But this is easier said than done. Many marketers ask themselves: How can we create personalised content on a global scale? Should we just translate one message into hundreds of languages?

The short answer is no. Translation is not enough. Instead, localisation 鈥� defined as the adaptation of content to a specific market 鈥� is the process that should lie at the core of every global marketing campaign if the content is to be diverse enough to address different values, lifestyles and ethnicities.

Why translation alone isn’t enough for global marketing

Many marketers still treat translation as an afterthought, simply converting words without considering how those words land in a new cultural context. Global audiences are both multilingual and multicultural, and localisation takes the next step, ensuring your message resonates authentically with different values, lifestyles and expectations.

Culture shapes how people interpret messaging, and translation alone can’t bridge that gap. For example, some cultures communicate through subtext or a shared understanding, while others like to be more direct. Even between countries that share similar cultures and languages, like in the Nordic region, you can find notable cultural differences. Read more about this topic in

Localisation goes beyond speaking the right language and helps you understand your audience鈥檚 mindset so that your message doesn鈥檛 just get heard, but actually sticks.

Advantages of localisation in global marketing

By prioritising localisation from the get-go, marketers ensure the following:

• Cultural authenticity: When localisation is considered from day one, you can create content that feels genuinely native to each market.
• Consistent brand voice: Early planning ensures that your brand personality, although tweaked linguistically and culturally, still resonates in a consistent way across all markets.
• Faster market entry: Campaigns can launch simultaneously across markets rather than in staggered rollouts, waiting for translations to go live.
• Time and cost efficiency: Starting with localisation in mind means you won鈥檛 need to go back and redesign campaigns that don鈥檛 translate well across cultures.
• Growth: Localisation can expand your brand reach and awareness, as well as offer a SEO boost.
• Connections: You can more effectively build personal connections with users and consumers because it gives your brand the human touch and shows respect for local cultures and values.
• Increased profits: If you do it right, localisation can form part of a successful organic growth strategy. If you don鈥檛, it could come at a significant cost to your business.

Choose the right localisation strategy for your global marketing content

Of course, not every message needs the same level of localisation. That鈥檚 where a tailored strategy comes into play.

To find a localisation strategy that best fits your goals and expectations, you first need to assess the level of impact of your different content pieces. Is it brand-critical content, such as a slogan, or is it lower-impact content, such as reviews for an online platform?

Understanding that different types of content may require different strategies can save you time and help your language service provider allocate efforts properly. The graphic below can serve as an initial guide to think about where you might want to start, but you can always get in touch with us for more personalised advice.

Localisation services for global marketing, explained

Market-specific copywriting

We鈥檝e talked about personalisation, so we know how crucial it is. Sometimes, the best way to ensure that this content can truly match a particular target audience is to create region- or country-specific campaigns to convey messages that are culturally and contextually appropriate.

Transcreation

Literal translations can sometimes cause misunderstandings and lead to considerable loss of meaning. Content should be transcreated instead, taking tone and style into account, having consideration for the design, the surrounding images and the broader context.

Full human translation

This is what you might call the traditional approach to content localisation. A human translator takes a text in one language and translates it into another, adding colloquial expressions and idioms, where appropriate, so the text reads like it was written in the target language. However, this doesn鈥檛 take into account cultural references and norms, and the meaning of images and symbols.

Machine translation post-editing

Some content, such as slang and idiomatic language, is not compatible with machine translation because of how culturally specific and ultimately human it is. However, when localising low-impact content, machine translation can be used in conjunction with human post-editing for a good and affordable option.

Multilingual SEO

There is more to SEO than simply translating keywords! Keyword localisation acknowledges the fact that the keywords that are popular in one region can be completely different in another. SEO needs to be tailored to each particular market.

The language solutions partnership process

While adding another stakeholder to your workflow may seem daunting, partnering with a language solutions provider that prioritises clear communication, seamless integration and mutual trust mitigates risks while delivering significant benefits to your marketing team.

Here鈥檚 a quick look into our process here at Sandberg:

• Strategic planning and onboarding

Our partnership begins with comprehensive onboarding outlining project requirements, costs, timelines and quality expectations. Strategic planning meetings assess longer-term needs, including expansion plans and peak work periods.

• Gathering project materials

Key client information is shared with project managers and linguists, including brand guidelines, style guides and terminology glossaries. Together, we’ll craft a detailed brief, containing specific brand and content information, with templates provided to streamline the process.

• Linguistic work with technology

Your content passes through multiple review stages by different linguists, ensuring accuracy and ISO standard compliance. Modern language technology, including translation memories and term bases, guarantees consistency. This may include AI and machine translation, followed by human review.

