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Another thread related to CAT Tools I read was one about estimating the price of a translation in case of a number of repetitions and I got to know the opinions of professionals on this subject. It was commented by many translators who use CAT Tools in their work -- the majority of them wrote that even if there is a number of repetitions, the translator has to check the correctness of a translation proposed by a CAT and confirm it, which requires professional knowledge and time, therefore the price estimation of a translation should not be lowered.
Date: February 2021

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There are numerous videos available on the SDL Trados Studio website for free, which make it possible to understand the tool better and develop skills required to use it effectively.
In the introductory basics video I learned:

how a TM works and how to create one

the different types of TM matches

how to translate faster with upLIFT Fragment Recall and Fuzzy Match Repair

how to create a TM from previous translations using alignment TM strategies: for example if a big TM or multiple small TMs should be used

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There are numerous programs that can be used to support the translation process. I found a graph presenting the most popular CAT tools divided into four different groups: paid/free, online/desktop.

source: smartcat.com
Date: February 2021

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During the process of self-learning, I was wondering how popular CAT tools are among translators and whether they are commonly used by them. I found information that around 85% of translators use CAT tools in their daily work.

source: smartcat.com
Date: February 2021

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When studying for the final test, I revised all my notes and made sure I know how to:

✓ create a translation project
✓ set project deadline
✓ add / create a TM
✓ add / create a TB
✓ add words to a TB
✓ verify a translation
✓ run spellcheck
✓ save a TT
✓ create a project package
✓ convert an Excel file to SDL MultiTerm

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I think there should be more projects carried out at the university like the post-editing project we had. Even though it required a lot of effort and was quite time-consuming, it was very useful and educational because we had to do it ourselves, from beginning to end, solving problems, which came up, on our own. Such projects teach us a lot and require a lot of self-management and organization. Moreover, I believe it would be a good idea to work on specific tasks/projects together in small groups because the number of skills that can be learned along will always later be needed and reflected in the workplace as well as in life.

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During CAT Tools I had a chance to learn one of the best/most popular tools and now I understand why professional translators use it in their daily work. I would like to "invest" in it one day too as I feel it is worth its price. I am used to working on a Macbook, which does not like CAT Tools so much. That is why I had to save up money and buy a Windows computer so that I could use the tools and all the necessary programs I worked with at work and during our classes. At first, it was difficult to switch to new software, but with time I got used to it. Additionally, I am aware of the differences between the two softwares and I know that Windows is more popular, especially when it comes to the translation industry. It is very unlikely that someday at a translation agency I would work with a Macbook, that is why I need to work on a Windows computer and develop my skills related to this software not to stay behind.

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Having done my internship in Chicago at the oldest Polish newspaper in the US -- Dziennik Związkowy, thanks to the successful mutual collaboration, I was offered a job as a proofreader and a translator in DZ's editorial office. My main tasks are proofreading and editing texts before they are printed out, writing articles and translating articles and advertisements. This experience gives me a chance to work with something I'm still learning and mastering, that's why I appreciate it a lot.

I also work as a freelance translator and proofreader. I recently translated texts for Muzeum Fotografii in Krakow as well as movie scripts. When it comes to translating, I usually like to work on my projects in Memsource since it's a tool available online and MemoQ, and for now, these are one of my favorite along with Trados.

Having discovered post-editing more during my CAT Tools &Post-editing  class and considering my experience, I am very positive that I will want to continue working in this field and developing my knowledge in order to become more professional.

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In the process of learning, I learnt some essential keyboard shortcuts, which speed up the process of translation. Even though at first I was not used to using shortcuts and I didn't feel the need to use them at all, I tried to remember some of them and to actually use them, which finally convinced me that they really are helpful, make our life easier and help the work go smoother.

Open project File - CTRL + O
Open package File > Open > Open package
Remove project - Projects view > right-click > Remove from list CTRL + ALT + F4
Save file - CTRL + S
Save all files - CTRL + SHIFT + S
Move to next unconfirmed segment without confirming - CTRL + DOWN
Move to previous unconfirmed segment without confirming - CTRL + UP
Copy Source to Target - CTRL + INS
Clear target segment - ALT + DEL
Browse TM matches - ALT + PgUp/PgDn
Select current TM match - CTRL + T
Merge Segments Select segment numbers (use SHIFT to select multiple) > CTRL + ALT + S
Activate termbase term recognition View ribbon > Information > Term recognition
Activate termbase search View ribbon > Information > Termbase Search
QA check current file Review ribbon > Quality Assurance > Verify F8
QA check all files Projects view > right-click project > Batch Tasks > Verify Files

