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What Is Post-Editing? Here Are 3 Approaches

What Is Post-Editing? Here Are 3 Approaches

 

Post-editing of Machine Translation (PEMT) wasn’t born yesterday (MT and post-editing is mature in Wordbee, for example). On the contrary, it’s as old as machine translation (MT) itself. And although at the moment, we have a large amount of material at our disposal about this topic, the nuances of the discussion are such that in some cases we risk losing sight of what PEMT really is.

PEMT: No Need for Creativity

Let’s start by saying that PEMT has nothing to do with revision. Nor does it require the “creativity” of, let’s say, transcreation. In spite of all the articles written on the subject and a brand-new ISO standard, up to now the most accurate definition of post-editing comes from the 2010 TAUS Post-editing in Practice report: “Post-editing is the process of improving a machine-generated translation with “a minimum of manual labour.”

The keywords in this definition are “a minimum of manual labour.”

While revision is based on a contrastive analysis of source and target texts and requires the reviser to check and edit terminology, style, and grammar, PEMT is characterized by higher productivity and limited cognitive bilingual effort. The main changes will concern mechanical errors (capitalization and punctuation), grammar errors, terminology inconsistencies (e.g. missing words), and other issues that are often the product of a poor source text and result in poor readability of the target text. A post-editor is not expected to rewrite entire sentences (unless those sentences are obvious nonsense or contain word salads), so they should only amend what’s necessary to make a sentence clearer to the reader.

The skills that distinguish a reviser from a post-editor are also different: A reviser must have a sound knowledge of both source and target languages, of translation techniques, and of a specific domain, but a post-editor, on the other hand, may even be monolingual. No matter what, though, they must have a strong knowledge of the target language and of the specific domain, and, ideally, an idea of how machine translation works.

3 PEMT Approaches in Practice

PEMT and the Enterprise

Let’s take an enterprise that has developed its own engine. The PEMT task could take place in a CAT environment or, in the case of enterprises that have their own MT engines but no translation department as such, it could be entrusted to external language service providers (LSPs).

Because in this instance we’re dealing with a customized engine, the MT output will be of high or good quality. The PEMT guidelines will be very specific and rigorously based on the error typologies produced by the engine in question. It will be necessary to indicate the level of PEMT necessary (light or full post-editing), and what the purpose of the text and the target group are. Glossaries are essential if the MT engine has just been put into use and has shown some terminological teething problems.

PEMT and the LSP

Only a few LSPs have the financial and technical resources needed to develop client-specific MT engines. Most LSPs will resort to using vertical (domain-specific) engines developed by MT technology providers and available in SaaS mode according to a pay-per-use model. The MT output will be sent to internal or external post-editors. Alternatively, post-editors might receive an API key to use a vertical MT engine in a CAT tool environment. In this specific case, post-editing becomes an interactive task.

Some LSPs will pre-translate a source text with a general MT engine, for example Google Translate or DeepL. This is a viable financial choice when starting out with MT, translating small texts or, again, facing a lack of financial/technical resources.

In this approach, because the post-editing level and goals will change from project to project or from client to client, LSPs always need to provide information about the final use of the translation and accurate guidelines on how to conduct the task. PEMT projects could be split among many post-editors: The specificity and strictness of the guidelines will ensure a certain level of consistency. It’s also important to provide a client-specific glossaryto reach a consistent use of terminology, especially in the case of a public engine.

PEMT and the Freelancer

Gone are the days of freelancers’ rage against machine translation. Nowadays, most freelancers use MT as a helpful tool that provides translation suggestions. The choice is usually Google Translate or DeepL (web version or with an API key).

There are not precise PEMT guidelines in this instanceThe freelancer using one or more general MT systems is free to decide which MT tool to use, how to use it, and how much to use of the MT output. From an ethical point of view, they should inform the client about the use of a public MT engine, or in any case, ask the client if there are specific criteria that might prevent the use of public MT engines. Think medical records, legal documents (involving sensitive or personal data; in one word GDPR), and confidential or IP-protected documents.

