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Google releases TensorFlow tutorial to help developers build their own Neural Machine Translation System

Source: Google Research Blog
Story flagged by: Jared Tabor

Machine translation – the task of automatically translating between languages – is one of the most active research areas in the machine learning community. Among the many approaches to machine translation, sequence-to-sequence (“seq2seq”) models [1, 2] have recently enjoyed great success and have become the de facto standard in most commercial translation systems, such as Google Translate, thanks to its ability to use deep neural networks to capture sentence meanings. However, while there is an abundance of material on seq2seq models such as OpenNMT or tf-seq2seq, there is a lack of material that teaches people both the knowledge and the skills to easily build high-quality translation systems.

Today we are happy to announce a new Neural Machine Translation (NMT) tutorial for TensorFlowthat gives readers a full understanding of seq2seq models and shows how to build a competitive translation model from scratch. The tutorial is aimed at making the process as simple as possible, starting with some background knowledge on NMT and walking through code details to build a vanilla system. It then dives into the attention mechanism [3, 4], a key ingredient that allows NMT systems to handle long sentences. Finally, the tutorial provides details on how to replicate key features in the Google’s NMT (GNMT) system [5] to train on multiple GPUs.

The tutorial also contains detailed benchmark results, which users can replicate on their own. Our models provide a strong open-source baseline with performance on par with GNMT results [5]. We achieve 24.4 BLEU points on the popular WMT’14 English-German translation task.
Other benchmark results (English-Vietnamese, German-English) can be found in the tutorial.

In addition, this tutorial showcases the fully dynamic seq2seq API (released with TensorFlow 1.2) aimed at making building seq2seq models clean and easy:

  • Easily read and preprocess dynamically sized input sequences using the new input pipeline in
  • Use padded batching and sequence length bucketing to improve training and inference speeds.
  • Train seq2seq models using popular architectures and training schedules, including several types of attention and scheduled sampling.
  • Perform inference in seq2seq models using in-graph beam search.
  • Optimize seq2seq models for multi-GPU settings.

We hope this will help spur the creation of, and experimentation with, many new NMT models by the research community. To get started on your own research, check out the tutorial on GitHub!

See more >>

Presenting InterpretBank, a new generation of terminology tools for interpreters

Source: TermCoord
Story flagged by: Jared Tabor

Today’s post is about the improvements in the field of terminology support for interpreters through computer-assisted interpreting (CAI) toolsInterpretBank is an example of such tools, it was developed as part of a PhD project and it uses IATE as one of its terminology sources. Our guest writer Claudio Fantinuoli (Johannes Gutenberg University Mainz in Germersheim) tells us all about it. Presenting InterpretBank banner1

InterpretBank is a computer-assisted interpreting (CAI) tool originally developed at the Johannes Gutenberg Universität Mainz in Germersheim as part of a PhD research project. The objective of this project was to create a computer program to support professional interpreters during all phases of the interpreting workflow, from preparation to the act of interpreting. With the aim of improving interpreting quality especially in the context of specialised events, InterpretBank focuses on the creation and management of specialised glossaries as well as on facilitating terminology memorization and retrieval during interpretation.

InterpretBank implements the results of several years of research and the feedbacks of a growing number of users. The tool integrates automatic translation and high-quality terminology databases, such as IATE, to reduce the effort and the time involved in writing glossaries. During preparation, a memorization utility helps interpreters learning the event-related terms. While interpreting, intelligent algorithms allow the user to access relevant terminology quickly and without distracting the interpreter from his or her primary activity – translating between languages. Several independent studies have confirmed that the tool can contribute to increasing the overall interpreting quality. We have now taken a further step forward integrating Speech Recognition.

The interest for the emerging field of CAI tools is growing: InterpretBank is taught in a large number of universities and in dedicated seminars held by professional associations around the world. InterpretBank is the tool of choice not only of many professionals but also when it comes to empirical research in the field of translation technology. In Germersheim, for example, an ongoing PhD project is investigating cognitive load in simultaneous interpreting with the support of terminology management tools.

More information about the tool at

Summer Discounts on The Ultimate Guide to Becoming a Successful Freelance Translator

By: Oleg Semerikov

Looking for a good book to read this summer? Something both insightful and entertaining?
We have an exclusive discount for readers of news on The Ultimate Guide to Becoming a Successful Freelance Translator! Act now to get it at the price of a one-day sunbed rental – the deal is valid till the end of July. Just go to and apply the code “SummerDiscount” during checkout to get a 50% discount.

