Month: March 2018

SDL Survey Results for Corporate Translation Technology Survey 2017

SDL Survey Results for Corporate Translation Technology Survey 2017

SDL published a new report contains the results of a survey about  Corporate Translation Technology Survey 2017 . The report starts with defining  the meaning of corporate included in this survey. By corporate, SDL means private- and public-sector organizations that are generating content to be translated. It therefore doesn’t include language service providers (translation agencies) or freelance translators.  As a result, this new survey reveals some answers to the question:


Given that cost is an ever-present issue for corporates, and that demand for translation continues to grow with no end in sight, just how are
corporates tackling their quest for quality in translation?

The survey covered the below regions with a total of 554 respondents from public and private sector corporates.

Quality Priority and Challenge:

The survey results confirm what we’ve learned before about quality and rising demand:

• Almost 9 out of 10 respondents agree that quality is much more important than cost.
• Maintaining quality also looms largest when they list their top challenges: now and in 5 years time.
• And the pressures of demand can clearly be seen in the other top challenges: shortened timelines and increased internal demand. These demand pressures are expected to be more of a challenge in 5 years.

The results shed light on the aforementioned challenges along with other challenges that face translation industry nowadays, such as:

  • Lack of qualified translators.
  • Educating stakeholders on the translation process.
  • Translation budgets not increasing in line with demand.
  • Increase in the number of language pairs to be translated.

The report tries to solve these challenges by asking  very important questions. The first question: “is outsourcing an answer?“. Then, it shows in numbers how respondents feel about and handle outsourcing and what are their different motives.

The second question: “is technology an answer?“. In this point, the survey results showed that  94% of respondents agree that they find translation technology vital to managing translation demand, and that two-thirds (66%) of respondents’ organizations use computer-assisted
translation (CAT). There are, however, significant regional differences in CAT use: it’s 73% for North America, 71% for Europe, and only 48% for APAC. 

CAT makes translators more productive, especially through the use of translation memory to speed up translation of content previously
encountered and simplify the re-use of quality work. How much of the content translated by your organization would you say is brand new vs previously translated? For the survey respondents, on average, it’s almost 50-50.

Speaking about technology and CAT tools, has led to an eventual question, is Machine Translation helpful or not?.  Indeed, 61% of respondents agree that machine translation is essential to coping with increasing translation demands. But only just over a quarter (28%) are using machine translation, with Europe (25%) lagging behind North America (35%) and APAC (31%).

Regarding the frequency of post-editing after using machine translation. Only 16% are occasionally, rarely or never post-editing, with 78% post-editing most or all of the time.

Then the report moves to terminology management tools and how they may help in the quality challenge. For corporates, maintaining quality in the face of increasing demand is not just about individual translation productivity, but at least as much about ensuring consistency across projects, translators, and the wider business. Terminology management tools help with consistency — but their value can only be fully realized if termbases can be efficiently shared.

The report ends with recommendations to solve some challenges such as outsourcing and machine translation.

To download the full report: https://goo.gl/L2vGZE

MemoQ’s First Release in 2018: MemoQ 8.4

MemoQ’s First Release in 2018: MemoQ 8.4

Kilgary released its MemoQ 8.4, its first release for 2018. Improvements come in five main areas: user experience, terminology, filters in memoQ, performance, and server workflows. Read on for details:

1- User Experience 

A- Customer Insights Program

The memoQ Customer Insights program will feature two major initiatives:

Usage Data Collection: When you work with memoQ, you are given the choice to enable sending data about how you use the software. Not all types of data will be collected. For more details, check here: https://goo.gl/fgppMh

The Design Lab: A loosely knit community where you can share your insights, opinions and knowledge. In exchange, we will evolve memoQ to be a user-friendly tool that meets your needs and solves your problems. For more details and how to join: https://goo.gl/dbHHeD

B- Comments in online projects

We have re-worked the way comments work in online projects. Now, project managers can delete any comments anywhere. And, non PM users can only delete their own comments. Also, users can edit their own comments only.

C- Task Tracker Progress Messages

In memoQ 8.4, Task Tracker progress messages are shown more consistently. From now on, the Task Tracker will display proper progress messages whenever TMs and TBs are exported. When you export a TM or TB, the message “In progress…” will be displayed as soon as the export begins, and “Done.” when the export completes.