• Independent review

Project managers or validators conduct final content reviews before delivery, ensuring alignment with reference materials. Your dedicated point(s) of contact handle issues promptly, with quick response times to maintain project momentum.

• Communication and feedback

Open communication extends beyond individual projects through structured feedback stages, enabling continuous improvement. We鈥檒l arrange annual business reviews to ensure there are clear communication channels for sharing ideas and feedback.

As you can see, the process is well-organised and can easily be tailored to your specific needs. For a more detailed explanation, read our article: /maximise-global-marketing/.

Make localisation a business priority

In an increasingly connected world where consumers expect authentic, culturally relevant experiences, localisation is a business imperative. Brands that invest in comprehensive localisation strategies from the outset can transform them into something that resonates deeply with diverse audiences across the globe.

The shift from translation to localisation represents more than just a change in terminology; it’s a fundamental reimagining of how brands connect with their customers. By embracing cultural authenticity, maintaining a consistent brand voice and leveraging the right mix of localisation services, from market-specific copywriting to multilingual SEO, companies can turn what might otherwise be seen as an afterthought or annoying extra cost into a powerful driver of growth and engagement.

Success in global marketing no longer favours those who speak the loudest, but rather those who speak meaningfully to each individual market. The brands that recognise this truth and build localisation into their processes from day one are the ones that thrive in our multicultural, interconnected marketplace.

At the moment, consumers have endless choices, and the brands that win are those that make every customer feel like the message was crafted specifically for them.

Ready to stand out from the crowd and start truly connecting with global audiences?

Explore our marketing solutions here.

Part of this article was initially published in 2019 by Gonzalo Fernandez, a former Sandberg team member, and has since been edited and revised with up-to-date information and new analysis.

The post The role of localisation in global marketing campaigns appeared first on sa国际传媒.

The current state of the heavy machinery sector

The global machinery market saw a healthy growth from , an annual growth rate of 7.6%, as the sector recovered from the pandemic lockdowns. Some analysts expect a further growth rate of 6.6% by 2027, driven in part by an increased demand for construction and infrastructure equipment.

The highest growth rates are expected in the Asia Pacific region, as ongoing large-scale infrastructure projects in China and India continue to be rolled out rapidly, including new highways and airports.听

European machinery and equipment manufacturers have also witnessed a significant recovery, with revenue growth spiking to 18% in 2022 compared to 7% in the North American sector. However, Europe lags in profitability, with EBIT margins falling to 8.6%, whereas North America achieved 10.4% over the same period, as reported in a . A large part of the reason is the inflationary pressures in Europe, particularly on energy costs, which have been mitigated in America through shale gas exploration.

The heavyweights of heavy machinery

In the construction industry, the US-based Caterpillar Inc. dominates the market. In 2023, Caterpillar Inc. reported establishing itself as the world鈥檚 leading manufacturer of construction and mining equipment, off-highway diesel and natural gas engines, industrial gas turbines and diesel-electric locomotives. Key competitors in the global market include Japanese Komatsu and Hitachi, US company John Deere and Swiss manufacturer Liebherr.听

Other notable players within the heavy machinery market are Daimler Trucks, one of the leading heavy machinery manufacturers in the world. With origins dating back to the 1890s, the company has a global presence, employing more than 100,000 people in 42 production sites worldwide. They produce lorries of various sizes and models, including commercial vehicles, buses and specialised vehicles like fire engines.听听

Founded in 1927, the Volvo听Group is another prominent player in the heavy machinery market, producing commercial vehicles, construction equipment and diesel engines. Volvo operates in several markets, including Europe, North America and Asia. In April 2021, Volvo Group sold its UD Trucks division, generating a USD 2.3 billion payout.听

To preserve its competitive stance globally, the Volvo Group has been steadily investing nearly (December 2022) in research and development (R&D), primarily focused on battery-powered commercial vehicles.

Paccar is a well-known American manufacturer of light, medium and heavy-duty commercial vehicles, marketed worldwide under the Kenworth, Peterbilt and DAF brands. Paccar also produces industrial winches under the Braden, Carco and Gearmatic nameplates.听

Scania, a Swedish manufacturer of heavy commercial vehicles and engines established in 1891, reported an impressive growth in in 2023. The company鈥檚 workforce grew to 58,163 people, an increase of 2,500 from the previous year. Scania has been focusing strongly on alternative fuel and electric vehicles, selling 6,454 units in 2023, a slight decrease from the previous year but still a substantial part of their total delivery of 91,652 vehicles.听

The main challenges facing the heavy machinery sector听

Skilled labour shortage

A critical issue in the heavy machinery sector is the lack of skilled manpower. By 2050, 30% of the global workforce will be 50 years of age or older; however, in the EU, only remain active in the labour market. This scenario underscores the urgent need for young recruits who are either highly skilled or given training opportunities within companies, in addition to finding ways to retain older workers.