Date: November 2020

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SDL Trados offers an app for devices called My SDL Trados, where I have access to all resources.  In the app you can find a list of webinars and take part in them, access a library of product content, feature demonstrations and functionality guidance, hints and tips on how to work with the CAT tool  and how to solve problems. In the app you can also download different apps such as: MyMemory, Glossary Converter or IATE Real-Time Terminology.
I came across the app while looking for what else SDL Trados has to offer to translators and I downloaded it to my phone, where I sometimes scroll it and watch different videos that are shared there.

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In my free time, I sometimes read threads on various Facebook groups for translators. This way I'm able to get some first-hand information, see what professionals deal with, what problems they face and how they solve them. One of the posts I read when I was trying to find threads about some CAT tools or post-editing was one, in which translators were talking about post-editing and machine translation in the group "TŁUMACZE - GRUPA OTWARTA - ZLECENIA DLA TŁUMACZY". They shared their remarks from telling about their experience with clients, providing quotes, the meaning of machine translation and what the client might refer to when looking for a post-editor to the future of the profession of a translator and the role of post-editing in it.  I find it great that one group associates so many professionals. Whenever I have a question related to translation, I use the search tool in the group and look for threads that could possibly be an answer to it and help me.

*although the thread is not recent, I found it very helpful and informative
Date: February 2021

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Cochrane - my translation vs. final version

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Cochrane - my translation vs. final version

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- In the future, I'd like to gain more knowledge about machine translation as I feel the need to understand how they work better as well as post-editing, with which I would like to work one day.  There are a couple of positions I found, which could be helpful:
- an article, which can be found on Jstor "Translating by post-editing: is it the way forward?" by Ignacio Garcia

-  “No rage against the machine.”, Bonet, Josep (2013)
- "Neural Machine Translation" and "Statistical Machine Translation" by Philipp Koehn

These resources will help me in answering the question of whether machine translation will replace human translators and prepare solid arguments for my current standpoint -- that they will not.

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I created a table, in which I compared my translation with its final version. It let me see what kind of mistakes I made and how a good translation of my text should look like.

The fact that I had a chance to take part in the project and be the General Project Manager in the past semester taught me a lot and I already noticed that it is of great advantage when it comes to job recruitment processes. It showed me that I would like to take on a role like that again in the future but before that work on skills needed to become a good translator/post-editor and project manager.

In the future, I would also like to translate more texts for Cochrane as thanks to the revision that is made, one can learn a lot from the mistakes.

Date: January 24, 2021

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SDL Trados Studio provides free webinars for translators. There are 9 upcoming ones planned for February. The ones I will do my best to participate in are "How a CAT tool can help you?" on Feb 25th (a screenshot showing I added it to my calendar to remember) and "Using neural machine translation (NMT) in SDL Trados Studio and SDL Trados Live" on Feb 23rd.
In my opinion,  we learned a lot during our CAT Tools classes but in order to know the subject of post-editing and CAT Tools inside out, there is still a long, long way ahead of us, which requires a lot of self-practice and studying. That is why we should take advantage of the resources available online as there is a countless number of them to be found.

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How to convert a Microsoft Excel File to SDL MultiTerm?

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How to convert a Microsoft Excel File to SDL MultiTerm?

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In order to be able to use an Excel file glossary in SDL Trados Studio, I needed to learn how to convert Excel File to SDL MultiTerm.

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How to use the translation alignment tool in SDL Trados Studio?

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How to use the translation alignment tool in SDL Trados Studio?

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Date: November 2020

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How to use the translation alignment tool in SDL Trados Studio 2019

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An encouraging and motivating note about learning MultiTerm and other CAT tools written by a translator who talks about her experience. 

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An encouraging and motivating note about learning MultiTerm and other CAT tools written by a translator who talks about her experience. 

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Date: November 2020

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Hey, all you CAT software users out there! If you are anything like me and balk at the idea of learning and using the powerful features of your CAT tools (either due to laziness, chronic lack of time or just a general dislike of change), let me tell you, you are seriously missing out.

A PM recently ordered me to use MultiTerm to do what was an extremely repetitive and fiddly job. He even sent me a glossary to convert and load. I was horrified! That meant opening TWO PROGRAMMES that I had never used before and then actually USING THEM. I did try to protest. "I don't know how to use it!" I whined. However, the PM just sent me a link to a website with some instructions on a website and left me to get on with it.