One thing to remember: When using a public MT engine through an API key in a CAT tool environment, the segments containing MT output in some cases might be tagged with AT or MT, therefore revealing their origins.

PEMT in the Translation Workflow

It is also worth noting that an automatic translation is not something created by a machine with free will and an independent (albeit electronic) brain. An MT output (the technical term for automatic translation) is generated by stochastic calculation, like with statistical and neural machine translation, by an algorithm; the algorithm exploits bilingual translation corpora and, more generally, language data produced by humans. There can be various reasons for the high or low quality of an MT output: lack of clean language data, insufficient technical and financial resources for the development of an MT system, inadequate quality of the source text etc… But the common factor in all these reasons is due to humans.

PEMT should replace the two main phases of the TEP (translation, editing, proofreading) workflow. In order to implement an MT engine, it’s necessary to adapt the translation workflow to this technology and make sure that your TMS has what it takes to manage the PEMT phase efficiently.

Project managers should have at their disposal functionalities like automated QA and 100% match blocking, adding labels to specific segments, and so on.

Training post-editors

The PEMT courses available nowadays provide a general training on how machine translation works and give a few examples of the differences between revision and post-editing. There’s no need to provide long trainings using the MT output of Google Translate and DeepL, as the differences in many cases are minimal.

Whether you’re an LSP or an enterprise, to help your post-editors to become more efficient it’s important to provide a second level of training based on customized engines, with specific domain, language pairs, and text typology.

Don’t forget the basics of automatic post-editing: Instruct your post-editors on which functionalities and controls can be done on the MT output, for example how to visualize suggestions and how to modify them within your work environment.

 

Reference: https://bit.ly/2C1dw2D

Four Ways the Translator Role Will Change

Four Ways the Translator Role Will Change

Translators have been the staple of the translation business for decades. Linguistics, multilingual communication, and quality of language—this is their domain. They are grammarians and often self-admitted language nerds.

Translators are usually bilingual linguists who live in the country where their native language is spoken—it’s the best way for them to stay connected with linguistic and cultural changes. Their responsibility traditionally has been to faithfully render a source text into a specific target language.

But now, with the advent of more complicated content types such as highly branded content (like mobile apps or PPC ads), and with much higher volumes of content like User Generated Content (think customer reviews), all bets are off. The role of the translator has to evolve: translators now have to offer the right solution to new globalization problems…or risk being left behind.

This reality isn’t just relevant for translators: localization project managers need to know what new qualifications to look for as they try to match resources to their content. Four new specialist roles have evolved out of changing global content needs, allowing translators and linguists to expand their offerings and learn new skills.

Transcreators

In transcreation, a highly specialized linguist recreates the source content so that it’s appropriate for the target locale. They key term here is ‘recreates’, which means re-invent or build again. The goal is to create content that inspires the same emotions in the target language as the source content does in the home market.

Typically, the process of transcreation applies to taglines, product names, slogans, and advertisement copy; anything highly branded.

The linguists performing this service are highly creative translators: senior, experienced professionals with lots of marketing content translation experience. They also might have agency expertise.

Many transcreators begin their professional career as translators, and as they gain proficiency in marketing content, they become adept at the re-creation process. If they are creative types, this expertise can lead them right into the specialization of transcreation.

Content creators or copywriters

In-country copywriters create materials from scratch for a target market—a highly creative process. There’s no actual translation here. While the resource is often an ad agency professional with copywriting experience, they may also be a translator—or have been one in the past. (It’s not uncommon for a translator with creative content experience to move into copywriting.) Like translators, these professionals must be in-country in order to represent the latest trends in that market.

Cultural consultants

These folks, who also reside in the target country, provide guidance to a client on the motivations and behaviors of target buyers. They are researchers and representatives of their culture. They also may be experts in local paid media, search marketing, social media, influencer marketing, CRO, and UX.

Whatever their areas of expertise, these in-country experts could, for example, plan and manage an international digital campaign, conduct focus groups to determine user preferences, or do demographic research to help an enterprise understand or identify their target client. Bilingual, in-country translators already have—or can learn—the skills required to become a cultural consultant.