In Europe, the number of unfilled jobs in the sector rose by around 70% from 2020 to 2022 and has remained just above 500,000 vacancies since the beginning of 2022. Germany alone reported approximately 250,000 vacancies in the machine sector in 2022, a doubling from the previous year as highlighted by a In a figures show that the increased integration of smart connected devices, equipment and systems require highly skilled roles, which are expected to grow the fastest between 2022 and 2032. 听

Understanding the evolving expectations of new generations and the changing needs of older workers is crucial. Companies are also implementing internal systems to enable workers to upskill and work flexibly when the need arises, such as the 听positions, which have proven highly successful.听

Big Data and heavy machinery

The industrial equipment industry is not immune to technological innovation, Big Data and artificial intelligence. The integration of advanced technologies like the Internet of Things (IoT) is and making it smarter.听

systems, where vast amounts of data can be generated and analysed at once, enable fast decision-making, predictive maintenance and operations optimisation. Maintaining such systems requires an ongoing and dedicated investment in equipment, resources and skills training for engineers and technicians.

AI, automation and Big Data integration can also help mitigate the shortage of skilled staff in the sector by simplifying and optimising processes. For example, machine parts can be exchanged before they break, and changes and upgrades can be planned proactively.听

Carbon footprint and the price of transition

Another challenge the sector faces is the transition to a sustainable low-carbon future. The term 鈥渮ero carbon鈥� is still widely used to describe Electrical Vehicles (EVs), despite a from the Advertising Standards Authority stating that it is misleading to call an EV 鈥渮ero carbon鈥� unless referring exclusively to driving it. This is because CO2 is released during the vehicles鈥� manufacturing process听and potentially in the production of the electricity that powers them.听听

The increased demand for batteries is also not without its environmental impact. , it takes 2.2 million litres of water to produce 1 tonne of lithium. When Chile, for example, produces 8 million tonnes of lithium, this will have used around 17.6 trillion litres of water, in addition to the impact the mining has on the surrounding communities and the environment. 听

Furthermore, that the cobalt used in batteries, including vehicle batteries, is in some cases sourced from mines where child labour and other dangerous work practices are rife. 听

The EU , which means that European companies must document that the products they import adhere to environmental and human rights standards. For the machine industry, it will be necessary to carefully build supply chains, as any environmental or human rights impact will undoubtedly come under scrutiny as the transition to the desired low-carbon reality gathers pace.

Inflation and profitability

As mentioned before, revenue and productivity have both increased in Europe, but profitability is weaker than in North America. , including the increased price of energy, fuelled largely by sanctions on Russia, and interruptions of supply chains.听

The development of shale gas has helped the US keep gas prices down. This has had knock-on effects across the market, not least on energy-intensive steel production, which is extensively used in the production of heavy machinery.

The conflict in the Middle East is also driving up volatility, as well as insurance costs and transportation times for goods that are re-routed away from the Suez Canal.听

Businesses that are robust enough to withstand these difficulties and perhaps even increase their efficiency to thrive in troubled times will stand even stronger in a global industry where there will always be an element of volatility.听

Innovation and R&D

Patents remain vital for protecting and maintaining a company鈥檚 competitive edge. Innovation is crucial in a mature market like heavy machinery, where distinguishing a product by solving problems more efficiently can drive sustainable growth.听

Patent applications to the听 from 181,532 in 2019 to 199,275 in 2023. However, the number of patents for specialised machines in 2022. The top five countries for patent applications were the USA, Germany, Japan, China and the Republic of Korea, with a total of 57% of applications coming from outside Europe and the UK. 听

Notably, the Nordic countries of Sweden, Denmark and Finland rank in the top five for the number of patent applications as a share of the population. Denmark and China are the only two countries that have had stable growth in the number of issued patents across all major sectors. 听

As skilled labour becomes scarcer, innovative industrial equipment that enhances efficiency and productivity per working hour will be essential for future success. 听

Heavy machinery translation and localisation

In the global market, making use of heavy machinery translation services provides significant added value and is often a crucial necessity.