Realising I had no choice but to obey, I bit the bullet, followed the instructions and.... I'm in love! Yes, it's true. I'm in love with MultiTerm. Now I would no more do a job in Studio 2014 without it than I would type into an original document.

Since I finished that job, more accurately and in half the time it would have taken me otherwise, I have had another couple of similar jobs for which I have created my own glossaries. Not only is the result better than it would be otherwise, but I also feel more confident and much happier while doing the work, not having to click on concordance and wade through stuff and not making typos.

I have become evangelical about MultiTerm. I have even forced fellow translators to learn how to use it. I hope you are grateful, girls!

Heaven only knows what other fantastic and yet undiscovered features these CAT tools have. I will investigate.

So that got me to thinking about other things I am resisting. Who knows what I might be missing out on? I know it's time to change. Is it too late to learn to ski? Perhaps it is. Anyway, that's why this morning I am not wearing my usual translation uniform (which consists of a thick fluffy dressing gown over pyjamas with slipper socks). I have had a shower, done my hair and put on some light make up. At least if the postman comes he won't think I've got flu and run back down the drive and jump on his Vespa!

Juliet Allaway

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Hey, all you CAT software users out there! If you are anything like me and balk at the idea of learning and using the powerful features of your CAT tools (either due to laziness, chronic lack of time or just a general dislike of change), let me tell you, you are seriously missing out.

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I discovered this Youtube channel, where you can find videos and tutorials about translation with the use of CAT tools. It provides useful information and tips on how to work with them.

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MultiTerm is ideal for translators working with bilingual glossaries or ongoing projects with highly specific terminology. It is one of the most powerful data terminology management tools on the market. What the MultiTerm product does is convert the glossary into an .xdt file which is recognized by Trados and can then be linked to the Trados workbench. What this means for the translator is that instead of searching through countless entries of an Excel file looking for the right term, Trados will automatically recognize if the term is in the glossary, and if so, it will display the corresponding translation in the “Term Recognition” window on the Workbench."

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BLEU scores are the bread and butter of (almost) everyone who works with Machine Translation. If the layman tries to understand MT, they will often find their search come to nothing as they drown in a pool of technical buzzwords. To clear up at least one of these enigmas, today's post will explain what "BLEU scores" are - and why debates on the accuracy of translations sometimes assume near-philosophical dimensions.

What might sound like a color at first, is actually an acronym for "Bilingual Evaluation Understudy". In July 2002, a research group from IBM published a report, in which they explain why human evaluations of Machine Translation are both too time-consuming and expensive. They recommended a more affordable method that should help support people reach quicker results - BLEU.

"Human evaluations of machine translation are extensive but expensive. [...] We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent [...]. We present this method as an automated understudy to skilled human judges which substitutes for them when there is a need for quick or frequent evaluations."

At its core, the team (made up of Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu) used a simple approach: BLEU scores measure the degree of difference between human and machine translations. A relatively simple algorithm is used. First, individual segments (usually sentences) are compared, after which an average value is determined for the entire text. As the machine translation comes closer to the human translation, the score goes up. A scale of 0 to 1 is generally used, with a score of 1 meaning identical with the human reference translation, and a score of 0 signalizing that the machine translation has no matches with the human translation. Many MT developers multiply this scale by 10 or 100 and the scores go up correspondingly. A scale of 50 therefore does not imply 50 times perfection, but rather corresponds to a score of 0.5 on the original scale. The goal is not to achieve a score of 1 (or 10/100), because the translation does not necessarily have to precisely match the available reference translations. The real goal is to produce translations with the highest degree of accuracy, and not to imitate the provided references.

The score works in the same way for all languages, which makes BLEU a very practical metric. The algorithm is also identical for all languages, and it does not take into consideration which languages are used - only the differences between the "new" translation and the reference. Scores usually fall between 20 and 40, or 0.2 and 0.4. This might seem like a low average, but seeing as how even humans cannot attain "perfect" scores, these scores are actually a lot more impressive than they might seem. Fair enough - so what's the problem?

The difficulty of accuracy scores

A fundamental problem in Machine Translation revolves around the difficulty of measuring the accuracy of translations. Translations vary from one human to the next as well, which makes it hard to specify with whom or which reference the machine translation should be compared in the first place. When is a translation 100% accurate?