Post-editors

It’s not uncommon for an enterprise with a maturing localization program to deploy Machine Translation (MT). And most MT programs involve some level of post-editing: the process by which a linguist edits the machine’s output to a level of quality agreed upon between the client and vendor.

Post-editing needs a different skillset than translation: instead of converting source text to target text faithfully, a post-editor has to understand how an MT engine operates and what errors might be typical, and then fix all issues required to meet the requested quality bar. It’s one part translator, one part linguistic reviewer, and one part Machine Translation specialist. Translators with good critical thinking skills can train to do this work.

The needs of global businesses give translators an opportunity to stretch and grow into a variety of other industry positions that make use of their unique skillset and cultural expertise. Will translators of the future do any actual translation? Only time will tell. In the meantime, these newer linguistic services are growing in demand, and thus, so will the need for talent.

Reference: https://bit.ly/2P1hIRJ

TAUS BIG DATA GETS BIGGER

TAUS BIG DATA GETS BIGGER

Introduction

By July 2018, the TAUS Quality Dashboard benchmarking database had exceeded 100 million words. It is still small in comparison with TMS databases, but it is slowly becoming a relevant aggregation of translation project metadata from which we can start drawing early conclusions.

TAUS’s QD is fed by a handful of enterprise companies, a notable exception being Baltic LSP Synergium. Collectively, they add about 1 million words a day.

Translation memory (TM) is the main way for churning translations at enterprise companies using TAUS DQF. Unedited matches from the TM account for a 64 percent of all content translated, and edited fuzzy matches represent close to 10 percent more. Depending on the discount scheme for matches used by vendors, companies might be saving anywhere from one half to 70 percent of their human translation budget with the help of CAT-tools and TMS.

That could mean  USD 7.5 – 10.5 million in savings for the whole sample of 100 million words, assuming the average price per word is  USD 0.15. Technology for ten enterprise companies should cost around USD 1 million a year, warranting a 7 – 10x return on investment.

TAUS’s figure for savings is much greater than any previous benchmarks. Two years ago, using data from Memsource, I looked at the TM leverage using a database of 500 million words, and the median saving was at 36 percent. Today, TAUS shows a 50 – 70 percent economy. The difference is that most Memsource clients at that time were language services companies. Large LSPs usually deal with varied content from multiple clients. A significant portion of their content is new and has no corresponding matches in the memory. Content in the enterprise is more regular and repetitive, and thus the TAUS database can boast higher match rates.

The quest for an ideal TM + MT combo

According to the dataset, machine translation is nowhere close to replacing TM in the business-boosting human translator productivity. MT accounts for roughly only 12.5 percent of segments. Furthermore, most MT suggestions require some editing. However, drawing final conclusions would be unfair considering that the sample for MT is still very small.

TAUS is looking for an ideal threshold on which to replace TM with MT. The report splits the sample into two workflows. In the first, there is translation memory with humans. In the second MT-supported workflow, the text goes through the TM first, and machine translation is used for segments where memory matches are below a quality threshold. At the moment, an early speculation is that the best threshold is a 70 percent match rate, after which MT becomes inefficient. Companies use this cut-off point in practice, and TAUS’s objective is to check whether there is data to prove this is the most efficient way.

The search continues — through Levenshtein edit distances and tag-riddled segments.

400 words an hour — the average productivity for a human translator

Finally, the dataset gives insight into human productivity. TAUS offers an online tool to benchmark, but the data there is skewed because most of the volume comes from TM and MT. Using the report data on human translation volumes we were able to configure the visualization for languages with significant human-made volumes only (German, Baltic, Russian). The result: 400 words an hour without the help of technology. Pure, un-augmented human brain power.

A 7-hour full work day nets about 2,800 words, or roughly 11 pages. If only someone could sit for 7 hours straight to perform uninterrupted translating…

TMS databases could offer a more precise picture

TAUS QD database of 100 million is tiny compared to the massive silos on which cloud-based TMS companies sit. For instance, Memsource claims to have processed more than 20 billion words last year, but with about one third actually translated. XTM says their public cloud clocked 14 billion in source words, and on private cloud clients uploaded billions more. In a recent presentation, Smartling claimed that they translated  8.5 billion words in 2017.