In addition, you will be able to open the location where the export was saved by using the Open folder icon.

2- Terminology

A- Import and export term bases with images

With memoQ 8.4, you can now import and export term bases with images.

B- Forbidden terms in the spotlight

MemoQ 8.4 adds new functionality to work with forbidden terms more effectively and -transparently. It will be marked in the term editor for easy identification. It will be highlighted in red for exported and imported term bases.

C- Filters & QA settings

MemoQ 8.4 features small improvements that will facilitate the way you work with terminology while boosting efficiency. You can now determine which of the term bases assigned to the project you want to use for quality assurance in a specific project.

D- More effective stop word lists

MemoQ 8.4 improves stop word list functionality to make term extraction sessions more productive. By improving your stop word lists you can reduce the number of term candidates you need to process in a term extraction session.

E- Filter filed in term extraction

MemoQ 8.4 introduces a more user-friendly filter field on the term extraction screen featuring the history of the term extraction’s session.

F- Smart search settings in QTerm

From now on, when you log into QTerm, you will see the same settings you used the last time on the search page (term bases to search, view, languages, term matching). This is particularly useful if you typically use QTerm for term lookup in a specific language combination and/or with specific term bases.

G- Entry relationships in QTerm

If you establish a symmetrical (homonym, synonym, antonym, cohyponym) or an anti-symmetrical (hyponym, hypernym) entry relationship in one entry with another, the corresponding relationship is also created in the other entry.

H- Easy Term Search

MemoQ 8.4 now offers memoQWeb external users simple and easy access to QTerm term bases for lookup.

I- Filtering Options

The “Begins with” filter condition and search option has been revamped and it now features a more user-friendly term matching interface.

3- Performance

A- Improvements in responsiveness​

When you download memoQ 8.4, you will experience performance improvements in the following areas:
  • Opening the memoQ dashboard,
  • Opening projects,
  • Opening translation documents,
  • Scrolling through resources,
  • Faster rendering of various screens.
Note: The degree of improvements in performance you experience depends on your hardware configuration.

B- MemoQ server back-up

With memoQ 8.4, backing up your server should be faster. We have improved the performance of this task by decreasing back-up time by up to 50%.

Note: The improvement in backup duration may not be significant for memoQ Servers running on SSD drives.

4- Document Import and Export

A- Import filter for subtitles and dubbing script

The new import filter in memoQ 8.4 can handle two subtitle formats:

  • .srt files
  • custom-made .xlsx.

The preview displaying live video will be a plugin based on Preview SDK.

B- ZIP Filter

The new filter offers a generic option for handling ZIP packages. It will display the files of the archive as embedded documents. It will also be possible to import only some of them.

5- Server-to-server Workflows

A- Lookup on Enterprise TM

Until now, memoQ servers around the world resemble big powerful giants that are unable of “talking to each other”. This is now going to change.
MemoQ is investing effort in developing this new technology that will add significant value to customers using the following workflows:
  • Client + Vendor
  • Making use of several memoQ servers.

The projects created from packages now have direct access to the parent TMs. It is done through the child server, so firewalls can be configured to let the traffic through. Project Managers can deliver with one click.

Edit Distance in Translation Industry

Edit Distance in Translation Industry

In computational linguistics, edit distance or Levenshtein distance, is a way of quantifying how dissimilar two strings (e.g., words) are to one another by counting the minimum number of operations required to transform one string into the other.  The edit distance between (a, b) is the minimum-weight series of edit operations that transforms a into b. One of the simplest sets of edit operations is that defined by Levenshtein in 1966 which are:

1- Insertion.

2- Deletion

3- Substitution.

In Levenshtein’s original definition, each of these operations has unit cost (except that substitution of a character by itself has zero cost), so the Levenshtein distance is equal to the minimum number of operations required to transform a to b.

For example, the Levenshtein distance between “kitten” and “sitting” is 3. A minimal edit script that transforms the former into the latter is:

  • kitten – sitten (substitution of “s” for “k”).
  • sitten –  sittin (substitution of “i” for “e”).
  • sittin –  sitting (insertion of “g” at the end).

