Savvy Diversification Series – Diversification into Machine Translation

The Savvy Newcomer team has been taking stock of the past year and finding that one key priority for many freelance translators and interpreters has been diversification. Offering multiple services in different sectors or to different clients can help steady us when storms come. Diversification can help us hedge against hard times. With this in mind, we’ve invited a series of guest authors to write about the diversified service offerings that have helped their businesses to thrive, in the hopes of inspiring you to branch out into the new service offerings that may be right for you!

Taking the pulse of the U.S. localization industry demonstrates what should be an economically prosperous period for qualified translators and editors. It’s true that it doesn’t sound great for the industry to be operating in what the Joint National Committee for Languages calls a period of “language crisis” in the United States. The materials distributed to U.S. lawmakers during the February 2021 Virtual Language Advocacy Days give alarming statistics: “9 out of 10 US employers rely on employees with world language skills[, and] 1 in 3 foreign language-dependent employers reports a language skills gap[ and] 1 in 4… lost business due to a lack of foreign language skills” (JNCL-NCLIS, Legislative Priorities). That is to say, at the same time that the U.S. market is feeling repercussions for its lacking investments in multilingual education over the years, qualified language professionals are in high demand, while the roles being demanded by the market are becoming ever more technological in nature. In article “Future Tense: Thriving Amid the Growing Tensions between Language Professionals and Intelligent Systems,” Jay Marciano points out, “The day-to-day work of the translator of today will be hardly recognizable to a language services professional in 2030.”

Newcomers to the industry are at a particular advantage within these circumstances. During Slator’s Briefing for their Pro Guide: Translation Pricing and Procurement, Anna Wyndham noted that experienced buyers of localization services are less likely to adopt new pricing models, while new buyers from the tech industry and beyond are more open to and indeed may expect “human-in-the-loop” pricing models based on full integration with machine translation. Likewise, savvy newcomers to the translation profession are more likely to adopt machine translation as a reality of the role, while more veteran translators may feel less incentivized to go through the disruptive change of integrating Machine Translation (MT) technology into their everyday workflows. Newcomers and veterans alike who are looking to diversify now and have their services remain relevant for decades to come would do well to incorporate machine translation before the learning curve has become so great as to effectively disqualify one from key markets.

This article outlines key MT-related services to include in your portfolio as 21st-century translators reinventing themselves as language technologists. As language technologists, your expertise in translation makes you an asset at MT-engine training, writing content for MT, and post-editing of machine translation (PEMT) stages. This article considers these services in reverse order, starting with the PEMT services that translators are most likely to perform, before shifting further and further upstream, first to writing for MT and then to training MT engines. The discussion of each service type addresses common misconceptions and key competencies so you can start developing the skills needed to add MT services to your field of expertise. Check out the additional resources section for further reading to continue your exploration of this dynamic service area.

Service #1 – Post Editing of Machine Translation (PEMT)

In Episode 49 of The ATA Podcast, “A Look into the Future of Post-Editing and Machine Translation,” Jay Marciano defines post-editing of machine translation as a “step that a professional translator takes to review and make corrections to machine translation output in the provisioning of… high quality translation[s]” (Baird and Marciano). By rights, Marciano believes that this terminology “post editor” adds specialized meaning to what is already a post editing role. To summarize, traditional translation denotes not only the invention of completely new copy, understood to be the translation of “new words,” but also the act of editing translation memory (TM) output at the segment level, the level of work involved depending on the quality of the contributors to shared, proprietary resources, and the level of match of the source segment for translation to existing segments within the TM, generally starting from 75% percent matches to above. Incorporating segments that have been pre-translated using MT adds another segment type for human post-editing, though the term “post-editing” itself is used exclusively to denote work reviewing machine translation output.

The belief that it takes less skill to post-edit machine translation than it does to produce traditional human translation is a misconception that has circulated in the translation field since the advent of MT. This misconception is tied to several factors. Among those is the outdated perception that MT produces poor quality output that is too repetitive to be interesting for humans to review. Older rules-based or statistical models indeed perform better for content that corresponds to lower levels of the Interagency Language Roundtable (ILR) scale for translation performance. The ILR scale is comprised of 5 levels, with level 2 and below indicating limited or minimal performance, and level 3 and above indicating levels of professional performance. Traditionally, rules-based and statistical models have been best geared for texts that correspond to level 2 of the ILR scale, or straightforward texts like sets of instructions produced using controlled language that leaves little room for creative interpretation. ATA certification is a mid-career certification that demonstrates that a translator performs at (at least) a Level 3 of the ILR scale, and older MT models could not at all compete with professional humans for content characterized by the abstract language, implication, and nuance that requires a human mind to be parsed. However, machine translation technology has evolved at light speed, and even if MT cannot surpass the quality produced by human translators, the levels of fluency and correspondence it is possible to achieve using artificial intelligence and neural machine translation is remarkable. The linguistic challenges encountered in this work are interesting for those who enjoy studying the intersection of human and machine-produced languages too.