Topics within the book include:
• Skills and qualifications
• Finding and winning new clients
• Marketing tips for freelance translators
• How to handle some of the trickiest translation problems

There’s also a wealth of information beyond these subjects, including a comprehensive list of resources for translators.

If you’re interested in learning more, visit or our Amazon product page to see what The Ultimate Guide To Becoming A Successful Freelance Translator can do for you.

Once you’ve read the book, please do let us know your feedback.

Highlights from the TAUS Speech-to-speech translation report

Source: Moravia
Story flagged by: Jared Tabor

Speech-to-Speech (S2S) technology seems to have finally stepped out of the realm of science fiction, yet it’s not ready for prime time. In their report published earlier this year, the Translation Automation User Society (TAUS) recognizes this as the paradox the technology currently finds itself in.

The report outlines the current status, future directions, challenges, and opportunities of speech translation. It also includes interviews with 13 people who represent institutes and companies researching and working in this field. We present highlights from the report.

New directions and possibilities

Ike Sagie of Lexifone believes that existing engines for Machine Translation (MT) and Speech Recognition (SR) cannot be used straightaway. Optimization layers and other modifications are also required. Since people speak continuously, there must be an acoustic solution that cuts the flow into sentences or segments and sends the output to an audio optimization layer. Linguistic optimization is needed in the next stage to ensure translation accuracy, such as making sure interrogative sentences are annotated with question marks.

Chris Wendt of Microsoft/Skype states that SR, MT, and Text-to-Speech (TTS) by themselves are not enough to make a translated conversation work. Because clean input is necessary for translation, elements of spontaneous language—hesitations, repetitions, corrections, etc.—must be cleaned between automatic SR and MT. For this purpose, Microsoft has built a function called TrueText to turn what you said into what you wanted to say. Because it’s trained on real-world data, it works best on the most common mistakes, Wendt says.

According to Chengqing Zong from the Chinese Academy of Sciences, future advancements in S2S technology may also include different means of evaluating quality than current automatic techniques such as Bleu Scores. In the future, Zong says, “We’ll rely more on human judgment. Work on neural networks will continue, despite problems with speed and data sparseness.”

Read full article >>

Thank you to site moderators, class of 2016-2017

Source: Translator T.O.
Story flagged by: Jared Tabor

The moderator class of 2016-2017 is coming to an end, but before this happens, would like to thank all of those members who have given of their time to help maintain a positive, results-oriented atmosphere on the site. Each person in the class has made valuable contributions to, and some of them even beyond the moderator program. moderators are volunteer members who have benefited from and have chosen to give something back by playing their part, in turn, in a system put in place to ensure fair play. Their role is to foster and protect the positive, results-oriented atmosphere that makes possible, by:

  • Greeting and guiding new participants, and helping them to properly use and benefit from what is available to them at
  • Enforcing site rules in a consistent and structured manner to maintain a constructive environment.

The moderator class of 2016-2017 is certainly a very good example of the role. Thank you mods!

Now, the moderator class of 2017-2018 is scheduled to begin in September. So, if you are a member and would like to volunteer for a one-year term as site moderator, please visit or contact site staff through the support center.

Issue two of “Connections” is now out

By: Jared Tabor

The second issue of Connections came out a few days ago. You can read it by clicking on the image or link below:

Connections, Issue 2 >>

If you missed it, the first issue can be seen here:

Connections, Issue 1 >>

The Rise of Interpretainment [Podcast]

Source: Moravia
Story flagged by: Jared Tabor

On this week’s episode of Globally Speaking, Renato Beninatto speaks with Maria Paula Carvalho, a conference interpreter and translator, on a new concept called “interpretainment.”

With interpretainment, the interpreter tries to mimic the speaker’s tone and gestures, in addition to translating the content. Topics include:

  • The difference between consecutive and simultaneous interpretation
  • The definition of interpretainment
  • Why interpretainers must surrender to the speaker’s emotions—laugh, cry, shout, dance, whatever is needed to achieve the intended impact
  • How common is interpretainment in the language industry today

Listen here >>

18 open source translation tools

Story flagged by: Jared Tabor

Computer-assisted translation (CAT) tools


OmegaT CAT tool

OmegaT CAT tool. Here you see the translation memory (Fuzzy Matches) and terminology recall (Glossary) features at work. OmegaT is licensed under the GNU Public License version 3+.