What are the application of edit distance in translation industry?

1- Spell Checkers

Edit distance is applied where automatic spelling correction can determine candidate corrections for a misspelled word by selecting words from a dictionary that have a low distance to the word in question.

2- Machine Translation Evaluation and Post Editing

Edit distance can be used to compare a postedited file to the machine translated output that was the starting point for the postediting. When you calculate the edit distance, you are calculating the “effort” that the posteditor made to improve the quality of the machine translation to a certain level. Starting from the source content and same MT output, if you perform a light postediting and a full postediting, the edit distance for each task will be different, and the human quality level is expected to have a higher edit distance, because more changes are needed. This means that you are measuring light and full postediting using the edit distance.

Therefore, the edit distance is a kind of “word count” measure of the effort, similar in a way to the word count used to quantify the work of translators throughout the localization industry. It also helps in evaluating the quality of MT engine by comparing the raw MT to the post edited version by a human translator.

3- Fuzzy Match

In translation memories, edit distance is the technique of finding strings that match a pattern approximately (rather than exactly). Translation memories provide suggestions to translators, and fuzzy matches are used to measure the effort made to improve those suggestions.

Machine Translation and Compliance

Machine Translation and Compliance

 

Compliance management is no simple task in today’s world. The sheer volume of data involved is intimidating enough. But when that data is in multiple languages, you have an additional layer of complexity to manage as well as another significant expense to budget for.

Machine translation is no replacement for expert human translators. But it can help solve some of the compliance problems multicultural organizations face.

Internal Compliance Monitoring

Ideally, organizations should aspire to catch (and end) compliance issues as early as possible. Firing employees is an expense in and of itself, and if you address these issues quickly you can often solve the problem with education rather than termination. Meanwhile, whether the behavior in question is illegal, unethical or just plain risky, the sooner you put a stop to it, the less likely you are to get stuck with expensive fines.

Is your organization monitoring employee communication to identify concerning behavior? Machine translation makes it possible to understand, analyze, and review large amounts of archived data in foreign languages, so you can stop problems before they start.

eDiscovery Compliance

Businesses today generate vast amounts of electronic documents and communications. That makes eDiscovery like looking for needles in a haystack, sifting through tons of irrelevant information to find materials that are relevant to the case. And of course, there are penalties for not identifying and producing all of the necessary documents in a timely manner.

The most workable solution is appropriately-deployed machine translation followed by review and post editing from human experts, when required. Machine translation is not a substitute for human translators. That said, in large cross-border cases, machine translation can be used to produce documents for opposing counsel, and then human translators can translate only those documents that seem relevant. Machine translation can also help your team identify and classify large numbers of documents for review.

Data security

Using machine translation when applicable can also improve data security, as long the platform used is secure. (Note: That means free platforms are strictly off limits!) No matter how careful your employees are, each person who accesses a document creates a new security risk. Machine translation can reduce the number of people who need that access to reduce security vulnerabilities.

Machine Translation and Compliance Budgets

As the cost of compliance goes up, so does the pressure for businesses to make their compliance procedures more efficient. Machine translation can help optimize your compliance budget by only using human translators when necessary.

When Machine Translation is a Compliance Nightmare

When wielded wisely, machine translation can be a powerful weapon in your compliance arsenal. But it can also be risky. For instance, if individuals in your organization rely on free online translation services, your data security could be at risk.

Last year, employees at Norway’s Statoil discovered that sensitive data translated using Translate.com’s free MT tool was available to the public via a simple Google search.

Though the quality of machine translation has improved by leaps and bounds during the past few years, it’s still not a substitute for human translators when clear and accurate translations are required. If inaccuracies make your translations misleading or incomprehensible, that’s a compliance risk, too.

 

Reference: https://goo.gl/krFhns

LookAhead Feature – Towards Faster Translation Results

LookAhead Feature – Towards Faster Translation Results

 

 

To facilitate your work on SDL Trados Studio 2017 SR1, SDL powered it with LookAhead feature. LookAhead is an in-memory lookup and retrieval mechanism which ensures that your translation search results are displayed fast when you activate a segment for translation. LookAhead technology radically improves the retrieval speed of TM search results, especially for long or complex source text. Once your source text is loaded in SDL Trados Studio , the application starts matching source text strings against the available translation resources (TMs, termbases or machine translation) in the background for the next two segments after the current one. As a result, you are instantly provided with translation hits for each segment that has matching translation results.