Companies track word numbers differently, and none of them believe each other, but it’s a good measure for the order of magnitude.

Though smaller, TAUS database has the benefit of neutrality. You can believe that their numbers are true.

Reference: http://bit.ly/2vOOP49

 

 

 

 

 

 

 

 

 

 

 

A Beginner’s Guide to Machine Translation

A Beginner’s Guide to Machine Translation

What is Machine Translation?

Machine translation (MT) is automated translation by computer software. MT can be used to translate entire texts without any human input, or can be used alongside human translators. The concept of MT started gaining traction in the early 50s, and has come a long way since. Many used to consider MT an inadequate alternative to human translators, but as the technology has advanced, more and more companies are turning to MT to aid human translators and optimize the localization process.

How Does Machine Translation Work?

Well, that depends on the type of machine translation engine. There are several different kinds of MT software which work in different ways. We will introduce Rule-based, Statistical, and Neural.

Rule-based machine translation (RBMT) is the forefather of MT software. It is based on sets of grammatical and syntactical rules and phraseology of a language. RBMT links the structure of the source segment to the target segment, producing a result based on analysis of the rules of the source and target languages. The rules are developed by linguists and users can add terminology to override the MT and improve the translation quality.

Statistical MT (SMT) started in the age of big data and uses large amounts of existing translated texts and statistical models and algorithms to generate translations. This system relies heavily on available multilingual corpora and an average of two millions words are needed to train the engine for a specific domain – which can be time and resource intensive. When a using domain specific data, SMT can produce good quality translations, especially in the technical, medical, and financial field.

Neural MT (NMT) is a new approach which is built on deep neural networks. There are a variety of network architectures used in NMT but typically, the network can be divided into two components: an encoder which reads the input sentence and generates a representation suitable for translation, and a decoder which generates the actual translation. Words and even whole sentences are represented as vectors of real numbers in NMT. Compared to the previous generation of MT, NMT generates outputs which tend to be more fluent and grammatically accurate. Overall, NMT is a major step in MT quality. However, NMT may slightly lack behind previous approaches when it comes to translating rare words and terminology. Long and/or complex sentences are still an issue even for state-of-the-art NMT systems.

The Pros and Cons of Machine Translation

So now you have a brief understanding of MT – but what does it mean for your translation workflow? How does it benefit you?

  • MT is incredibly fast and can translate thousands of words per minute.
  • It can translate into multiple languages at once which drastically reduces the amount of manpower needed.
  • Implementing MT into your localization process can do the heavy lifting for translators and free up their valuable time, allowing them to focus on the more intricate aspects of translation.
  • MT technology is developing rapidly, and is constantly advancing towards producing higher quality translations and reducing the need for post-editing.

There are many advantages of using MT but we can’t ignore the disadvantages. MT does not always produce perfect translations. Unlike human translators, computers can’t understand context and culture, therefore MT can’t be used to translate anything and everything. Sometimes MT alone is suitable, while others a combination of MT and human translation is best. Sometimes it is not suitable at all. MT is not a one-size-fits-all translation solution.

When Should You Use Machine Translation?

When translating creative or literary content, MT is not a suitable choice. This can also be the case when translating culturally specific-texts. A good rule of thumb is the more complex your content is, the less suitable it is for MT.

For large volumes of content, especially if it has a short turnaround time, MT is very effective. If accuracy is not vital, MT can produce suitable translations at a fraction of the cost. Customer reviews, news monitoring, internal documents, and product descriptions are all good candidates.

Using a combination of MT along with a human translator post-editor opens the doors to a wider variety of suitable content.

Which MT Engine Should You Use?

Not all MT engines are created equal, but there is no specific MT engine for a specific kind of content. Publicly available MT engines are designed to be able to translate most types of content, however, with custom MT engines the training data can be tailored to a specific domain or content types.