The most successful companies in the global marketplace are those that maintain the integrity of their product while adapting it to meet local preferences. Swedish furniture giant IKEA, for example, not only tailors its product range to fit local needs but also makes linguistic and cultural changes so that its product and marketing materials resonate with the target audience.听

Although industrial machines are not typically marketed directly to consumers like an IKEA coffee table, the underlying principle remains the same: reaching international audiences is crucial for growth and overall business sustainability. To succeed, business must tailor their offerings to appeal directly to decision-makers in the industry or industries they aim to penetrate.听

Localisation covers a range of activities and areas crucial for commercial success. This can include product adjustments to meet specific local conditions, such as modifying machinery to handle dry, sandy environments or cold, snowy conditions.听

In addition, localising product descriptions enhances听the connection with local buyers, while localised customer service and helplines foster long-term relationships.听听

With the global shortage of skilled labour, attracting qualified and highly motivated people from all over the world is another obvious benefit of a global approach, guided by the principle that has made Spotify a success: Think Global, Act Local.

Specialist knowledge

Heavy machinery translation often requires linguists with specialised knowledge of commercial and regulatory needs as well as technical terminology. 听

Professional localisation services use several tools to ensure that translations are linguistically accurate and industry-specific (e.g. a 鈥減ipe鈥� may mean something very different in the oil-drilling industry than in the construction industry). This includes maintaining term bases with preferred translations and technical terminology.听

Automated solutions can also be beneficial for ensuring accuracy and consistency across technical translations. However, it is essential that a competent human translator reviews all content for accuracy and adherence to agreed terminology.

Common content types for heavy machinery translation听

There are several key areas where the heavy machinery sector needs translation services:听

Product information

Key product information translation may be mandated by regulations such as the on machinery. This regulation stipulates that all key information regarding machines, including statements of conformity, instructions and a broad range of other documentation, shall be presented 鈥�…in a language which can be easily understood by users.鈥�听If necessary, it shall 鈥�… be translated into the language or languages required by the Member State in which the machinery or related product is placed on the market, is made available on the market or put into service.鈥�

Product descriptions

In a market dominated by established players, standout product descriptions can significantly impact how a company鈥檚 unique selling points are communicated to a technically savvy audience.听 A specialised translator can help ensure that the terminology and meaning are spot-on for every听audience.听

Safety information

Heavy industrial machines and construction machinery are high-risk entities to use, with the construction sector topping the stats for .听Given these risks, it鈥檚 crucial that product manuals and operating instructions are clearly presented in a language that each user fully understands, both for safety and regulatory compliance.听听

Patents听听

When it comes to patents, translators should have experience with both technical and legal translation to ensure precise and effective communication.

Reach global audiences in a global industry

Producers of heavy machinery 鈥� whether it is industrial equipment or machinery, construction machinery or heavy goods vehicles 鈥� face numerous challenges including the need for skilled labour, increased globalisation, the integration of technologies like Big Data, IoT and AI, and evolving sustainability and low-carbon requirements.听

Despite these challenges, the sector is strong and continues to grow and improve. For instance, the UK government’s Advanced Manufacturing Plan, launched in November 2023, highlights 听across Europe, the UK and Asia.听

But to ensure compliance with regulations and maintain a competitive edge in such a globalised and competitive industry, high-quality heavy machinery translation services are a necessary component of a successful global sales and marketing strategy.

The post Heavy machinery translation 鈥� building our future in a global industry听 appeared first on sa国际传媒.

Ever since machine translation started to become the norm 鈥� in both academic and commercial contexts 鈥� users of machine translation have been asking themselves whether or not they鈥檙e doing something different mentally and practically when post-editing. This has also led researchers to ponder whether the language that鈥檚 produced from post-editing is actually a new one or simply a different type of translation.

What the research suggests

One such researcher is Antonio Toral, from the University of Groningen in the Netherlands, who has recently published a paper called . In it, he explains how he compared a number of post-edited texts with human translations of the same texts using simplification, homogenisation and interference as his main assessment criteria.

He found that post-edited texts tend to have lower lexical variety and lower lexical density, with sentence lengths matching the source text a lot more closely. These tendencies produce texts that are generally less varied and less rich. They also tend to be more homogenous and introduce significant interference from the source language. The sample size, metrics used and text types selected for this study have their limits, but it鈥檚 still quite interesting to observe this phenomenon.

The effects on the wider language

Language change is a natural process: it happens and it has happened regardless of machine translation, with factors like instant messaging, social media and a constant need for better, faster and more optimised communication being key drivers of this trend. Now we can add post-editing to the list of factors that influence how the language we read evolves and develops.

That said, an important note to make is that while things like instant messaging are entirely driven by human input, machine translation (and by extension, post-editing) is not that different. Yes, the text is produced by a machine, but the machine itself is trained on data that is generated by humans, so in a sense, the machine is simply replicating what we all write and say to suit a specific context. So while it is an innovation, it鈥檚 also solidly grounded in data collected the old-fashioned way.