How to enable LookAhead?

  1. Go to File, and select Options.
  2. In the Options dialog, in the navigation tree, expand Editor.
  3. Select Automation.
  4. Under Translation Memory, select the Enable LookAhead checkbox.

How to Cut Localization Costs with Translation Technology

How to Cut Localization Costs with Translation Technology

What is translation technology?

Translation technologies are sets of software tools designed to process translation materials and help linguists in their everyday tasks. They are divided in three main subcategories:

Machine Translation (MT)

Translation tasks are performed by machines (computers) either on the basis of statistical models (MT engines execute translation tasks on the basis of accumulated translated materials) or neural models (MT engines are based on artificial intelligence). The computer-translated output is edited by professional human linguists through the process of postediting that may be more or less demanding depending on language combinations and the complexity of materials, as well as the volume of content.

Computer-Aided Translation (CAT)

Computer-aided or computer-assisted translation is performed by professional human translators who use specific CAT or productivity software tools to optimize their process and increase their output.

Providing a perfect combination of technological advantages and human expertise, CAT software packages are the staple tools of the language industry. CAT tools are essentially advanced text editors that break the source content into segments, and split the screen into source and target fields which in and of itself makes the translator’s job easier. However, they also include an array of advanced features that enable the optimization of the translation/localization process, enhance the quality of output and save time and resources. For this reason, they are also called productivity tools.

Figure 1 – CAT software in use

The most important features of productivity tools include:

  • Translation Asset Management
  • Advanced grammar and spell checkers
  • Advanced source and target text search
  • Concordance search.

Standard CAT tools include Across Language ServerSDL Trados StudioSDL GroupShare, SDL PassolomemoQMemsource CloudWordfastTranslation Workspace and others, and they come both in forms of installed software and cloud solutions.

Quality Assurance (QA)

Quality assurance tools are used for various quality control checks during and after the translation/localization process. These tools use sophisticated algorithms to check spelling, consistency, general and project-specific style, code and layout integrity and more.

All productivity tools have built-in QA features, but there are also dedicated quality assurance tools such as Xbench and Verifika QA.

What is a translation asset?

We all know that information has value and the same holds true for translated information. This is why previously translated/localized and edited textual elements in a specific language pair are regarded as translation assets in the language industry – once translated/localized and approved, textual elements do not need to be translated again and no additional resources are spent. These elements that are created, managed and used with productivity tools include:

Translation Memories (TM)

Translation memories are segmented databases containing previously translated elements in a specific language pair that can be reused and recycled in further projects. Productivity software calculates the percentage of similarity between the new content for translation/localization and the existing segments that were previously translated, edited and proofread, and the linguist team is able to access this information, use it and adapt it where necessary. This percentage has a direct impact on costs associated with a translation/localization project and the time required for project completion, as the matching segments cost less and require less time for processing.

Figure 2 – Translation memory in use (aligned sample from English to German)

Translation memories are usually developed during the initial stages of a translation/localization project and they grow over time, progressively cutting localization costs and reducing the time required for project completion. However, translation memories require regular maintenance, i.e. cleaning for this very reason, as the original content may change and new terminology may be adopted.

In case when an approved translation of a document exists, but it was performed without productivity tools, translation memories can be produced through the process of alignment:

Figure 3 – Document alignment example

Source and target documents are broken into segments that are subsequently matched to produce a TM file that can be used for a project.

Termbases (TB)

Termbases or terminology bases (TB) are databases containing translations of specific terms in a specific language pair that provide assistance to the linguist team and assure lexical consistency throughout projects.

Termbases can be developed before the project, when specific terminology translations have been confirmed by all stakeholders (client, content producer, linguist), or during the project, as the terms are defined. They are particularly useful in the localization of medical devices, technical materials and software.

Glossaries

Unlike termbases, glossaries are monolingual documents explaining specific terminology in either source or target language. They provide further context to linguists and can be used for the development of terminology bases.