CAT tools are a staple of the language services industry. As the name implies, CAT tools help translators perform the tasks of translation, bilingual review, and monolingual review as quickly as possible and with the highest possible consistency through reuse of translated content (also known as translation memory). Translation memory and terminology recall are two central features of CAT tools. They enable a translator to reuse previously translated content from old projects in new projects. This allows them to translate a high volume of words in a shorter amount of time while maintaining a high level of quality through terminology and style consistency. This is especially handy for localization, as text in a lot of software and web UIs is often the same across platforms and applications. CAT tools are standalone pieces of software though, requiring translators that use them to work locally and merge to a central repository.

Tools to check out:

Machine translation (MT) engines


MT engines automate the transfer of text from one language to another. MT is broken up into three primary methodologies: rules-based, statistical, and neural (which is the new player). The most widespread MT methodology is statistical, which (in very brief terms) draws conclusions about the interconnectedness of a pair of languages by running statistical analyses over annotated bilingual corpus data using n-gram models. When a new source language phrase is introduced to the engine for translation, it looks within its analyzed corpus data to find statistically relevant equivalents, which it produces in the target language. MT can be useful as a productivity aid to translators, changing their primary task from translating a source text to a target text to post-editing the MT engine’s target language output. I don’t recommend using raw MT output in localizations, but if your community is trained in the art of post-editing, MT can be a useful tool to help them make large volumes of contributions.

Tools to check out:

Translation management systems (TMS)


Mozilla's Pontoon translation management system user interface

Mozilla’s Pontoon translation management system user interface. With WYSIWYG editing, you can translate content in context and simultaneously perform translation and quality assurance. Pontoon is licensed under the BSD 3-clause New or Revised License.

TMS tools are web-based platforms that allow you to manage a localization project and enable translators and reviewers to do what they do best. Most TMS tools aim to automate many manual parts of the localization process by including version control system (VCS) integrations, cloud services integrations, project reporting, as well as the standard translation memory and terminology recall features. These tools are most amenable to community localization or translation projects, as they allow large groups of translators and reviewers to contribute to a project. Some also use a WYSIWYG editor to give translators context for their translations. This added context improves translation accuracy and cuts down on the amount of time a translator has to wait between doing the translation and reviewing the translation within the user interface.

Tools to check out

Terminology management tools


Brigham Young University's BaseTerm tool

Brigham Young University’s BaseTerm tool displays the new-term entry dialogue window. BaseTerm is licensed under the Eclipse Public License.

Terminology management tools give you a GUI to create terminology resources (known as termbases) to add context and ensure translation consistency. These resources are consumed by CAT tools and TMS platforms to aid translators in the process of translation. For languages in which a term could be either a noun or a verb based on the context, terminology management tools allows you to add metadata for a term that labels its gender, part of speech, monolingual definition, as well as context clues. Terminology management is often an underserved, but no less important, part of the localization process. In both the open source and proprietary ecosystems, there are only a small handful of options available.

Tools to check out

Localization automation tools


Ratel and Rainbow components of the Okapi Framework

The Ratel and Rainbow components of the Okapi Framework. Photo courtesy of the Okapi Framework. The Okapi Framework is licensed under the Apache License version 2.0.

Localization automation tools facilitate the way you process localization data. This can include text extraction, file format conversion, tokenization, VCS synchronization, term extraction, pre-translation, and various quality checks over common localization standard file formats. In some tool suites, like the Okapi Framework, you can create automation pipelines for performing various localization tasks. This can be very useful for a variety of situations, but their main utility is in the time they save by automating many tasks. They can also move you closer to a more continuous localization process.

Tools to check out

See full article >>

Startup seeks to translate your voice

Source: TrendinTech
Story flagged by: Jared Tabor

BabelOn, a startup in the San Fransisco area, is developing software that can transform your speech from English to any other language, without using any additional translation services, and it will sound like you own voice. While using artificial means to create the sounds of a human voice, a technique called speech synthesis, has been around for a while, BabelOn is offering a very specific and unique spin on the technology. Using a specialized combination of custom designed hardware and software, BabelOn will analyze your voice for its unique characteristics then use those results to recreate language that sounds like the words are coming out of your own mouth in any language you want.

