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My introduction to OmegaT part 1

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My introduction to OmegaT part 2

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First steps with OmegaT 4.0 (2016 edition)

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A screenshot of me using the glossary while translating a book of my choice.

(time spent working: approximately 1 hour)

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A screenshot of me using translation memory and inserting fuzzy matches.

(time spent working with the text: app. 1 hour)

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A screenshot of me translating in memsource

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A screenshot of me translating in memoQ and learning how to perform various operations.

(time spent working with the text: app. 3 hours)

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I took part in a translation project for Cochrane. I was responsible for the translation and revision of several (1 text to translate, 2 other to revise) specialized texts (medical field) called Plain Language Summaries.
Here's a screenshot of me translating in memsource.

(time spent working with the texts: app. 3-4 hours translating each text, 2 hours revising)

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Here's the website where the Plain Language Summaries translated or edited by me appear once they are verified by Cochrane.

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Zamierzeniem niniejszego przeglądu Cochrane była ocena efektów zmierzających do zwiększenia liczby szczepień przyjmowanych przez młodzież. W poszukiwaniu odpowiedzi na to pytanie badacze z Cochrane przeanalizowali odpowiednie wyniki badań i znaleźli szesnaście, które dotyczyły tego problemu.

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Screenshot of notes on OmegaT functions provided by my PM, which turned out to be very helpful when I encountered problems with the program (such as the spellchecker).

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Screenshot of notes on memoQ provided by my PM, which I used frequently when I was familiarizing myself with the program (especially when I had to learn how to perform alignment).

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Here's the website where I found solutions to the problems I came across when I was working with memoQ (such as joining of segments).

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The tutorial I watched to get myself familiar with Memsource Editor, particularly how to run spell-check for Asian languages, which is helpful as I'm trying to better my Japanese and add it to my language pairs.

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I was curious how to create a TM in a different CAT tool than memoQ. This video taught me how to perform alignment in Memsource, fix segments in Excel, and allowed me to compare memoQ and Memsource in terms of creating TM.

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Memsource tutorial: How to Align Texts to import to translation memory (TM)

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Nov 5th / Glossary Converter + Term Base

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Nov 5th / Glossary Converter + Term Base

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I watched this video tutorial to learn how to convert files to .tbx format in Glossary Converter. While I haven't figured out yet how to do it, I've learned many useful things about working with SDL Trados Studio, like:

- how to translate a single document without creating a project
- how to add a machine translation engine
- a selection of the OpenExchange Apps
- that it's possible to convert files back and forth between formats
- how to add a new term base mid-translation
- what it looks like when Trados Studio and MultiTerm work together on one computer

The tutorial was recorded a few years ago. I tested it out with the 2021 version, and some features seemed to not work (e.g. the MT - because not purchased).

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Here's where I found out how to convert files to .tbx format in Glossary Converter. I also converted them to .tmx and ran memoQ to see if it indeed worked.

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converting sdltb tbx sdl trados support tech translators translation forums discussion help solution

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Nov 16th (updated Nov 20th) / Trados check-list 

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Nov 16th (updated Nov 20th) / Trados check-list 

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Basic operations I know how to do:
+ create a new project
+ add/create a TB/TM
+ add files to the project
+ translate the segments
+ access project settings
+ add comments
+ search for help
+ add new terms to TB

Operations I intend to practice in the future:
- pre-translate the document (update: done, but didn't work for the whole document --> to check; I suspect what the issue was - probably better not to pre-translate using a new TM when some segments were already translated by me --> to verify with a new document)
- generate TT (update: done)
- review the document (update: done, together with accepting/rejecting changes --> must remember to be careful with that, there were some issues due to my haste)
- analyze files (update: done)
- align documents (update: done, new TM created)
- update: convert TM from .tmx to .sldtm (with help I found here: https://www.proz.com/forum/sdl_trados_support/269659-how_to_convert_tmx_into_sdltm_tm_or_xliff_file.html)

Things I need to check:
- what to do when a term doesn't show in the term recognition field but it definitely exists in TB
- can you use two TB at once when translating one document - didn't work so far
- what to do when concordance search doesn't work (but the search box in review does) - though I have a suspicion it's because I probably haven't enabled character-based concordance search when creating a TM --> to verify (update: it was indeed the reason why)

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The most helpful tips I've found on postediting include:

- the type of postediting depends on the client's instructions
- if the MT output is good enough, don't change it (!!! something to remember!!! as I tend to try to polish the TT to perfection)
- check the terminology only when the MT output is clearly wrong
- don't forget about the QA!

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Nowadays, the output from MT post-editing is getting better and better, especially since the increased use of neuronal machine translation. It means that companies, in order to save time and money, are more and more inclined to use this technology for their translation projects.

Of course, the results are still not good enough for publishable content and this is why we need post-editing. For more information about that, feel free to check out this knowledge post! But what is post-editing exactly? What are the different types of post-editing? And more importantly, what makes a good post-editor? What are the most common mistakes made by the machine? And how can the translator of today become the post-editor of tomorrow? Let's find out!

What is MT post-editing?