No matter the complexity of the content that a machine translation engine is designed to pre-translate, MT engines are far from replacing humans. According to the ATA Position Paper on Machine Translation, this is because “Computers can be very sophisticated in calculating the likelihood of a certain translation, but they understand neither the source nor the target text, and language has not yet been captured by a set of calculations.” While the results of MT are getting better all the time, when confirmation of any degree of accuracy or polishing is needed, a professional post editor is the one to do that job. According to ISO 17100 Translation Services – Requirements for translation services of the International Organization for Standardization (ISO), the professional competences of translators are: translation, linguistic and textual competence in the source and target language, research, information acquisition and processing, and cultural, technical, and domain competences (3.1.3). Professionalism is a competence added to the translator competences indicated in ISO 17100 for MT post editors according to ISO 18587 – Translation services – Post-editing of machine translation output – Requirements. That professionalism entails a knowledge of MT technology, common linguistic errors produced by MT, and Computer-Assisted Translation (CAT) tools, and the ability to carry out linguistic analysis, provide structured feedback to improve MT output over time, and interact with terminology management systems (“5 Competences and qualifications of post-editors” ISO 18587).

To undertake the linguistic challenges that post editing of machine translation presents requires a thorough understanding of key post-editing concepts and how those concepts relate to post-editing specifications. To review, specifications outline the requirements of buyers and expectations of target users that change how localization services are produced. With regards to machine translation, the value proposition of the content being produced will determine whether light post-editing or full-post editing is needed, that is, whether what the TAUS MT Post-Editing Guidelines refers to as “good enough” or “human translation” quality is needed. If light-post editing is called for, such as in circumstances in which speed of delivery takes priority over fluency and stylistics, the post editor will intervene minimally in the raw MT output to make corrections to inaccurately rendered meaning, grammar and spelling errors, and culturally offensive content. If full-post editing is called for, greater checks for consistency in terminology, product names, and mechanical aspects of the text are also employed.

Within either light or full post-editing models, discipline is key, and in post-editing, discipline is demonstrated by using the least number of keystrokes to make only the necessary corrections. Experienced post-editors can quickly distinguish among segments that are good enough, segments that require minor edits, and segments that need to be started from scratch.  Localization managers use post-editing distance – or the measure of the change between raw MT output and post-edited content – to gauge the overall quality of the MT engine and the post editor’s work and to identify instances of over-editing and under-editing. According to Silvio Picinini of eBay, low edit distances can be an indicator of both quality and productivity, since if both the MT engine and the post editor have been well trained, that should result in lower edit distances. For those who are interested in working as post editors or in training post editors, Sharon O’Brien recommends the following curriculum in the 2002 paper “Teaching Post-editing: a proposal for course content”; “Introduction to Post-editing, Introduction to Machine Translation Technology, Introduction to Controlled Language Authoring, Advanced Terminology Management, Advanced Text Linguistics, [and]Basic Programming Skills” (103).

Service #2 – Writing for Machine Translation

In a world in which more-and-more data is being authored on a daily basis than could ever possibly be translated by humans, the authors of a great percentage of that data may not be good writers at all, much less good writers of content intended for translation. Within workflows that incorporate MT, professional linguists have an opportunity to get involved before any content is even imported in the engines that produce the raw output for PEMT. Just like workflows built around human translation benefit if the source content is written for translation, workflows that incorporate machine translation benefit from increased efficiency and quality if the source content is written expressly for that purpose. Localization workflows for human translation already incorporate copy-editing of source content to promote smooth processing during translation, especially where multiple target languages are involved. This copy-editing stage decreases the need for clarification mid-workflow and prevents the extensive rework that results from misunderstandings and poor comprehensibility by identifying and correcting ambiguities and inconsistencies in source content prior to sending that content for translation.

Once post editors have a good sense for the errors that are common to a language pair, subject field, and text type, they will be more equipped to customize recommendations for how to best write for machine translation, and for certain text types and subject fields, the professional recommendation may just be that MT will not suffice. Ambiguities and inconsistencies that should be flagged prior to both human and machine translation include unclear referents, the use of synonyms, long compound nouns, and the misinterpretation of homonyms, among many other textual features. Examples of some common sources of translation errors are provided below.

  • Unclear referent: Group A and group B compared their results, and they [Group A, Group B, or Group A & B?] decided to make changes based on finding C.
  • Potential synonym use: The drying process should take so many days. Once the dehydration process is complete, do this next. [Are drying and dehydration separate processes, or do both refer to the same process?]
  • Misinterpretation of homonyms: Our earnings for this quarter are as follows. [Depending on the context, the best equivalent for “earnings” may be an equivalent that conveys one of these senses: pay, profits, returns, income, etc.]