Originally the idea was conceived for use in film dubbing or translating video games but the ultimate goal for BabelOn is to provide real-time language translation in your voice, like when you’re on a Skype call or similar circumstances. Although Microsoft currently offers a comparable service for some time, their voice is digital sounding, like Siri, making the BabelOn difference more personal.

While this is certainly a very interesting concept, it is still very early on in BabelOn’s development. There has yet to be a software demonstration, nor have they done any work for clients. Currently, BabelOn is bidding for a soon to be released video game translation but it is not a done deal. The software has a potential for success but also presents a glaring security concern in the concept of having one’s voice “stolen”.


“Gender and Family in the Language Services Industry” research from Common Sense Advisory

Source: PR Newswire
Story flagged by: Jared Tabor

Issues related to gender, the workplace, and family are important social and political concerns. “Gender and Family in the Language Services Industry” is the first in a series of reports by independent market research firm Common Sense Advisory (CSA Research) dealing with gender and family issues among those who are employed in the language industry – translation, localization, interpreting, and related tasks – or who work with language services.

What matters most to you in your career?

What matters most to you in your career?

Earnings Reports
Economic studies show that jobs considered “women’s work” typically show lower pay than those associated with men, and that wages fall as more women enter a field. By contrast, both genders earn above-average wages in language services: US$50,900 in North America and $34,800 in Europe (versus economy-wide averages of US$49,630 and roughly US$20,000, respectively).

“These results are generally positive. Despite significant downward price pressures, language services professionals’ earnings are in line with – or exceed – those of other skilled professionals, without the penalty often associated with ‘women’s work,’” comments Arle Lommel, a senior analyst at CSA Research.

Key Findings and Dataset
Based on 2,200 global responses, the CSA Research report shines light on topics ranging from pay to personality to promotions. Key findings from the report, which is available for free with registration, include:

  • The world-wide language services gender pay gap is 19%. Adjusting for employment status, men still make 14% more than women. Earning disparities are highest in top and middle management and executive positions.
  • The gender gap is lowest in North America, but higher in Europe. However, Europeans believe they are closer to pay equality than others and Americans believe their employers are less equitable than others.
  • Majorities believe that gender issues do not affect them personally. Women are more likely to believe they have been personally affected, but both men and women, tend to see gender issues as important problems that affect other people.
  • Both men and women see women as having more positive qualities as employees. Respondents of both genders tend to agree that women have more positive qualities – including those important for leadership roles – than men do. Nevertheless, these qualities do not convert into advancement opportunities.

See more >>

These are the nominees in the 2017 edition of the community choice awards – vote now

By: Jared Tabor

Voting is now open in the 2017 edition of the community choice awards. Be sure to check out the people and resources that have been nominated, cast your votes, and spread the word.

Nominees in the translation category:

Blog: Best overall blog related to translation, based on activity and content from January 2016 to date.

Website: Best overall professional translator’s website.

Twitter: Best overall “Twitter”, based on activity from January 2016 to date.

Facebook page/group: Best overall Facebook page or group, based on activity and content from January 2016 to date.

Podcast: Best podcast (series or single podcast) from January 2016 to date.

Trainer: Active trainer in in-person or online training.

Conference speaker: Based on lectures/sessions given at events from January 2016 to date.

Best presentation: Best in-person or video presentation related to translation, based on activity and content from January 2016 to date.

Translation mentor: Based on quality and quantity of mentoring.

Article: Best article published (online or in print form) from January 2016 to date.

Book: Best book published (print or digital format) from January 2016 to date. May include re-releases or new editions.

Blog post: For a single blog post, as opposed to the “blog” category, which is based on a blog as a whole. This category may include guest blog posts. profile: Most professional/attractive profile.

Most helpful contributor: All-around contributions, be they in forums, in term help, on social media, etc.

YouTube channel: Active YouTube channel dedicated to freelancing and translation.

Platform/resource: A platform or other resource designed to help freelance translators.

Nominees in the interpreting category:

Blog: Best overall blog related to interpreting, based on activity and content from January 2016 to date.

Website: Best overall professional translator’s website.

Twitter: Best overall “Twitter”, based on activity from January 2016 to date.

Facebook page/group: Best overall Facebook page or group, based on activity and content from January 2016 to date.

Podcast: Best podcast (series or single podcast) from January 2016 to date.

Article: Best article published (online or in print form) from January 2016 to date.