Post-editing is slowly changing the landscape of the translation industry. But what is it exactly? It is the process by which a translator or a linguist reviews a translation generated by a machine and then corrects it in order to make it intelligible. Depending on the clients' instructions, different types of post-editing can be used. For texts that are not meant to be published or when there is not enough time and only the overall meaning matters, light post-editing can be a solution. For this type of post-editing, only major mistakes that impact the ability to understand the text (mistranslations, omissions, additions, etc.) are corrected. Spelling, grammatical, or punctuation mistakes may not always be corrected, which can be a real challenge for any translator.

For publishable content, full post-editing is the solution; the final text has to read as though it was written by a human translator. It involves following the client's terminology, adapting to the source text style and format, keeping consistency, and of course correcting every single mistake that is detected.

Tips for future post-editors

In order to provide the best quality translation, the translator might be tempted to translate from scratch. But MT post-editing requires you to use as much MT output as you can. There is no need to over-edit, change the style or use nice synonyms. Here are a few tips to help you get started as a future post-editor:

Use as much MT output as you can: if it's good enough, don't ignore it and don't erase it. Be quick, as time equals money.

Read the instructions very carefully: each client will have their own guidelines, be sure to follow them!

Don't use synonyms: if a word or sentence is correctly translated, you do not have the obligation to substitute such expression with one that sounds prettier.

Checking terminology is obviously a useful step, but this only needs to be carried out thoroughly when the MT output is clearly wrong.

Go back and read everything again once you have finished (well, let's hope you already do that).

Perform a QA check at the end: the machine is not 100% reliable and I'm sorry to break it to you, but neither is the person post-editing the text afterward.

And finally, keep an open mind. The machine is a tool and is there to help you, not to replace you. Ignoring it won't make it go away. The industry is evolving and you must evolve with it.

Sounds hard, right? How can I ignore all of my instincts and work faster?

Common machine translation mistakes

One of the most important things is to understand the machine and to know what to expect from it. So, here are the most common mistakes made by the machine:

Added/omitted words in the target

Mistranslations

Wrong word order for compound nouns

Wrong gender or number

Wrong punctuation

Wrong capitalization

Inconsistencies

Words in source language left in the target

Wrong/missing articles

So, hopefully, you are no longer apprehensive and are now ready to take on the challenge of MT post-editing. Now it's your turn. What do you think? Do you think every translator should start preparing for this new task? Read more related topics at our blog. Feel free to share your thoughts in the comment section!

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MT post-editing: definitions and tips for becoming a good post-editor - Pangeanic

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Of course, the results are still not good enough for publishable content and this is why we need post-editing. For more information about that, feel free to check out this knowledge post! But what is post-editing exactly? What are the different types of post-editing? And more importantly, what makes a good post-editor?

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Some further postediting tips:

- prepare the ST specifically for MT
- the choice of MT engine matters!
- postediting strategy is a spectrum, choose the one most suitable for the project
- TB and other references help with consistency (something to keep in mind when multiple post-editors work on one project)
- postediting analysis is important because the ST might need adjusting or more reference documents

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Machine Translation Report

What is the optimal MT Engine for you? Find out in the latest MT Report by Memsource.

Post-editing Machine Translation Best Practices

Machine translation (MT) is continuously improving by becoming faster, cheaper, and more accurate. Yet it still has some way to go before it achieves parity with human translators. Post-editing machine translation (PEMT) helps combine the best of both worlds: the speed and ability of MT engines to quickly handle large volumes of text, with the skill and sensitivity of trained linguists. Read on to find helpful tips for implementing post-editing in your translation workflows.

Looking for a more general introduction to machine translation? Check out our Beginner's Guide to Machine Translation.

Perhaps the most important step in post-editing happens right at the start: making sure that you have a high-quality MT output. The better the original engine output, the less work will be required during the post-editing step, leading to faster turnaround times at a lower cost. Here are two points to consider:

1. Start with the Source Text

As with all translation projects, the source text should be carefully created or pre-edited to ensure that both the post-editors and the MT engine are working with the best. Early errors can compound and create problems down the line. That's why it's important to make sure that the original text has as few spelling and grammatical errors as possible. The terminology and formatting should be consistent. The source text should, at the very least, be prepared as if it was going to be handled by a human translator.

For the best results you should consider preparing your text specifically for machine translation. Although MT is rapidly improving, there are certain steps you can take to increase output quality.

Generally, MT works best with input that is clear and concise. An ideal sentence should be under 20 words and have simple grammar. Complex sentences or headline-style sentence fragments do not work well.

MT also tends to struggle with nuance and likes to be as literal as possible. Avoid sarcasm (machines are really good at picking it up), avoid double-negatives (they won't do no good), and where possible have the dates in a non-numeric format. 01/05/2020 can lead to some ambiguity: is the 1st of May or the 5th of January?

For other useful guidelines, consider having a look at IBM's Machine translation tips.

2. Choose the Optimal Translation Engine

There is a large number of MT engines to choose from and new engines are being developed all the time. Not all engines are created equal, some simply perform better than others. Some are more suitable for specific language pairs or subject matter (domains). Choosing the most effective engine for your project can save a lot of time and effort.