The machine is simply replicating what we all write and say to suit a specific context.

One could then argue that machine translation is innovative in the way it recycles the language to reuse it when possible 鈥� a very 鈥済reen鈥� approach of not wasting any training data it has been fed. Data is, after all, the new high-value commodity in our modern world, and language data is incredibly important for any translation provider thinking about using machine translation to its fullest potential.

A different skillset for translators

At the end of the day, it鈥檚 easy to think 鈥� regardless of whether you鈥檙e post-editing or translating 鈥� that you鈥檙e just turning one language into another, right? While that might be true, the way you approach the task is substantially different on a fundamental level: when you post-edit, there鈥檚 already something there: you鈥檙e not starting with a blank canvas.

This may sound obvious, but it leads to a number of interesting habits for post-editors, one of which is the temptation to simply read the MT output and think 鈥測eah, that鈥檒l do, next鈥�, especially if you鈥檙e pressed for time with a deadline looming. False translations, unidiomatic constructions and internal inconsistencies are among the most common examples of 鈥渦nder-editing鈥�, so it鈥檚 important to always be careful and rely on good old-fashioned attention to detail.

It鈥檚 important to always be careful and rely on good old-fashioned attention to detail.

Oddly enough, this is complicated by the fact that the latest developments in machine translation, and particularly in , have led to great improvements in the flow and grammatical accuracy of the output: the language can sound so natural that it can trick post-editors into thinking that there is less to edit than there actually is.

This means that translators working on post-editing jobs should not underestimate the task at hand: yes, they do have the existing skills to be ready for it, but the process might be more mentally complex that they initially expect.

When is MTPE the right solution?

This is all well and good, but what should a buyer of translation services ultimately make of this information? And what should a language services provider take into consideration when offering translation and post-editing of machine translation?

It all boils down to the intended purpose of the text (and in turn, your buyer): homogenising the text might sound like a terrifying thought, but if you鈥檙e ordering the translation of a safety data sheet for a chemical product or a list of ingredients for a beverage, is the flow of the language really that important? Wouldn鈥檛 the opportunity to be faster and more productive when translating these texts with machine translation 鈥� which thrives on repetition and recurrent patterns 鈥� be far more appealing?

Wouldn鈥檛 the opportunity to be faster and more productive with machine translation be far more appealing?

And at the other end of the spectrum, if you鈥檙e dealing with a text that鈥檚 very creative, for example a client鈥檚 website that鈥檚 on view to the public, it might be preferable to consider a different approach. In this case, machine translation might not be the best solution and you should consider opting for transcreation for a better end product.

A good example of correctly used machine translation is usually an engine trained and used for a particular domain or text type. For instance, an engine built entirely with and for legal texts will generally perform well with the often formulaic and standardised terminology and constructions typical of that domain. Neural machine translation should also be the best solution here, since legal texts tend to have lengthy, verbose sentences that can be quite time-consuming to break down and translate manually without extra aid.

It鈥檚 safe to say that the decision to use MT should be made on a domain-by-domain and perhaps even job-by-job basis. If you want to know more about when it鈥檚 the right solution, download our free Guide to machine translation.

The post The right way to use machine translation appeared first on sa国际传媒.

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听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听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听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听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 惭眉濒濒别谤听(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听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听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听machine translation here.

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

While looking for savings, it is important to realise that the raw output quality of MT engines varies wildly from Google Translate to highly-tuned and customised domain-specific engines. The latter can give very good results. Unless you have worked on the particular MT engine output before, it is difficult to estimate exactly what an equitable level of compensation for the post-editing should be.

The MT output is also usually combined with traditional TM output. Typically, any matches above 70鈥�75% come from the translation memory and are dealt with by the linguist as in any normal job. Any segment below a 70鈥�75% match gets machine-translated and the linguist post-edits it, recognising that there is a difference between editing a segment you know was produced by a human in the past (a TM match) and a segment that was put together by an engine. Typical errors in MT output include incorrect sentence structures, tenses, articles, inconsistent terminology and incorrect or missing tags. A good rule of thumb for the linguist is to look at an MT segment for two seconds and if they do not think they can easily edit it to produce a good result, to discard it and translate it from scratch, or use a lower fuzzy match from the TM instead.

The speed at which a linguist can carry out post-editing is directly linked to the quality of the raw MT output. With the projects STP has done so far, involving Scandinavian languages and customised MT engines, an experienced linguist would generally be expected to process 20鈥�50% more work than when working from scratch. This productivity enhancement gives an indication of the savings in time and costs that can be expected.

Learn more about听machine translation here.

The post From the engine room appeared first on sa国际传媒.