Ultimately, choosing an MT engine is a process. You need to choose the kind of content you wish to translate, review security and privacy policies, run tests on text samples, choose post-editors, and several other considerations. The key is to do your research before making a decision. And, if you are using a translation management system (TMS) be sure it is able to support your chosen MT engine.

Using Machine Translation and a Translation Management System

You can use MT on its own, but to get the maximum benefits we suggest integrating it with a TMS. With these technologies integrated, you will be able to leverage additional tools such as translation memories, term bases, and project management features to help streamline and optimize your localization strategy. You will have greater control over your translations, and be able to analyze the effectiveness of your MT engine.

Reference: http://bit.ly/2P85d7P

TRANSLATION TECH GOES MEDIA

TRANSLATION TECH GOES MEDIA

Four out of the five fastest-growing language services companies in 2018 are media localization specialists. The media business has seen a boom over the last two years, and traditional translation companies are taking notice. Media localization won’t stay an uncontested insular niche for long. In fact, conventional LSPs and technology providers are moving into this sector and expanding their technical capabilities this year.

HERE ARE A FEW EXAMPLES, WITH MORE TO FOLLOW…

Omniscien launched an automated subtitling tool

Omniscien, previously Asia Online, is best known for its trainable machine translation software, but now they are going into a new area – video subtitling. Omniscien has just started selling Media Studio, which was built based on product requirements from iflix, a Malaysian competitor to Netflix.

Under the hood Media Studio has machine learning components: audio transcription, dialog extraction, and neural MT engines pre-trained for subtitles in more than 40 language combinations. The technology is able to create a subtitle draft out of a raw video already in the target language. It can even adjust timings and split long sentences into multiple subtitles where necessary. And it’s learning all the time.

For the human part of the work, Media Studio includes a web-based subtitle editor and a management system, both including a significant range of features right from the start. Translators can edit time codes in a drag-and-drop fashion, skip parts of the video without speech, customize keyboard shortcuts, and more. Project managers can assign jobs and automatically send job notifications, track productivity, and MT leverage.

The video is hosted remotely and is streamed to linguists instead of sending complete films and episodes. This adds a security layer for the intellectual property. No one in the biz wants the next episode of the Game of Thrones to end up on thepiratebay.org faster than it would on a streaming service. Linguists in low-bandwidth countries can download videos in low quality and with a watermark.

On the downside, this new tool does not integrate with existing CAT and business management systems for LSPs out of the box, doesn’t have translation memory support or anything else that would make it fit as one of the blades in the Swiss army knife of LSP technology.

According to Omniscien’s CEO Dion Wiggins, iflix has processed hundreds of thousands of video hours through the system since its inception in late 2016. By now, three more large OTT providers have started with Media Studio. Content distribution companies are the main target for the tool, but it will be available for LSPs as well once the pricing is finalized.

GlobalLink deployed subtitle and home dubbing software

At a user conference in Amsterdam this June, TransPerfect unveiled a new media localization platform called Media.Next. The platform has three components:

The subtitle editor is a CAT-tool with an embedded video player. Translators using this platform can watch videos and transcribe them with timings, possibly with integrated speech recognition to automatically create the first pass. As they translate using translation memory and termbase, they are able to see the subtitles appear on the screen.

The home dubbing is all about the setup on the voice-actor side. TransPerfect sends them mics and soundproofing so that recording can happen at home rather than at a local audio studio.

A media asset management platform stores videos at a single location and proxies them to the translator applications instead of sending complete files over the Internet, similar to Omniscien’s approach.

The official launch of TransPerfect’s Media.NEXT is scheduled for mid-August.

Proprietary tech launched earlier this year

TransPerfect’s tech is proprietary, meant to create a competitive advantage. Media localization companies such as Zoo Digital and Lylo took a similar approach. They have launched cloud subtitling and dubbing platforms, but continue to keep technology under the radar of other LSPs.

The idea of “dubbing in the cloud” is that it gives the client visibility into the actual stage of the process, and flexibility with early-stage review and collaboration with the vendor. The same idea permeates Deluxe Media’s platform Deluxe One unveiled in April this year. It’s a customer portal that provides clients with access to multiple services and APIs.