When getting started with writing for MT, the principles from controlled language and plain language have good general rules that can be applied too. Uwe Muegge’s Controlled Language Optimized for Uniform Translation, for instance, includes such guidelines as expressing only one idea per sentence, using simple yet complete grammatical structures, limiting the use of pronouns by restating nouns instead, and using articles so that nouns can be easily identified; and Plain Language Association International recommends that jargon be avoided and that simple words be employed (“What is plain language?”). The rules for controlled language and plain language may imply that these forms of communication are easy to use, but even identifying the myriad of textual features encompassed by these principles takes a great deal of study, practice, and experience. The Simplified Technical English, a controlled language of the AeroSpace and Defense Industries Association of Europe, for instance, consists of sixty-five writing rules in nine different categories and a dictionary of nearly 1000 approved words.

Service #3 – Training Customized MT Engines

The invention of machine translation has largely remained in the realm of programmers and engineers. Despite the noticeable lack of linguists involved in MT development, so much high-quality data is needed to train customized MT engines that getting corpus linguists involved before undertaking what can be expensive, manual data collection processes makes perfect sense. A corpus is a collection of texts that have been selected for a specific purpose. A general language corpus will include many millions of words, while a corpus of specialized texts written by experts from a specific subject field may include only hundreds of thousands of words to start. Parallel corpora of translated and aligned segments are most frequently sought when training MT engines, whether rules-based, statistical, or neural models. However, high-quality parallel corpora take a long time to build and are exceedingly hard to find in any off-the-shelf format. Because high quality parallel corpora are so hard to find, those training MT engines may turn to comparable corpora, or collections of similar texts in multiple languages, for languages with less resources.

When building monolingual corpora, linguists will be able to identify the characteristics of the most representative data to collect for each corpus upon which the MT engine will be trained. Corpora might include one technical corpus of general content written by subject matter experts in a specific subject field per language and one client-specific corpus of proprietary product documentation per language. Since MT is trained using human produced language, it therefore replicates human biases. Linguists can help identify and mitigate the race and gender biases that manifest in large data sets by identifying specific populations, geographical regions, or language dialects not adequately represented in a corpus. They can help by eliminating any content from the corpus that is not fit for use too. Thus, MT users will not be made to feel insulted by offensive language produced by an MT engine and MT developers can avoid alienating MT users. Salvador Ordorica gives several examples of high-profile manifestations of racial and gender bias in MT and how to overcome it in the article “Avoiding Bias and Discrimination in Machine Translation” published via Forbes.

Most would-be localizers need to look no further than the translation memories under their command to start getting practice managing parallel corpora. Translation memories that contain high-quality content are highly sought-after while being hard to find, and this makes quality TMs exceedingly valuable. When a single person is contributing to a TM, each segment should be tagged with anonymized client and project identifiers so that individual clients’ data can be later isolated as necessary, in keeping with any confidentiality agreements that govern the use of content produced. Linguistic patterns will emerge from overall TMs used to train MT engines if multiple clients’ content is mixed together, so producing distinguishable copy from that content is a challenge that needs to be taken into consideration as well. Linguists can help with the style and terminology guides that make producing distinguishable copy from MT possible. If multiple people are contributing to a TM, keep the number of people contained and their identifiers clearly documented with proper protections over copyrighted assets that include the ability to rate the contents according to the quality of the producer of the source and target segment and revoke access rights, as necessary. Again, take these precautions because high quality TMs make the training of MT engines much more efficient, and these TMs therefore fetch a very high price.

Pricing MT Services According to Skill

In summary, to diversify into the MT services that are already a nearly ubiquitous part of the provisioning of human translation services, translators should develop advanced skills in CAT tools, technology in general, and linguistic post editing, the ability to match services rendered with the quality expectations conveyed in specifications, and knowledge of controlled languages, corpus building and analysis, TM management at scale, terminology management, and data security. Regardless of the wide range of competencies necessary to work in MT, be aware that traditional buyers accustomed to per-word pricing models tend to see the incorporation of MT as an opportunity to purchase translation services at further discounts to TM-pricing models. As Slator emphasizes in the Pro Guide: Translation Pricing and Procurement, new buyers mean that new pricing models are possible. When working with new buyers, shift to value-based pricing models that more adequately compensate you for your rich expertise where you can. Above all, remember that in the design, implementation, and review of MT, teaching the parrot to talk is among the goals, but it is much more valuable if you can teach the parrot to say the correct thing.

Works Consulted & Recommended Resources for Further Reading

Aslan, Şölen. “9 Types of Data Bias in Machine Learning.” TAUS, 2021 Mar 22, Accessed 2021 Apr 12.