Let's look at the simple example of image recognition: does the picture show a blue wall - yes or no? This question is easy to answer. The question of whether a translation is correct, on the other hand, is much harder.One example is the problem which revolves around the translation from gender-neutral to gender-specific languages. "I'm visiting a friend this weekend" is hard to translate to many languages (among which French, Spanish, German, etc.). Is the friend male or female? The meaning of certain words is simply impossible to determine without more context. When announcing the title of the Harry Potter book, Harry Potter and the Deathly Hallows back in 2006, Joanne K. Rowling left fans with many questions. What do the hallows refer to? Saints? Cries? There were plenty of ideas and theories, but only the content of the book - this concerned three magical objects - could help a translator find a fitting translation. How can we expect a computer to produce accurate results, if humans already have various options at their disposal?

BLEU is rather shortsighted

BLEU has a major blind spot. A sentence that was actually translated correctly, can still receive a low score, depending on the human reference. Moreover, BLEU cannot evaluate the importance of errors. For a BLEU score, an error is just that: an error. In real life, if a word is placed incorrectly within a sentence, it can change its entire meaning. BLEU does not take the gravity of errors into consideration. Generally speaking, the score is not suitable (and was never intended) to evaluate machine translations. A very low score does generally reliably indicate that a translation is of poor quality, but a high score can also simply be the result of an error in the system. Usually, companies only achieve exceptionally high scores if they compare machine-translated content with several reference translations. This makes sense, because there are usually various correct translations, but this method still distorts the test results. Additionally, BLEU can hardly be used for evaluation in post-editing - where a very similar score has replaced BLEU: TER.

The developers of BLEU were already aware of these shortcomings when developing the metric 18 years ago. BLEU scores should be used as a form of efficient benchmarking, and are not designed to provide detailed error reports and suggestions for improvement. BLEU can answer a question such as, "Is our current system better than the old one?". This is exactly why the algorithm was developed. The details are then analyzed manually, by humans.

Bottom line

If sentences and texts were translated word by word, the metric would not suffer from shortcomings such as difficulties in measurement (and Machine Translation would be a much simpler task in general). But that's not how humans use language. They are more complex - even artificially developed languages. Different languages have different sentence structures and are additionally based on other cultural customs - word play, rhythm, target audiences, etc. - the context and stylistic devices in which sentences are embedded are of paramount importance. Only humans can properly understand and implement them. Accurate (and above all good) translations require in-depth knowledge of the languages used as well as a profound understanding of the culture of the relevant country. BLEU scores do immensely help research by providing quick evaluations. Want to learn more about working with Machine Translation? Read our interview with Team Lead Machine Learning Martin Stamenov.

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MT for Beginners: What is BLEU and what is wrong with it?

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BLEU scores are the bread and butter of (almost) everyone who works with Machine Translation. If the layman tries to understand MT, they will often find their search come to nothing as they drown in a pool of technical buzzwords.

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An overview of machine translation systems, which was quite useful before beginning to work on the post-editing project. 

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An overview of machine translation systems, which was quite useful before beginning to work on the post-editing project. 

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Date: December 2021

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During the class each person was supposed to carry out a post-editing project. We had to choose 2 texts - one specialized and one non-specialized, and translate them with the use of a statistical and neuronal translation system.
· statistical system: GOOGLE SHEETS - translation formula: =GOOGLETRANSLATE (A1, “en”, “pl”) – oprogramowanie online Excela

· neuronal system: DEEPL

For each of the texts, we did post-editing and we needed to collect data on what workload and how much time we have to invest in achieving  the "publishable" quality.
The project helped me understand how the SMT and NMT systems work and what is the level of difficulty when it comes to the process of post-editing of these texts.

Doing this project and learning about post-editing, at first I had a great difficulty differentiating between light and full post-editing and sticking to light post-editing only. Thanks to the precise analysis of TAUS's guidelines after the class and practicing on random texts I translated with the use of e.g. DeepL, I finally grasped the difference and now find it easier. However, I still have to work on it as I have the habit of correcting almost every single thing I feel could be replaced with something better, which I am trying to get rid of.

Date: January 2021

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SDL Trados/MultiTerm
Raw Output Evaluator (ROE)
Notepad ++
Flashback Express

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BLEU stands for Bilingual Evaluation Understudy and it is a score for comparing a candidate translation of text to one or more reference translations.

Date: January 2020