Voting will be open through the end of July. Cast your votes here >>

Results of Slator’s reader poll on subcontracting, payment methods, adaptive MT

Source: Slator
Story flagged by: Jared Tabor

In June 2017, a German federal court published a ruling in a tax case that delved into the details of where exactly freelancing ends (German: freiberufliche Tätigkeit) and a commercial enterprise begins (German: gewerbliche Tätigkeit).

As Slator reported, the court ruled that freelance translators are not allowed to offer languages they don’t personally understand and continue to enjoy the tax breaks and other administrative benefits that come from operating in a freelance capacity.

On Twitter and LinkedIn, the Slator article triggered an interesting debate about outsourcing to fellow freelancers and Slator’s take of the ruling (we stand by it). Beyond the ruling in Germany, subcontracting by freelancers has been a hot-button issue for decades, and so we wanted to know how our readers perceive the practice.

A clear majority of the 118 respondents who participated in the poll conducted among Slator’s e-mail newsletter subscribers view subcontracting unfavorably — 22% of respondents say it depends and only 10% approve of subcontracting.

If there is an industry that could benefit from more efficient payment technology, it’s the language services industry. Global in nature, hundreds of thousands of freelancers are based in every possible country under the sun, and millions upon millions of relatively small payments are made every single month.

But despite a boom in fintech, two decades of PayPal, and cryptocurrencies like Bitcoin becoming more mainstream, bank transfers, which often involve expensive fees, remain the top choice of paying freelancers among the respondents to our poll. Expect this to change in the coming decade.


Translation news roundup for June

By: Jared Tabor

Here are some of the highlights in Translation News for the month of June 2017:

Translation / Interpreting




Amazon is planning to rival Google with a service that translates languages

Source: CNBC
Story flagged by: Jared Tabor

Amazon’s cloud computing division has been working on a translation service that developers could use to make their websites and apps available in multiple languages, CNBC has learned.

Amazon already has machine-translation technology that it uses across the company to do things like provide product information in multiple languages. Now, the company is preparing to make it available through Amazon Web Services, said a source familiar with the matter. Amazon could announce the service before its annual re:Invent conference in Las Vegas in November.

The imminent launch comes almost two years after Amazon acquired a translation start-up called Safaba. A co-founder of Safaba, Alon Lavie, leads Amazon’s machine translation research and development group in Pittsburgh.

The other major cloud infrastructure providers, Alphabet and Microsoft, sell translation services. Google first released a language translation service for developers in 2008Facebook has developed machine translation technology for use in its own main app.

AWS in recent months has released services that draw on artificial intelligence in various ways — there are tools for recognizing objects in images and for turning text into speech, for example. A translation service would fit in well with that strategic push, which could help AWS further diversify its revenue away from the raw computing and storage resources that other companies provide.

The field of machine translation has been exploding with activity in the past two years as researchers have found gains by adopting deep learning. Deep learning is a type of AI that involves training software systems, called neural networks, on lots of data — like text snippets labeled with translations — and then getting the neural networks to make predictions about new data.

When Google Translate switched from a phrase-based system to a neural machine translation system for Japanese a few months ago, people immediately picked up on the higher quality of translations. Google has also added the neural system to its service for developers.

AWS, which generated $3.66 billion in revenue in the first quarter, offers more than 70 services, including applications for email and video conferencing. Companies that already extensively depend on AWS might well be willing to experiment with a new application programming interface for translation. Amazon also operates the Mechanical Turk web service that companies can use to farm out small tasks such as translating sentences to a number of people.

Independent companies like Gengo, Smartling and Unbabel rely on AWS infrastructure to operate human-powered translation services for companies.

ModernMT: new open source machine translation engine

Source: Slator
Story flagged by: Jared Tabor

Four major institutions in the European Union have developed a new machine translation (MT) engine that will be released worldwide. The source code was also made available to the developer community on GitHub.

The open source project, called ModernMT, received a EUR 3m grant from Horizon 2020, the EU’s framework program for research and innovation, and was released in beta in the fourth quarter of 2016.

Built as a ready-to-install application, the software seeks to address the remaining hurdles that still hinder the adoption of MT technology by end-users, notably language service providers (LSPs) and enterprises.

Currently, it is available as a plug-in for various computer-assisted translation (CAT) tools, including SDL Trados Studio. The full version of the enterprise-grade software is set for release in the fourth quarter of 2017.