“ATA Position Paper on Machine Translation: A Clear Approach to a Complex Topic.” American Translators Association, 2018 Aug. 13, Accessed 2021 Apr 1.

Baird, Matt and Jay Marciano. “E49: A Look into the Future of Post-Editing and Machine Translation.” The ATA Podcast, Episode 49, 2020 Sept 24,

Berger, Carola F. “An Introduction to Neural Machine Translation.” American Translators Association, ATA 59th Annual Conference, October 2018, Accessed, 2021 Apr 10.

“ILR Skill Level Descriptions for Translation Performance.” Interagency Language Roundtable, (Links to an external site.). Accessed 2021 Mar. 30.

ISO 17100:2015(E), Translation Services – Requirements for translation services, International Organization for Standardization, Geneva, Switzerland, 2015,

ISO 18587:2017, Translation Services – Post-editing of machine translation output – Requirements, International Organization for Standardization, Geneva, Switzerland, 2017,

Legislative Priorities of the Language Enterprise-177th Congress. Joint National Committee for Languages and the National Council for Languages and International Studies (JNCL-NCLIS), 2021 Feb, handout.

Marciano, Jay. “Future Tense: Thriving Amid the Growing Tension between Language Professionals and Intelligent Systems.” The Chronicle, American Translators Association, July/August 2020, 29-32, Accessed 2021 Apr 12.

Massardo, Isabella, et al. MT Post-Editing Guidelines. TAUS, 2016,

Muegge, Uwe. Controlled Language Optimized for Uniform Translation (CLOUT). Bepress, 2002,

O’Brien, Sharon. “Teaching Post-editing: A Proposal for Course Content.” European Association for Machine Translation, 2002.

Ordorica, Salvador. “Avoiding Bias and Discrimination in Machine Translation.” Forbes, 2021 Mar 1, Accessed 2021 Apr 12.

Picinini, Silvio. “Going the Distance – Edit Distance 1.” eBay blog, eBay Inc., 2019 Aug 8, Accessed 2021 Mar 31. See also “Going the Distance – Edit Distance 2 & 3.”

Pro Guide Briefing: Pricing and Procurement. Slator, 2021 Apr 7, Webinar.

Pro Guide: Translation Pricing and Procurement. Slator, 2021 Mar 19, Accessed 2021 Apr 12.

Simplified Technical English Specification ASD-STE100. AeroSpace and Defence Industries Association of Europe, Issue 7, 2017. PDF.

“What is plain language?” Plain Language Association International (PLAIN), 2021, Accessed 2021 Apr 12.

Zetzsche, Jost, Lynne Bowker, Sharon O’Brien, and Vassilina Nikoulina. “Women and Machine Translation.” The ATA Chronicle, American Translators Association, Nov/Dec 2020, Volume XLIX, Number 6. Print. Also available via:

Author bio

Alaina Brandt is a Spanish>English translator with an MA in Language, Literature and Translation from the University of Wisconsin–Milwaukee. Her professional experience includes roles in terminology, vendor, quality, and localization project management. Alaina is currently an assistant professor of professional practices in the Translation and Localization Management program at the MIIS at Monterey. In 2017, she launched her own company Afterwards Translations to offer localization consulting and training services. Alaina is membership secretary of ASTM International Committee F43 on Language Services and Products and serves as an expert in Technical Committee 37 on Language and Terminology of the International Organization for Standardization. She has been the Assistant Administrator of ATA’s Translation Company Division since 2018.

Work smarter, not harder: Scripts to enhance translator productivity

*Note: The instructions found in this post should work on the majority of Windows computers. Apple users, let us know if you come up with your own way of making this work!

Recently, my IT guy [husband] set me up with a great new tool. It has made my life as a translator so much more effective that it would be a crime not to share it with you all. I can see tips like this helping with productivity on so many levels and I’d love to hear what other hacks you all can come up with.

Here’s the trick: we set up a “script” to run on my computer so that whenever I hit CTRL+SHIFT+c on my keyboard, it automatically opens a new tab on my browser and performs a Google search for the text I’ve highlighted. I no longer need to copy some text, switch programs, open a new tab in Chrome, and then paste and search; I simply use my mouse to highlight the text I want to research and hit CTRL+SHIFT+c on my keyboard. I’ve used this about a million times since I started running the script a few months ago; here are just a few instances in which the tool has been extremely handy:

  • Reading through a source text in MS Word and came across a word I didn’t recognize
  • Wanted to make sure a phrase in my translation in Trados was the proper way to say something in target language
  • While editing a colleague’s work, wasn’t sure if the term they were using was the proper collocation
  • Reviewing my own translation, I came upon a name that I wasn’t sure was spelled correctly

You can imagine how often these situations arise in our daily work as translators, editors, transcribers, copywriters… you name it. Here’s how to implement the script on your device; be sure to let us know how it works and if you come up with any hacks of your own!