Deluxe One user interface

MemoQ and Wordbee add view video preview for subtitling

At the same time, subtitling capabilities are beginning to make their way into tools that are available to hundreds of LSPs around the world.

Popular translation editor memoQ has added a video player with a preview in their July release. The editor now opens the video file at the point that is being translated and displays the translated text so that translators can check it live. It can also show the number of words per minute, characters per second, or characters per line.

A similar preview appears in Wordbee. The embedded video player can open videos from an URL, or play clips that are uploaded to the editor directly. The initial release includes a character limitation feature to keep subtitles concise, and anchoring: clicking on the segment with the text rewinds the video to that text.

This is a step showing memoQ’s and Wordbee’s move deeper into media, and differentiating them from other TMS.

So far, few TMS had video previews, one of them was Smartcat. Subtitling functionality in Smartcat has been developed in 2013 for a special project, crowdsourced localization of e-learning platform Courserra. Today, users need to enable subtitling functionality on request. The feature set available includes a video player, timecode parsing, and anchoring. Subtitling user numbers in Smartcat are rising, according to product manager Pavel Doronin.

Back to memoQ and Wordbee, their development teams probably will need to expand the list subtitling features over time: first of all, timecode editing. Moreover, memoQ and Wordbee support .SRT extension, whereas Omniscien’s tool supports TTML as well: a more advanced format that allows manipulating subtitle colors, position on screen and formatting. TTML might become more important for video on demand work and streaming platforms, for instance, it is the format that Netflix uses.

Future “luxury” features could include character tracking with descriptions explaining their voice and preferred vocabulary, support for the speech-to-text technology, audio recording, etc.

Subtitling commoditization looms

Subtitling is not new to the translation industry, and almost every mature CAT/TMS supports .srt SubRip text files. However, linguists have to run a third-party video player in a separate window to see their work. They also have to reload and rewind every time to see changes in the subtitles.

That’s why in professional scenarios, subtitlers often use Amara, Subtitle Workshop, Oona captions manager, CaptionHub or similar specialized tools. These tools came from outside the language industry and didn’t support translation memories, term bases, and embedded MT.

Previous attempts to create tools that combine the best of two worlds didn’t quite meet with commercial success. Years following the launch, user numbers for dotsub.com, hakromedia SoundCloud, and videolocalize.com stayed limited. So far, most language industry professionals viewed media localization as a niche service rather than as a growth area. As a result, they didn’t invest in specialized departments and software. But with video content increasing in share, and with media companies demonstrating record revenues, this might eventually change.

However, by the time it does change, translation tools may achieve a “good enough” capability. Fast-forward 1-2 years – most LSPs might be able to subtitle without extra investment or training. It will become even easier to enter into subtitling and compete, leading to price pressure. Subtitling may turn into an even more crowded and low-margin space before you can say “commoditization”.

Dubbing: Home studio vs studio M&A strategy

Dubbing, on the other hand, is a different kind of deal.

So far, the dubbing market has been dominated by larger companies such as Deluxe and SDI Media that provide voice talent in physical sound studios located in multiple countries. Perhaps one of the best examples of this would be Disney’s Let It Go which has been translated into at least 74 languages.

Infrastructure for such projects is costly to build and maintain. Brick-and-mortar studios have high bills and need a constant flow of work to stay profitable. Projects might be hard to find for second-tier language outlets. To have a French studio overloaded and a Croatian studio suffering losses year after year is a realistic scenario for a network company.

The virtual/home studio approach being used by newer players in this field such as TransPerfect, Zoo Digital and Lylo Media, is more scalable and provides acceptable quality for most clients. But will it be enough for high-profile content owners that award the largest contracts?

If the home studio approach produces sustainable growth, commercial software vendors will jump in and replicate the technology, leading to lower entry to dubbing. However, if it fails over 2018-2019, instead M&A will become the go-to-market strategy in the media localization space. Watch out for smaller studio acquisition frenzy!