Benefits of Translation Technology

The primary purpose of all translation technology is the optimization and unification of the translation/localization process, as well as providing the technological infrastructure that facilitates work and full utilization of the expertise of professional human translators.

As we have already seen, translation memories, once developed, provide immediate price reduction (that varies depending on the source materials and the amount of matching segments, but may run up to 20% in the initial stages and it may only grow over time), but the long-term, more subtle benefits of the smart integration of translation technology are the ones that really make a difference and they include:

Human Knowledge with Digital Infrastructure

While it has a limited application, machine translation still does not yield satisfactory results that can be used for commercial purposes. All machine translations need to be postedited by professional linguists and this process is known to take more time and resources instead of less.

On the other hand, translation performed in productivity tools is performed by people, translation assets are checked and approved by people, specific terminology is developed in collaboration with the client, content producers, marketing managers, subject-field experts and all other stakeholders, eventually providing a perfect combination of human expertise, feel and creativity, and technological solutions.

Time Saving

Professional human linguists are able to produce more in less time. Productivity software, TMs, TBs and glossaries all reduce the valuable hours of research and translation, and enable linguists to perform their tasks in a timely manner, with technological infrastructure acting as a stylistic and lexical guide.

This eventually enables the timely release of a localized product/service, with all the necessary quality checks performed.

Consistent Quality Control

The use of translation technology itself represents real-time quality control, as linguists rely on previously proofread and quality-checked elements, and maintain the established style, terminology and quality used in previous translations.

Brand Message Consistency

Translation assets enable the consistent use of a particular tonestyle and intent of the brand in all translation/localization projects. This means that the specific features of a corporate message for a particular market/target group will remain intact even if the linguist team changes on future projects.

Code / Layout Integrity Preservation

Translation technology enables the preservation of features of the original content across translated/localized versions, regardless of whether the materials are intended for printing or online publishing.

Different solutions are developed for different purposes. For example, advanced cloud-based solutions for the localization of WordPress-powered websites enable full preservation of codes and other technical elements, save a lot of time and effort in advance and optimize complex multilingual localization projects.

Wrap-up

In a larger scheme of things, all these benefits eventually spell long-term cost/time savings and a leaner translation/localization process due to their preventive functions that, in addition to direct price reduction, provide consistencyquality control and preservation of the integrity of source materials.

Reference: https://goo.gl/r5kmCJ

Wordfast Releases Wordfast Pro 5.4 and Wordfast Anywhere 5.0

Wordfast Releases Wordfast Pro 5.4 and Wordfast Anywhere 5.0

Wordfast today released version 5.4 of its platform independent desktop tool, Wordfast Pro. Notable features and improvements include Adaptive Transcheck, a new Segment Changes report format, a new feedback proxy tool, and the ability to connect to Wordfast Anywhere TMs and glossaries. This latest feature puts the power of server-based TMs and glossaries into the hands of desktop users for free.

Wordfast also recently released Wordfast Anywhere 5.0 which includes a localized user interface (UI) in French and Spanish. The UI is ready to be translated to other languages with a collaborative translation page accessible through a user’s profile.

Wordfast will be showcasing the interconnectivity of Wordfast Pro and Wordfast Anywhere during its 4th annual user conference – Wordfast Forward – to take place on June 1-2, 2018 in Cascais, Portugal. For more details about the program, please see the dedicated conference page.

Reference: https://goo.gl/hstxKp

Adaptive MT – Trados 2017 New Feature

Adaptive MT – Trados 2017 New Feature


SDL Trados Studio 2017 includes new generation of machine translation.

How does it work?

It allows users to adapt SDL Language Cloud machine translation with their own preferred style. There is a free plan and it offers these features:

  • 400,000 machine translated characters per month.
  • only access to the baseline engines, so this means no industry or vertically trained engines.
  • 5 termbases, or dictionaries, which can be used to “force” the engine to use the translation you want for certain words/phrases.
  • 1 Adaptive engine.
  • Translator… this is basically a similar feature to FreeTranslation.com except it’s personalized with your Engine(s) and your termbases.

How does it help?