1. Download a scripting program (I used AutoHotkey)

2. Create your script (these instructions can also be found by opening the AutoHotkey program on your computer and clicking “create a script file”):

Right click on your desktop and select “New” > ”AutoHotkey Script”

Name the script (ending with .ahk extension)

Locate the file on your desktop and right click it

Select “Open with” > “Notepad”

3. Write your script: To write the script itself, just paste the following text into Notepad and hit save.



Send, ^c

Sleep 50




4. Run your script: To begin executing the program, just double click the desktop icon to run the script. You might not notice any change on screen, which is normal. Test that your script is working by highlighting text in any application and clicking CTRL+SHIFT+c simultaneously on your keyboard. If this operation opens your browser and does a Google search for the highlighted text, you’re all set!

5. Troubleshooting: If you find that your search script isn’t working, make sure you’ve set the script to run on startup (so that each time your computer restarts, the script runs automatically and you don’t have to remember to click on it). To do this, click Windows+r on your keyboard to open the Run dialogue box. Type “shell:startup” into the field and hit OK. This will open your computer’s Startup folder, which contains files, folders, and programs that are set to open or run automatically when you start your device. Just copy the file containing your beautiful new .ahk script from your desktop into this folder and you will no longer have to worry about it.

Another script I came up with to enhance productivity inserts a specific line of text that I use very frequently (“[Translator’s Note: Handwritten text is indicated in italics.]”) with just two clicks of my keyboard! What other uses can you come up with for scripts and macros like these?

For more ideas and help with AutoHotkey, check out their user forum here. A tutorial on the basics of AutoHotkey can also be found here. You’ll find that tools like AutoHotkey are a very simple form of computer programming, and similar to the languages that we work with as translators, computer languages have syntax, rules, and exceptions that can actually be fun and useful to learn about. Happy scripting!

Image source: Pixabay

So You Want to be a Freelance Translator (or Interpreter): Tech and Tools

This post is the fifth and final (first post, second post, third post, and fourth post) in a series of posts written in response to questions we at The Savvy Newcomer have received. Sometimes these questions have come from people within the translation world, but also from bilingual friends and family who are interested in translation and interpreting (T&I). Our hope is that this series will serve as a guide for people who are considering a career in T&I and want to know where to start.


So You Want to be a Freelance Translator (or Interpreter): Tech and Tools

When an artist sits down to begin a new project, he collects his paints and paintbrushes, selects the right canvas, sets up an easel, and sits down at a chair that’s just the right height. He also chooses the right setting to work in. What about translators and interpreters? What tools do we need to be prepared for the task at hand?


If you’ve started researching technology for translators, you might think that the only software a language professional uses is a CAT, or “computer-assisted translation,” tool. This couldn’t be farther from the truth! While a CAT tool is an advisable purchase and a time-saver in the long run, a number of other software tools exist that can be useful and beneficial to translators and interpreters. However, we’ll start with translation-specific software and work our way to other types of software you may not think to consider when equipping yourself as a translator or interpreter. The links included for each category are a non-exhaustive list—I’ve selected a few ideas to suggest based on what I have used myself and options that my colleagues and other Savvy team members have used.

Hardware: First things first! You need a device or devices you can trust. I personally prefer my ultrabook laptop over a desktop computer for quick, quality performance and mobility—be sure to select a machine with a strong processor and plenty of ram to handle many applications at a time and still operate quickly (8 or 16 GB is ideal). Other translators may use desktops and store their files securely in cloud-based storage so they can access them anywhere (say, from a tablet while on the road). Multiple monitors are also a good idea for translators, since much of our work involves comparing two documents (the source and target) or doing research in a web browser while working in a CAT tool. Having additional monitors helps reduce eye strain and the time it takes to open and close documents repeatedly, among a host of other benefits.

CAT Tools: A variety of vendors sell CAT tools from open-source to thousand-dollar project management versions, but the three I see most often are SDL Trados, MemoQ, and Wordfast. It’s important for beginner translators to be aware that a CAT tool is different from machine translation—CAT software helps you translate more efficiently and consistently by offering suggestions based on previously translated text from a “translation memory”. It can also aid your work by breaking down large chunks of text into more manageable pieces or sentences called “segments”. The makers of the various CAT tools available on the market will also offer terminology and localization tools, either paired with their main products or at an additional price.

Editing or QA Software: Editing software isn’t only for copyeditors and reviewers—it’s great for helping to check your own translation work as well. PerfectIt and Xbench are two favorites for proofreading and QA.

Invoicing: Some translators use a basic Excel spreadsheet to track projects and invoices, but you can also consider paying for an invoicing tool like QuickBooks, Translation Office 3000, or Xero to record your financial information, send invoices, and run reports.