Consider a range of generic MT engines and evaluate them using samples or past experience. Although creating custom analyses can be a time-consuming option, it can lead to cost-effective solutions in the long run. Another option is to consider a customized translation engine, trained using your own data. This will generally produce high-quality results for the content that you are used to working with.

Memsource Translate offers an interesting solution to the problem of MT choice. This feature was developed by the AI team at Memsource to dynamically select the best MT engine for your content. It considers the text's domain, source and target language, and looks for an optimal MT engine based on past performance. It will always automatically select the best engine for your content. Find out more about Memsource Translate here.

Once the MT engine produces its translation, post-editing can begin. How much post-editing is required varies from project to project, so it is important to always define your expectations early. The three main considerations are time, quality, and cost. Build your post-editing strategy around this by choosing the right approach.

Light Post-editing (LPE)

With LPE, raw MT is only modified where absolutely necessary to ensure that the output is legible and accurately conveys the meaning of the source document. The post-editor should be especially mindful of errors that might hinder the document's purpose or outright subvert it. Without review, raw MT can create embarrassing results, as one tech giant discovered recently. The editor should aim to make as few edits as possible. This approach can lead to fast and cost-effective results.

Full Post-editing (FPE)

With FPE, raw MT is thoroughly reviewed and modified to ensure that there are no errors whatsoever. Where LPE focuses on the bare essentials of accuracy and legibility, FPE considers a number of factors, including but not limited to:

stylistic and tonal consistency within the document (and with other appropriate documents)

the absence of all grammatical errors

appropriate cultural adjustments for the target language (such as idiomatic expressions)

A document that has gone through FPE should convince its reader that it was originally written in the target language. This approach is slower and more expensive than LPE, but will achieve a high-quality output.

Project-specific Strategy

Requirements should always be tailored to the specific translation project. It can be useful to think of LPE and FPE as being on a spectrum rather than a binary choice. Pick and choose your post-editing priorities based on considerations of time, cost, and desired quality. It can be effective to selectively prioritize certain segments that have a higher business value than others.

Another option to consider is bypassing post-editing completely. For certain projects this is possible, for example with internal documents where MT output is expected to be good and the consequences of bad translation are negligible.

To help with post-editing, both editors and managers should be aware of the various tools that can help. Virtually all CAT platforms now offer support for post-editing. Here are a few tools to keep in mind:

1. Set up terminology management systems to help post-editors ensure consistency. This includes translation memories, term bases, and any useful reference documents. These should be kept up-to-date for future projects.

2. Use Machine Translation Quality Estimation (MTQE). In the Memsource Editor this AI-powered feature can provide quality scores for all MT output, giving editors an indication of how much work is required for each specific segment. This can help linguists and project managers identify segments that they should prioritize for editing.

3. Use a QA tool, either integrated or standalone. These will help dynamically identify any issues in the original output that were overlooked or new errors that were introduced during the post-editing step.

It is important to address perhaps the largest misconception with PEMT: the post-editor is not a translator. Though there is some overlap in the nature of the work they perform, the exact skills required differ in a few important ways. In 2017, ISO 18587 defined some of the key aspects of PEMT, including the specific skills and competencies of the post-editor.

Best results are always obtained with qualified and experienced post-editors. An ideal post-editor will have practical experience working in the specified language pairs, with the content, and with the relevant tools. It can be helpful to train post-editors for specific tasks before they start on a project. Formal training courses are now available, such as TAUS' Post-Editing Course.

To help improve the results of PEMT, it is important to continuously evaluate the process and results using data and feedback. Consider post-editing to be an iterative process that can be improved with time and experience.

A range of tools are available to help with post-editing analysis. For example, the Memsource Editor can help calculate post-editing effort, which shows how much work was required by linguists to finalize the translation. This information can be as granular as required. Knowing, for example, that certain segments require a disproportionate amount of post-editing can help future projects: perhaps the source text can be adjusted, or post-editors can be provided with useful reference documents.

Besides gathering data, it is invaluable to proactively seek out feedback from all the key stakeholders. This can include the content creators, post-editors, clients, and project managers. Ask them about their experience with the project and identify what worked and what can be improved.

Post-editing is a new process that is still defining and improving itself. If you are interested in pursuing post-editing and machine translation further, consider reading our e-book which provides more practical information and case studies that can help you and your business.

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Post-editing Machine Translation Best Practices

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Machine translation (MT) is continuously improving by becoming faster, cheaper, and more accurate. Yet it still has some way to go before it achieves parity with human translators. Post-editing machine translation (PEMT) helps combine the best of both worlds: the speed and ability of MT engines to quickly handle large volumes of text, with the skill and sensitivity of trained linguists.

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I've noticed I have a tendency to over-edit, so I've come up with a set of my own rules to follow:

1. Does the segment make sense?
2. Do I really need an equivalent or is it just my own preference?
3. Is the word order really wrong or am I just picky?
4. Focus more on grammar than style.
5. If it ain't broken, don't fix it!

Start with as little changes as possible - try to approach the full postediting as if you were doing the light postediting.