  • Faster translation with smarter MT suggestions.
  • Easy to use and get started.
  • Completely secure – no data is collected or shared.
  • Unique MT output, personal to you.
  • Access directly within Studio 2017.
  • No translation memory needed to train the MT.
  • Automatic, real time learning – no pre-training required.

What are the available language pairs?

Uptill now, Adaptive MT is available in these language pairs:

English <-> French
English <-> German
English <-> Italian
English <-> Spanish
English <-> Dutch
English <-> Portuguese
English <-> Japanese
English <-> Chinese

For reference: https://www.sdltrados.com/products/trados-studio/adaptivemt/

The Translation Industry in 2022

The Translation Industry in 2022

In this report, TAUS shares predictions for the future of the translation industry in line with their expectation that automation will accelerate in the translation sector during the coming 5 years. The anticipated changes will inevitably bring along various challenges and opportunities all of which are explained thoroughly in the Translation Industry in 2022 Report.

The report explains the following 6 drivers of change

1. Machine Learning

Machine learning (ML) was introduced in the 1950s as a subset of artificial intelligence (AI), to have programs feed on data, recognize patterns in it, and draw inferences from them. 2016 was the year when ML went mainstream, with a lot of applications that were almost unimaginable a few years earlier – image recognition and self-driving cars are just two examples.Computational power and unprecedented advances in deep neural networks will make data-driven technologies astonishingly disruptive. This might be also the case of MT.

As a rule, the growth of machine intelligence represents a threat to many human jobs as people will be replaced by intelligent systems.  The majority of creative jobs is relatively safe while sales jobs could be at risk. The forecast is dubious for technology jobs, but the more senior jobs being relatively secure, while computer programmers and support workers may likely be replaced.  The assumption that jobs requiring manual dexterity, creativity, and social skills are the hardest to computerize is already obsolete: new developments in deep learning are making machines more powerful than anticipated, especially in areas relating to creativity and social interaction. 

In the translation industry – as in other industries – many functions will be affected – whether enhanced, expanded or replaced – by ML.

2. Machine Translation

In the past years NMT has been said to be achieving impressive results, and it is more and more often presented as a replacement for SMT. Advances in artificial neural networks are bringing extremely high expectations, suggesting that NMT could rapidly achieve higher accuracy than SMT. Independent evaluators fnd that NMT translations are more fluent and more accurate in terms of word order compared to those produced by phrase-based systems. Better quality MT will mean that a broader range of document types and audiences can be addressed.
NMT will help the further expansion of speech-to-speech (S2S) technologies, now available mostly as English-based monolingual systems. Transforming these into multilingual systems implies many deep and expensive changes. Most S2S technologies are still at an infancy stage and confned to university labs. NMT will help bring speech-enabled devices to the streets.

MT will lead to the ultimate disruption in the translation industry when, only the premium segment of artsy—and possibly life sciences—
translation will remain tradable.

3. Quality Management

Due to the uncertainties intrinsically involved in translation quality assessment, and the fixity of the relevant concepts in the translation community, users seem now willing to accept good enough MT output, especially for large volumes, delivered virtually in real time. For serviceable MT output with no human intervention downstream, TAUS coined the acronym FAUT (Fully Automated Useful Translation) already in 2007. Investing in quality-related decision support tools has become essential to gain translation project insights and beneft from MT.
Applying machine learning to data-driven translation quality assessment will be a disruptive innovation that will call for a major shift in conception and attitude.  Data-driven applications in translation quality assessment will go from document classifiers to style scorers, from comparison tools to automatic and predictive quality assessment, from content sampling to automatic error detection and identification. The data-driven approach to quality will require another major attitude shift.

4. Data

There is a strong need for data scientists/specialists/analysts, but this profile is still absent from the translation industry.

Data has been the fuel of automation, and after entering the automation era at full speed, we are being challenged with many issues.  Translation data is typically metadata: data about translation that can be harvested downstream the closure of a translation project/job/task.  The analysis of translation data can provide a very valuable insight into the translation processes to find the best resource for a job, to decide what to translate and which technology to use for which content. Translation data will be more and more frequently generated by algorithms. More data will come from rating staff and KPIs. All these kinds of data will come from ML applied to translation management platforms, which will get rid of human involvement.