Reference: http://bit.ly/2LVhf6C

Translating in-house? Here’s what you need to know

Translating in-house? Here’s what you need to know

How does your law firm get translations done? If you do some or all of it in-house, you may run your own translation team; rely on the language skills of lawyers, knowledge managers and other colleagues; or use a combination of the two.

Given the ever-present need to work as efficiently as possible in order to meet delivery deadlines, we’ve seen a number of ways in which law firms try to speed up their in-house translation process. Here are a few of the things they’ve learned along the way:

Be wary of manually reusing content

Translators may look to reuse content from previous translations by copying and pasting chunks of text. It’s an exceedingly common practice — but one that’s fraught with danger. Just as with drafting precedents, starting with existing documents risks missing subtle differences between cases — and horror stories abound of the consequences of these errors (including houses being sold to the wrong person). The end result: most of the time saved reusing content could well be spent fixing errors (either those caught internally or spotted by your client).

Many hands make light work?

Another option is to split a document into sections for several colleagues to work on concurrently. This may be faster and may reduce the chance of errors when compared to reusing old documents, but it raises concerns around consistency between different translators. It also means great care must be taken when the translated document is stitched back together at the end of the process.

Free tools come with a cost

It may be tempting to turn to free online translation tools like Google Translate to make the work go faster, especially for small jobs like birth certificates or even tweets. But law firms are generally against the idea — and understandably so. For starters, free tools aren’t designed to translate complex legal terminology, or to render legal concepts from one jurisdiction to another — so there’s no guarantee of quality or accuracy. Reviewing and amending the translated output could therefore take more time than doing the work from scratch. Besides that, using such tools could put confidential or valuable information at risk.

The right technology is a lifesaver

One reason many law firms struggle to get translations done quickly, accurately and consistently is that they’re doing the work using standard office productivity apps. But as these apps aren’t designed with translators’ needs in mind, they don’t include the features needed to make translation an efficient process.

That’s why you’ll find that firms who are translating in-house successfully are often using computer-assisted translation (CAT) tools.

CAT tools are designed to help translators work faster and smarter. Using technology developed specifically to support translation work, it can:

  • Raise quality and consistency, and accelerate handling of repetitive content, with translation memories and terminology databases that simplify reuse of previously approved content
  • Increase translators’ productivity with features to increase the speed of translation while safeguarding against mistakes
  • Turn lengthy documents round faster by making it easy for several people to collaborate on the same translation

Balance speed, quality and cost

At the end of it all, the translation challenge comes down to three variables: cost, quality, and speed. Just as with the outsourcing model, translating in-house brings unique challenges to maintaining the balance between these three variables.

CAT tools can help law firms tip the scales in their favor, by giving them a way to improve speed without compromising on quality.

Reference: http://bit.ly/2OgpXcg

Why People Leave the Language Industry

Why People Leave the Language Industry

Working in a language service provider (LSP) can be an exciting and challenging place to be: managing complex projects, figuring out best workflows, delivering to demanding deadlines, working with teams of people from diverse cultural and linguistic backgrounds… But what motivates people who have studied languages and translation to leave behind a career in the language industry? And where are they now?

Slator conducted a number of interviews and spoke to individuals who had all worked for 3+ years in a sales or operational capacity within a language service provider in either France or the UK. All had studied languages and / or translation to degree or post-grad level.

Respondents revealed that they were originally attracted to the language industry for a variety of reasons, including the appeal of a “multicultural environment” coupled with a “passion for languages”, “the ability to be able to communicate without borders”, having the opportunity to use languages on a daily basis and “learning more about…the art of translation and culture surrounding the language.”

The aspects of their roles that they enjoyed most ranged from “the camaraderie and internationalism” to “client side, travel and coaching people” and the fact that “it was always busy and fast-paced which kept me on my toes.”

International Environment

Asked what they liked about the companies (LSPs) they worked in, one respondent identified “the feel you were doing something important for large clients” and another said that she had worked in “a small company and became friends with colleagues”. Again, “international environment” and “international focus with different office locations” featured prominently.

On the benefits of the industry as a whole, one respondent said she “learned a lot about finance and legal, practicing at least two languages daily”. Innovation and technology also contributed to the appeal: “it is forever evolving and has an exciting future ahead,” one said, while another thought that “interesting technology advances” were a distinct positive.