Speech-to-text: Translators often find it useful to use speech-to-text or text-to-speech in order to dictate translations or proofread their own writing. Free versions of text-to-speech tools exist on most word processors, and Dragon Naturally Speaking is a popular speech recognition software that can help save time during translation.

OCR Software/PDF Editor: Clients will sometimes provide files in flat PDF format, which can make it challenging to estimate a word count or use the source file in a CAT tool. Software tools like Adobe Acrobat and ABBYY FineReader can help translators edit PDFs or run optical character recognition (OCR) in the course of their work.

Security: In order to comply with independent contractor agreements and government regulations, translators and editors should secure their files against viruses, hackers, and hardware problems. See this post on antivirus software for some helpful ideas. As for a backup solution to restore your data in the event of loss, options include cloud storage services, cloud backup software, and network attached storage (NAS) systems. Last but not least, don’t forget about encryption software.

Other Tools

Office supplies: Don’t worry about going to Staples and buying the latest standing desk right away, but make sure that you are comfortable in your office environment. You may not be concerned about health problems now, but if you plan to make a full-time job of freelance translation, you’ll want to invest in equipment that’s good for your health at some point! An ergonomic computer mouse and keyboard is a great addition to your office repertoire, and even if you aren’t ready to purchase an adjustable desk or exercise ball chair, you should be sure to elevate your computer screen(s) so that you won’t have to crane your neck to view it. Some companies, like Contour Design, for instance, will even offer a free trial so you can see if their products are right for you.

Then there is the matter of desk organization preferences. If your desk is too cluttered, invest in a file organizer. If you edit best by reading printed materials, buy a printer and some paper so you can make hard copies when reviewing documents. If you expect to be translating a lot of official documents that need to be notarized and mailed to clients, get yourself some stamps and envelopes. The bottom line is to purchase what you think you’ll need. Many office expenses are tax-deductible, so don’t stress over buying these small-ticket items for your office that make your work life easier or more efficient.

Print resources: Dictionaries may seem a thing of the past to anyone outside our industry, but they can be of great value for specialized translators in certain language pairs. You don’t need to have a library-sized collection when you’re just getting started, but keep an eye out for online sales or conference bookshops that offer the types of print resources you may want to reference depending on your specialty area and language.


So you want to be a translator or interpreter…what do you think? Are you ready to take the plunge? We hope this blog series has helped to answer some of your questions about getting started and put you on the path to a successful career in translation and interpreting. Here are a few more ideas of steps to take as you get started:

  • Join ATA and get involved by attending the annual conference, joining divisions, etc.
  • Join your local professional association and attend their events
  • Take a course or courses (see GALA’s Education and Training Directory, one of the courses offered in the ATA Member-to-Member Program list, etc.)
  • Read blogs or books by translators and interpreters (The Savvy Newcomer is a great start!)

As you take your first steps into translation and interpreting, keep in touch with us at The Savvy Newcomer. We would love to hear your advice for newbies to this profession.

Image source: Pixabay

Computer-Assisted Translation Tools: A Digest

I recently asked the community of translators on ATA’s Business Practices listserv to weigh in on the pros and cons of the Computer-Assisted Translation (CAT) tools they use. The question sparked a well-attended discussion, and brought helpful insight on using CAT tools in translation. I have compiled the conversation’s highlights here for the benefit of all.


Translators first adopted CAT tools—previously known as Translation Memory tools—as a way to efficiently catalog and retrieve their translations of technical words and phrases. These tools helped maintain consistency within a single document or across documents on a specific subject matter. They also saved the translator time by storing translations and supplying them on demand.

Today, CAT tools retain this fundamental memory function, and can further boost translator productivity and quality with the following features:

  • Autosuggest supplies recurring words and phrases and obviates typing them out each time they appear. One translator commented that, in some cases, she translates faster with autosuggest-assisted typing than by dictation using Dragon® software.
  • Quality assurance functions check the translation for omissions and numeral inconsistencies, and proof it using target language standards.
  • Side-by-side alignment of segments from the source and target texts helps maintain workflow by keeping the translator from getting lost while working between two documents.

Use with Caution

Several listserv members warned against allowing the tool to manipulate the translation through imperfect matches and suggestions. CAT tools are not translators, but tools that assist translation. The user should therefore always control the tool, and is responsible for reviewing the tool’s output with an expert’s eye. Furthermore, a tool’s original settings may not be the best; the user must be familiar with the software and be able to manipulate it to benefit his or her unique projects.


Most commentators agreed that CAT tools are most useful across technical documents in which subject-matter-specific terms must be consistent, and within documents with frequent repetitions. Creative works such as books or marketing copy benefit less from the tools’ memory function, since artistic expression is less repetitive and restricted than technical language. Nevertheless, a translator may leverage other functions, such as quality assurance checks and assisted typing, to efficiently process artistic translations. Again, the translator is ultimately responsible for the finished product. The skilled use of a CAT tool can help to create a better translation in a shorter amount of time, whereas an inept CAT tool user will waste time and produce substandard work.