Erroneously, also data covering multilingual text resources is labeled as translation data. In fact, language data specifically consists of translation memories, corpora, and lexicographical and terminological collections. Of course, all these resources have metadata too, which could be exploited. Stakeholders should become more open and massively start sharing their translation metadata to make it the real big data of the translation industry.

There is a strong need for data scientists/specialists/analysts, but this profile is still absent from the translation industry. Translation companies should be looking out for these specialists who can mine and use data for automation. This will most probably lead to a further reduction of the number of translation companies that are able to float and thrive in a more and more competitive market. The challenge for the next few years might be the standardization of translation data in order to shape it and make it convenient for users to derive the maximum benefits from it.

5.  Interoperability

Interoperability is the ability of two different systems to communicate and work together through a common language or interface. While many other industries have flourished thanks to standardization which led to interoperability, automation and innovation, the translation industry has always suffered from a lack of interoperability. This has been costing a fortune for years, both on the client side (in translation
budgets) and on the vendor side (in revenues).
  Things have been changing a lot since 2011, when TAUS published a report on the costs from
lack of interoperability in the translation industry
. Many blame the lack of compliance to interchange format standards as the primary barrier to interoperability, and no one believes any longer that true interoperability in the translation industry can be achieved only through awareness programs, education, and certifications. Interoperability should come from the adoption of standards created by consortia and not from the dominance of a market leader.

The spreading of MT has forced a breakthrough in the interoperability dilemma, starting a wave of innovation and renewed efforts. Most of these efforts have still been focusing on APIs though, as XML has been established for years as the common language, bringing everyone the industry to find its child formats TMX and XLIFF essentially enough.  So far, most of the many APIs made available are meant to simplify the translation business process and reduce translation management and overhead cost. Only a few have been designed to help disintermediation and facilitate access to services.

In this case, we could expect that the most influential buyers of localization and translation services will advance their requests; the technology vendors with the necessary technological and financial resources will fulfill those requests or even introduce their own solutions on the market, just as it happened in the past.

6.  Academy

Translation education is vocational by definition: it prepares people to work in the trade as translators. None of the skills translation students acquire is anything but sophisticated.  Today, many players in the translation industry complain about the lack of good translators, but they seem to ignore that, more than in many other academic fields, translation education follows obsolete models that are still shaped for the 20th century. To make matters worse, the gap between the academic world and the industry is so wide that, when approaching the job market, translation graduates instantly and bitterly realize they don’t know much about the actual work they are supposed to do. They also discover that the world is not interested in their basic skills.

The future may not really need translators, at least not in the old way, as the audience will become even more forgiving for lesser quality of fast-moving content. A highly-automated localization environment will depend on human skills in quality evaluation, content profiling, cultural advisory, data analysis, computational linguistics, and gradually less and less in post-editing; translating plain text will indeed be a long-tail business.

The success of any innovation depends entirely on the people that are going to nurture, develop, and implement it; in times of exponential growth, education is vital to drive adoption and prepare the next generations of workers. Employers should play a part in closing the skills gap with continued professional training. It is never too early to prepare for the future; vast workforce and organizational changes are necessary to upend stale business models and related processes.

For more details, download the full report.

‘Human Parity Achieved’ in MT

‘Human Parity Achieved’ in MT

According to Microsoft’s March 14, 2018 research paper with the full title of “Achieving Human Parity on Automatic Chinese to English News Translation,” a few variations of a new NMT system they developed have achieved “human parity,” i.e. they were considered equal in quality to human translations (the paper defines human quality as “professional human translations on the WMT 2017 Chinese to English news task”).

Microsoft came up with a new human evaluation system to come to this convenient conclusion, but first they had to make sure “human parity” was less nebulous and more well-defined.

Microsoft’s definition for human parity in their research is thus: “If a bilingual human judges the quality of a candidate translation produced by a human to be equivalent to one produced by a machine, then the machine has achieved human parity.”

In mathematical, testable terms, human parity is achieved “if there is no statistically significant difference between human quality scores for a test set of candidate translations from a machine translation system and the scores for the corresponding human translations.”

Microsoft made everything about this new research open source, citing external validation and future research as the reason.

Reference: https://goo.gl/3iFXXG