See more >>

China clamps down on poor translations

Source: RT
Story flagged by: Jared Tabor
Inventively mangled foreign-language versions of signs and menus have become an iconic feature of China, but the government is imposing a compulsory list of 3,500 common translated phrases for public use in a bid to rid the country of Chinglish.

Starting from December, the Standardisation Administration, Ministry of Education, and General Administration of Quality Supervision, Inspection and Quarantine will issue a new guide, while encouraging sign-makers to “prioritize correct grammar” and avoid misleading direct translations.

Particular focus will be on translations that are offensive, discriminatory, or unpatriotic.

© Phillip’s Adventures / YouTube

Wrong translations “damage the country’s image,” while better use of foreign languages in public spaces will pave the way for the “development of a multilingual society,” officials explained in an article published in the state-owned People’s Daily.

The rapid opening up and economic development of a country, where most do not read foreign alphabets or speak other languages, in the past several decades has produced a demand for foreign-language texts that is simply not matched by the requisite expertise.

German court bars translators from outsourcing if they run a freelance business

Source: Slator
Story flagged by: Jared Tabor

In a February 2017 ruling published on June 7, 2017, Germany’s Federal Fiscal Court struck down an appeal lodged by two freelance translators that they not be charged back taxes equivalent to the fees paid by commercial enterprises.

For more than two decades, the two freelancers had formed a so-called “civil law partnership,” a special legal form of incorporation under German law that allows two or more freelancers to pool resources. Such partnerships enjoy a number of advantages such as lower taxes or simplified accounting compared to other commercial enterprises such as an AG or GmbH (Ltd. or plc).

To qualify for such a status, the law specifies that the partners must provide the services entirely by themselves, which has made the legal form popular among freelancers.

The tax authorities, however, conducted an audit in 2008/09 and concluded that the partners’ business sold services provided by third-parties to a “significant degree.” As a result, the tax authorities ruled the partnership owed the government back taxes.

The two freelancers appealed the ruling all the way to the Federal Fiscal Court, which is the Court of last resort within the German jurisdiction over tax and customs matters.

The two plaintiffs’ business focuses on technical translations for mechanical engineering, translating mostly manuals, and technical documentation. Both are qualified translators. In addition, one of the partners holds an engineering degree.

Originally, the two limited their work to German, English, Spanish, and French – languages they were able to work on themselves. Over time, however, customers requested additional languages such as Turkish, Swedish, Dutch, Russian, and many others.

Therefore, the pair decided to farm out translations to other freelancers. By doing this, they exceeded the scope of business they were entitled to under the legal form of a partnership, the court now ruled.

The plaintiffs argued that they provide many other important services in a freelance capacity, such as consulting, technical editing, and layouting. They also explained that they maintain a Translation Memory and that their careful selection of third-party freelancers was essential to the overall offering.

The court would have none of it, however, explaining in elaborate German legalese that “shortcomings in a freelancer’s knowledge of a particular language cannot be offset by deploying a Translation Memory system, nor by carefully selecting third-party freelance translators, since the freelancer cannot personally ensure the accuracy of the translation.”

In short, freelance translators are not allowed to offer languages they don’t personally understand and continue to enjoy the tax breaks and other benefits that come from operating as a freelancer.

In Germany’s fragmented language services market, where individual freelance translators and boutique agencies form the backbone of the vendor ecosystem, the ruling’s impact will likely reverberate beyond this single case.

AUSIT national mini-conference (17-18 November, Canberra, Australia) – Call for papers

By: Aurelie Sheehan





Submission deadline: 31 July 2017

Bridging the gaps between languages and cultures as we do, it can sometimes be difficult for those of us in the interpreting and translation industry to balance expectations.  Particularly when working with sensitive information or in tricky situations, our errors in judgement can have far-reaching consequences.  In these circumstances, our professional ethics can help us make informed judgments to navigate tricky situations and guide us through ethical dilemmas.

Celebrating the 30th anniversary of AUSIT’s ongoing commitment to raising professional standards and awareness of the translation and interpreting industry, this year’s mini-conference serves as the best opportunity to reflect on our professional and ethical values, converge our thinking and discuss.

The Organising Committee is now inviting translation and interpreting scholars as well as practising translators and interpreters to submit proposals for papers addressing the conference theme, Translation and Interpreting: Ethics and Professionalism.  Presentations on all related aspects are welcome including, but not limited to, practice, theory, research and pedagogy.