Tool Choices

A few key considerations influence CAT tool choice. Several translators who responded to my question pointed out that, while direct clients may not care which CAT tool you use—and may not even be aware that you use a tool—translation agency clients often have tool preferences, and these preferences should guide your choice. You will attract more agency clients by having and being able to use a mainstream CAT tool, and can therefore reap dividends on the money and time you invest in buying and learning to use one. To a point, having and expertly using multiple tools will bring even more work, because you can target a wider segment of the agency market.

Cost may also influence your choice. Prices range from free to over $800; however, group buy discounts on can save you hundreds of dollars. You should also take advantage of free demo versions when they are available. Furthermore, keep in mind the frequency and price of updates and upgrades, which vary widely across tools.

Listserv respondents generally agreed that SDL Trados Studio is the CAT tool with the largest market share, followed closely by memoQ. Other tools that were mentioned (in no particular order) include Wordfast, Déjà Vu, OmegaT, and Across. Respondents recommended using the latest versions. Comments, some subjective, are given on each tool below.

  • SDL Trados Studio, widely used and demanded by many clients, is a feature-rich and powerful tool; nonetheless, it can be challenging to learn, has a congested interface, and is comparatively expensive, at $825 for the 2017 version. (The price, however, dropped to $575 on a recent group buy.)
  • memoQ, like Trados, is powerful and widely used and accepted, but it is nearly $200 cheaper. Some agencies lend a memoQ license, making purchase unnecessary in such cases. One user commented that the browser (online) version is not very useful.
  • Wordfast was described as not having as many features and options as Trados or memoQ, but, as a result, it is easier to master and still widely used. Like memoQ, Wordfast is cheaper than Trados, and was heavily discounted in a recent group buy.
  • Déjà Vu has strong segment assembly powers and is relatively inexpensive (listed at $450, and offered at 30% off on, but has weaker quality assurance features.
  • OmegaT is free and simple, and boasts a helpful support group online. One user complained that OmegaT does not segment Japanese very well.
  • Across: One respondent strongly discouraged using Across, as it apparently does not do much to assist translation. Corroborating this commentary, it has a rating of only two out of five stars on

As these tools have progressed, so has compatibility among them. A translator may be able to open in his or her favorite tool a translation memory file made with a different tool; or, an agency’s project manager may be able to open a translation in Trados that was completed in memoQ. Some respondents, however, still reported problems with compatibility, even among the mainstream tools. The shrewd translator who is aware of this pitfall will use caution when working across multiple tools. Nevertheless, once one has learned to skillfully use one CAT tool, the next should be much easier to master.

As the listserv discussion died down, I downloaded and began to use OmegaT (it’s free, after all). I had missed the first SDL Trados Studio 2017 group buy of the year in January, so I got the free and fully functional 30-day trial instead. When Trados was offered on discount again in February, I made sure to sign up, and have since purchased a full user license. Now the real work begins!

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Author bio

Paul Froese is a freelance Spanish to English translator specializing in scientific translation. A native of Walla Walla, Washington, he holds an undergraduate degree in plant science and biotechnology, and a graduate degree in crop science focused on plant breeding and genetics, both from Washington State University. Though a linguist since his late teens, he only began his translation career in 2016, and sees himself very much as a newcomer to the profession.

Visit Paul’s website at and his blog about trends in Latin American agriculture at E-mail him with ideas or suggestions at

Tech Talk: Software and Tools for Translators

Tech Talk: Software and Tools for TranslatorsIn 2014, I made two life-changing decisions: I committed to working as a freelance translator, and I purchased a PC after years of Apple use. I bought a cheap Lenovo, and told myself that, if I wanted to make money (which I wasn’t, then), I needed to spend it. Simple enough.

Then I tried opening a Microsoft Word file, only to learn that MS Office shipped separately from the computer itself. It might as well have come without a screen. What good was a laptop if I couldn’t even write something on it? On top of which, I’d have to pay a subscription for the privilege of downloading MS Office?

You cannot be a good, efficient, professional translator without the right technology, but professional-level software can be expensive, presenting a challenge for some first-time translators.

If you are looking to cut costs in at least one area, take heart: the web is full of free and open-source software that translators can use. Here are five programs I’ve found invaluable, not only because they literally have no price tag.


