on the buzz of office life and prefer to have people around you, the home-based setup can at times create an isolating and creativity-sapping environment.

One option, if you鈥檙e in the latter camp, is to find a public spot with internet access and work from there instead.

Cafes are the obvious choice. But unless you can afford all the drinks needed to keep a table for more than a few hours each day, coffee-shop working is more an occasional treat than a sustainable alternative.

At least, that鈥檚 how it used to be. Because now, thanks to the marvellous app, you can bring the cafe to your home office for free 鈥� no overpriced lattes required.

Bring the聽cafe to your home office with Coffitivity

Coffitivity recreates the gentle hum of a coffee shop, with all the whooshing machines, clinking cutlery and low, indistinct chatter that makes cafes such appealing places to work.

The app is built on showing that coffee houses create just the right level of ambient noise to fuel creativity, and it claims to help users work better and more productively.

I, your humble scribe, started using Coffitivity in 2013, back when I was a home-based freelance translator. And I still use it now on the odd occasion I need to work from home.

I struggle to focus in complete silence, but I also find music distracting. For me, Coffitivity is the perfect kind of background noise: unobtrusive, yet lively enough to get my thoughts and ideas moving.

I love being in the office, and Coffitivity is the best replacement I鈥檝e found so far for the background bustle my colleagues provide. After the app has been running for a while, I sometimes even forget 鈥� sad as it sounds 鈥� that I’m sitting and working alone in my front room.

Of course, real coffee shops and offices are always preferable. But if you don鈥檛 have regular access to either of those, give Coffitivity a try 鈥� and聽 if you like what it does.

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But there is, in fact, one other hardy little creature that’ll still be scuttling across the earth’s barren surface long after we’ve all gone:

The PDF source file.

Indeed, no matter how advanced translation technology becomes, there will always be clients that ask you to work from PDFs.

Rather than try to stamp PDFs out, the best thing you can do is learn to coexist with them. And to make that easier for you, we’ve put together these handy tables of shortcuts and commands.

Most of the functions work in the free version of , but one or two may only be available in paid editions. Either way, we’re sure they’ll help you handle PDFs faster and more efficiently.

General commands

Single-key shortcuts

To activate single-key shortcuts, click Edit > Preferences > General and tick Use single-key accelerators to access tools.

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Bonus tip: Search multiple PDF files

You can also search multiple PDF files at the same time. Here’s how:

1. Go to Edit > Advanced search

2. Tick All PDF Documents in and select the folder that contains the PDF files

3. Type your search term into the box, tick any additional search criteria and hit Search

Did you find this article useful? Let us know on or !

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Adam Dahlst枚m, STP鈥檚 language technology manager, knows more than a thing or two about Google’s advanced search features 鈥� so here we share his nine top tips to take your googling to the next level. We hope you’ll find them useful.

1. Get more relevant results with Verbatim

Getting too many irrelevant search results? Then try Google鈥檚 Verbatim feature.

Verbatim returns results for your exact search term, cutting out any hits for similar spellings or related words and phrases.

The Swedish “Spara” and “Sp氓ra,” for instance, are two different words 鈥� but in an ordinary search, Google would make no distinction between them and provide results for both.

To use Verbatim, search as normal and then select Search tools > All results > Verbatim on the results page:

2. Use quotation marks to find exact phrases

Wrap a whole phrase in quotation marks to search for that exact query, rather than pages containing the words one by one.

Example: “tennis racket”

This will find results for “tennis racket” but not “tennis” and/or “racket”:

3. Find alternatives with OR

Insert OR between words you want to search as alternatives, instead of searching separately for individual phrases.

Example: tennis OR badminton OR squash racket

This will find hits of “tennis racket,” “badminton racket” and “squash racket”:

4. Exclude words from a search

Put a minus sign before words you want to exclude from your search results.

Example: -badminton tennis racket

This will find hits of “tennis racket” but not “badminton racket”.

5. Save time with instant word definitions

Type define: followed by any English word or abbreviation and Google will give you a list of definitions.

Results are drawn from a large number of online glossaries, saving you a considerable amount of hunting around.

Example: define:racket

This will return an English definition of the word “racket”:

6. Set a numeric range

Put two full stops (without spaces) between two numbers to create a range.

Example: Wilson Blade 93..104

This will find “Wilson Blade 93,鈥� “Wilson Blade 98,鈥� “Wilson Blade 104” and any other Wilson Blade products with a model number between 93 and 104:

7. Use wildcards

You can use an asterisk as a wildcard to replace any words between other specific words.

Example: “game, *, and match鈥�

This returns the obvious 鈥済ame, set, and match鈥� as well as 鈥済ame,聽upset, and match,鈥� 鈥済ame, sweat, and match鈥� and countless other variations.

8. Search for similar words

Add ~ to any keyword and Google will show results for that exact word, or words with a similar meaning.

Example:听迟别苍苍颈蝉听词濒别蝉蝉辞苍蝉

This will find hits of “tennis lessons,鈥� but also “tennis classes,” “tennis instruction,鈥� and so on.

9. Get results from a specific web domain

Type site: followed by a web address to search within that domain only.

Example: site:tennis.com “tennis racket”

This will search tennis.com 鈥� and only tennis.com 鈥� for hits of “tennis racket”:

Did you find this article useful? Let us know on or !

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Your client has asked you to translate directly in Excel.聽What’s more, they’ve set a strict limit of 50聽characters per target cell.

Excel does not have an in-built character checker.聽And nobody has聽time to sit and count characters on the screen 鈥� so what to do?

One answer is to create simple character-counting聽cells or alerts to keep you aware of the limit while you work. Here’s how to set them up.

Setting up character counters in Excel

1. Select a cell to use as the character counter

2. Insert the formula聽=LEN(cell)听and replace聽肠别濒濒听with the target cell

In this example, the target cell is A2聽and the character counter is B2.聽So聽=LEN(A2)听is the active formula in B2:

The target character count (including spaces and numbers)听now shows next to the target cell.

To apply the same formula to consecutive cells 鈥� say, B2 to B5聽鈥� just highlight that range, put the root formula in聽B2聽and press CTRL+Enter. The bracketed part of the formula will then update for each cell.

Setting up character alerts in Excel

You might prefer a more visual warning when you exceed the character count. This is where alert cells can be handy.

1.聽 Select the cell in which you want the character alert to appear

2. Insert the formula =LEN(cell)<51聽and replace聽肠别濒濒听with the target cell

In the example below, A2 is your target cell, which should contain no more than聽50 characters. The alert cell is B2. So you need to insert =LEN(A2)<51 into column B2:

If the target text聽fits within the character limit,聽TRUE聽will show in column B. If not, it will say聽FALSE.

Bonus tip: How to compare column lengths

Now, what if you need to check the length of your target text against the source?

Easy. Just create an alert cell with this formula, making sure to replace target cell and source cell with the correct values:

=IF(LEN(target cell)>LEN(source cell),”Too long”,”OK”)

Got any Excel translation tips of your own? You鈥檙e welcome to share them with us on , leave a post on our or drop us an email.

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