Proposals for individual papers should be submitted as abstracts of 250 words via the submission page ( by 31 July 2017.


Papers will be allocated 20 minutes for presentation plus 10 minutes for discussion.


31 July 2017: Submission deadline

1 – 31 August 2017: Committee appraises abstracts and notifies presenters of acceptance

22 September 2017: Registration deadline for presenters.  Presenters need to register for the Mini-

conference on or before this date.

17 – 18 November 2017: Mini-conference, NAGM & Jill Blewett Memorial Lecture


Please ensure that you meet all or most of the following appraisal criteria.

• You clearly state the purpose of the presentation.

• You focus the content of your presentation, pacing it so that it fits into your allocated time slot (timekeepers will stop presentations at the advertised times).

• You contribute a presentation of good quality.

• You clearly reflect the conference theme in your presentation


- You define the method/approach, data and results (if applicable) in clear terms.

- You note the implications/relevance of the findings.


- You clearly identify the issues discussed as issues arising from particular professional situations.

- You clearly identify the implications/relevance.

For any enquiries, please contact the Organising Committee via natminiconf(at)

Parlez-vous franglais? More English words officially enter French language

Source: The Local
Story flagged by: Jared Tabor

The 2018 editions of France’s two most popular dictionaries reveal there are several new entries that won’t be so foreign to English speakers.

France’s two main rival dictionaries Robert and Larousse are set to release their 2018 editions and have leaked a few of the new words that made the cut.

Le Robert has added 200 words while Le Petit Larousse will see an additional 150 words on its pages.

To qualify for the honour, the new entries had to be in popular use, be used frequently by the media and not at risk of falling out of use in the short term.

The influence of technology is clearly visible in this year’s selection of anglicized French words getting the seal of approval.

With the words “spoiler” (usually a crucial bit of information that gives away the plot to a tv programme or film), “googliser” (to google), “liker” (to “like” something on social media) and “retweeter” (to retweet something on Twitter) joining the French language, younger generations of French and English speakers will have few problems understanding each other, at least when talking about the internet and social media.

Similarly, words inspired by English, such as hacktivisme (using technology to promote a political agenda) and uberisation (using web platforms that directly connect customers with the person providing the service to ensure lower costs than the traditional model), which have become common in France, have been included in the dictionaries.

Miley Cyrus, Jimmy Fallon perform Google Translated songs — ‘Yeah I like that cadaver’

By: MacLeod Cushing

From New York Daily News June 15, 2017:

Miley Cyrus does not like the pressures stones make.

What, you ask. Well, please see Google Translate.

Cyrus, appearing on “The Tonight Show Starring Jimmy Fallon,” took part in the host’s segment “Google Translate Songs,” putting strange lyrics to hits like Ed Sheeran’s “Shape of You” and Rick James’ “Super Freak.” The lyrics were placed into Google Translate and then shuffled back into English.

Cyrus kicked things off with the strange rendition of “Shape of You,” which is surely a version Hannibal Lecter could relate to.

One line: “Latch to my torso and throw me a cadaver.”

She continues, “I stand on your body’s curves/We do not like pressures stones make/But my organ drops right out/Yeah I like that cadaver.”

Next up: Fallon, with his version of “Super Freak,” which became “Really Weird.”

He sings, “This girl has an exemplary/Until toenails come from the top.”

“This girl has become mad/Literacy for the girl/Newspaper’s new thoughts/She is OK, she is OK/The girl has become OK for me/She’s really weird, she’s really weird, yes.”

Cyrus then continued the fun with a translated version of Dusty Springfield’s “Son of a Preacher Man.” While the lyrics were drastically altered, Cyrus still carried the performance with that “exemplary” voice of hers.

“Can you tell me where I left my house/Only one person contacted me/A minister’s male child/Only one male child ever informed me/A minister’s male child…” Cyrus sings.

The pair finished things off with a duet of Marvin Gaye and Tammi Terrell’s “Ain’t No Mountain High Enough.” The new title: “Land Forms Do Not Prefer to Get High.”

“I want aided limbs/I arrive two times/Very rapidly,” Cyrus sings.

The two then reach the chorus singing, “Landforms don’t prefer to get high/Depressions don’t prefer to fall over/I won’t overweight your harbor/I’m going to purchase your baby.”

View the segment here on YouTube >>

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