So, despite the lure of an international and evolving market, where you can meet people and travel, what factors are leading individuals to leave the industry in favor of alternative careers? And what opportunities are they seeking out?

Why People Leave

We asked respondents to describe their reasons for leaving the language industry in their own words. One person, who worked for six years in project management, said that it was “impossible to progress, [there was] no reward for going the extra mile, [and] no human contact”.

Another, who spent three years in the industry in a project management role echoed this sentiment, saying that “I found I’d reached a plateau, both in terms of pay and also in terms of what my job entailed (it became quite samey)”. She went on to say “I figured my skills could be transferred into another sector which could offer more variety and options for the future”.

60% of respondents cited lack of progression for the primary reason for leaving.

One individual saw few differentiating factors between LSPs saying “I felt that most LSPs were much of the same and there was little difference between being a project manager at any of them. I decided to leave to travel for a few months and didn’t feel like I wanted to return to the same career.” Yet another thought that “the progression was too slow. Individuals were being overloaded and therefore project standards were slipping.” One person identified a culture of blame in the company he left behind to pursue a teaching career.

Only one respondent gave reasons that were uniquely “pull factors” and said that she had left “to pursue other avenues in a different field and focus on the transferable skills gained during my time at the various LSPs, notably, leadership and coaching” after 13 years in the industry.

When pressed, 60% of respondents cited lack of progression for the primary reason for leaving, with comments including “impossible to progress”, no “options for career progression” and “being overlooked for promotion”.

Stepping Stone to Another Career

A few interview questions centered on the respondents’ experience of pursuing a new career – how easy did they find it to secure a new role and how had working the language industry prepared them for a new line of work.

Most (80%) felt that the skills they had acquired were relevant to other industries. And the respondents had moved into a variety of roles and industries, from HR and Recruitment to Wine Buying, Events Coordination for a cloud networking company, Supply / Logistics, Marketing and Teaching.

Most found it “quick”, “easy” or “relatively easy” to find a new role, suggesting that language industry leavers are in high demand across a wide range of industries.

Most (80%) do not regret the decision to leave, while 20% are unsure (perhaps because it is still early days), but none are openly dissatisfied with their choice to pursue a different career. Similarly, none could foresee returning to the industry out of choice, e.g. “I hope it does not happen, but if it does, it will be for a short time as I am not willing to stay in this industry.”

The exception to this seemed to be the possibility of returning in the capacity of a freelance linguist: “The flexibility of being a freelance translator might tempt me at some point in the future. (I still do some freelance translating on the side of my current job)” and “If there was a feasible option of getting into interpreting I’d also be tempted.”

What Can We Do Better?

In a tick box question, respondents selected the factors that would have led them to stay in the language industry. A more interesting role and better long-term career opportunities topped the list, with more money, and better progression, management and work-life balance also emerging as possible difference-makers.

Participants were also asked what advice and recommendations they would have for employers in the language industry through questions such as “If you could change one thing about the language industry what would it be?” and “What, if anything, could your company could have done differently that would have encouraged you to stay in the language industry?”

Of the respondents who said that there was something their company could have done to retain them (60%), all said it would have involved better career developments or pay.

Things that people hoped to change about the industry at large included:

  • “Better salaries compared to Project Managers in other industries”;
  • “Make its employees feel more valued. The turnover was very high as extra duties were heaped on with no reward”;
  • “Make people more aware of how much work is behind a simple translated document, have respect for others’ job/work”;
  • “More honesty around quality assurance”

As the industry matures with a wider breadth of job roles and shows healthy levels of employment globally, companies must pay attention not to lose key talent to competing industries. Some might argue that LSPs are necessarily bottom heavy and that attrition among the more junior levels of project managers and sales employees is to be expected as it is not possible for everyone to progress. But turnover of staff carries a huge cost – not least for training, recruitment and extra overtime – one that can be driven down by heeding advice and implementing good people management strategies.

Reference: http://bit.ly/2LPiFhS