OmegaT is a free, open-source computer-assisted translation (CAT) tool in the same model as such proprietary CAT tools as memoQ or Trados. It takes up comparatively little space on your hard drive and is easy to learn to use: it comes with a preinstalled guide for getting started, making it ideal for new translators. OmegaT lets you create, manage, and import translation memories and glossaries, breaks text into easily translatable segments, and allows for easy insertion of previously translated terms, which will reduce your translation time enormously. A perk of its being open-source is that independent developers have written scripts and plug-ins, making it more customizable than other tools on the market. Speaking of which, you may wonder why, if there’s a free, customizable CAT tool available, a market for paid ones exists at all. First, OmegaT is not the industry standard. Most translation companies and freelancers use a proprietary CAT tool. For compatibility reasons, especially if you access the company’s TMs through the cloud, you may have to use the company’s CAT tool.  Second, open-source software is not known for its polish. OmegaT’s interface looks like it was designed by someone with Windows 95 nostalgia; personally, I’ve found its layout confusing, especially when looking for other segments. Nevertheless, it’s the quickest, cheapest way to introduce yourself to an essential translation tool.

Google Drive and Google Docs

You generate a lot of files when you translate, and they take up space. They’re also troublesome to search through. Enter Google Drive, a cloud-based (read: not on your computer) storage system for nearly anything with a file extension. Google Drive lets you create as many folders as you need to organize your materials and gives you 15 GB of storage for free. For $1.99 a month, you can increase that to 100 GB. You can use Drive to create any kind of document or file you might create using Microsoft Office with the benefit of instant saving and the ability to revert to previous versions very easily. It’s also portable: files can be converted to Drive format easily, meaning you can take an MS Word file and edit it from anywhere with an Internet connection. Searching for files on drive is also easier than on your computer, for the simple fact that you’re using Google’s search function, and not Microsoft Explorer’s. When was the last time you Binged something?

Drive isn’t the only cloud-based storage system: Dropbox is also free, and you can use Apple’s iCloud or Microsoft’s OneDrive. Still, Google Drive integrates directly with other Google software, notably Gmail. You can add Drive to your desktop as well, making it easyto transfer materials from your computer to the web. As more and more companies move toward cloud-based storage systems, using and understanding Drive will make it easier to collaborate with potential employers.

However, it’s important to realize that the cloud is not completely secure, and someclient contracts stipulate that translators not store any files associated with the translation on cloud-based servers. Nevertheless,many translators still use Google Drive or one of its competitors for collaboration with other freelancers or to have personal documents within easy access, and not all clients are as sensitive to the cloud


For all its convenience, Google Drive is useless without an Internet connection. OpenOffice, a free version of word processing tools similar to the Microsoft Office suite, works offline like any regular piece of software, and isn’t subject to the connectivity hiccups that can slow down Google Drive. LibreOffice is another free word processing alternative to Microsoft Office many people use. For my purposes, the best thing about OpenOffice is that it’s intuitive: if you can use Microsoft Word, you can use OpenOffice Writer.

OpenOffice’s great shortcoming, which it shares with Google Docs, is that it doesn’t create the same type of files as Microsoft Word. This can lead to compatibility issues and inconsistent formatting. A Word document won’t necessarily retain all its features when you open it in OpenOffice, and vice-versa, meaning you must be ruthless in checking that you send a properly formatted document to clients. The consequence is that many translators do purchase Microsoft Office by the time they work with paying clients.


Evernote is a sort of notepad that syncs across devices. It allows you to create checklists, take notes, and collaborate with other users. You can also use it to bundle notes together, making it a great tool for tracking clients and keeping client-specific information within easy reach. Instead of, say, keeping one spreadsheet for client contacts and a separate text file for notes taken at conferences, you can create and link two notebooks in Evernote, making useful information much more easily obtainable. And unlike Drive, it runs without an Internet connection.


I’d had no idea I might need to use a file-renaming device until Jost Zetzsche’s most recent Translator’s Tool Box came out and featured ReNamer at the top. (Are subscribed to the Tool Box? It’s a stream of tech information specifically for translators from one of the most successful translators in the industry, and there’s a free version.) It only takes a few email exchanges with a client to learn just how quickly different versions of documents can accumulate, all of them with the inevitable _proofread_edited_re-edited attached to the end. Say you have a naming system for your files that your client is disregarding, and you want to keep your records consistent: ReNamer allows you to rename files without opening them or using any of the clunky techniques you’d have to use in Windows Explorer, and it can do it in bulk. Ten different files that you’ve translated and want to label as such? ReNamerinserts_translated to all of them with one click of a button.

A good rule for anything software-related is that if a proprietary version of something exists, a free version does too. It takes very little searching and tenacity to derive as much utility from free software as from paid, which can make a big difference if you’re a first-time freelancer looking to move up from living on cheese sandwiches. And these are only five examples; what do you get for free that the rest of us pay for?

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Author bio

Dan McCartney

Dan McCartney is a freelance French and Spanish to English translator based in Chicago. Before translating, he worked as a consultant, instructor, and freelance math problem writer.