Custom Machine translation, a new revolution on the way?

Fi2 n Co wrote:

In the translation race one word has to stand out: quality.

Indeed having terminology adapted to an end client’s need is ever more important. That’s is why many LSPs are implementing quality strategies involving steps such as:
_Managing tailor made glossaries
_Managing tailor made Translation Memories
_Affecting the same team of translators to this client
_Having fluent editors capable of giving life to the text
This is without mentioning efforts in the DTP field.

Yet in this quality process there is a new emerging actor: Custom Machine Translation
It is a very different take on what we are generally acquainted with regarding machine translation. Rather than being a way to replace the translator, CMT (Custom Machine translation) is starting a small revolution of its own. Allow me to explain:
When Translation Memory was invented, it gave the translator the opportunity to work with CAT tools that would help work faster but also improve quality by allowing to translate in the same fashion even relying on different translators.

CMT is entering the scene with a similar approach, how?

Imagine that an LSP will start working for XYZ Soft Drinks. They will come with their TMs and glossaries negotiate a massive translation partnership for two years.
The LSP will start building a team of translators and set up the terminology assets (TMs and glossaries). Now they can also set up a CMT: create and train specifically a machine translation that will be solely used to translate XYZ Soft Drinks material. They will do this using the provided TMs and Glossaries. The suggestions given by this CMT will then adhere to the same style used before when translating for this client.
The translation team has now 3 types of suggestions available to get to work:
_XYZ Soft Drinks TM
_ XYZ Soft Drinks Glossary
_And, XYZ Soft Drinks CMT
As the team translates, they get good information to provide quality translation faster.
After each project, the final versions can be used to improve the CMT suggestions.
For the translator, it will likely mean that he’d get his suggestions from glossary and TM as usual and below a 70% match threshold the translator would get suggestions from the CMT (as with regular MT).
Indeed, as it was the case when TM started to be used CMT is improving the coherence between translators for style and terminology. It also can improve tremendously the speed at which the work is done. And it will prove relevant as a source of tailored suggestions.

What is your take on this subject?

There are a few CMT providers out there, the one that stands out to me (for user friendly and effective) is Kantan MT.
If you want to know more about it, you can check their website here: https://kantanmt.com
If you are an LSP and would like training on the topic, check this training page: https://www.proz.com/translator-training/course/16573-give_extra_power_to_your_tms_with_kantan_mt

Hope you'll all find this topic interesting.

My bests 😊

Michael Beijer wrote:
GT4T and RYS both contain a very clever feature that allows you to fix MT results with your own glossary. This basically means that you can start with the already very good output of, say, DeepL, and if a particular term is consistently wrong, you just add its preferred replacement to your special glossary (called ‘Simple Glossary’ in GT4T, and ‘Search-and-replace list’ in the RYS plugin). It's fast and easy and extremely useful.

Michael Beijer wrote:

Thanks for the interesting post!

If you ask me, the two best custom MT systems currently available and relevant to freelancers are:

(1) GT4T (https://gt4t.net/en/quick-tasks/ ) +
(2) ‘RyS Translation Workflow Automation Package 2018’ (https://appstore.sdl.com/language/app/rys-translation-workflow-automation-package-2018/822/ ),

combined with:

DeepL and/or
Google Translate (neural) and/or
Microsoft Translator (neural)

GT4T and RYS both contain a very clever feature that allows you to fix MT results with your own glossary. This basically means that you can start with the already very good output of, say, DeepL, and if a particular term is consistently wrong, you just add its preferred replacement to your special glossary (called ‘Simple Glossary’ in GT4T, and ‘Search-and-replace list’ in the RYS plugin). It's fast and easy and extremely useful.

Michael





[Edited at 2018-11-06 16:52 GMT]

[Edited at 2018-11-06 16:55 GMT]

Kay-Viktor Stegemann wrote:

I don't know this tool but it sounds to me as if it could work better into English than into other languages, since grammar in English is so simple. Search and replace won't work as easily when you work into German (and probably most other languages), since you cannot simply replace a term with another, without considering case, tense, mode, and so on. DeepL is rather impressive for German because it gets the grammar right (well, often), as opposed to most other MT stuff, but do the tools GT4T and RYS also get the grammar right when replacing one term with another?

Fi2 n Co wrote:



For results, MT engines will depend on:

1/how clever is the programming. The type of MT can create diffrences on how it performs.
The industry is now moving towards Transformer models. To keep it simple it's a preprocessing by a Statistical model and a full processing by a Neural MT. This is what gives best results at the moment.
2/The amount and the quality of data used to train the engine. So for the same engine, different results will be generated depending on the quality and quantity of data that each language pair received. This is the reason why some MTs are known to be better at legal field, another at EU translations etc.

Kay-Viktor Stegemann wrote:

I did not talk about the MT, I did talk about the tools Michael mentioned, that work on top of an MT system. And I would like to know if these tools are able to recognize and apply complex grammar rules in the course of their search and replace functions or not. ]

Kay-Viktor Stegemann wrote:



I don't know this tool but it sounds to me as if it could work better into English than into other languages, since grammar in English is so simple. Search and replace won't work as easily when you work into German (and probably most other languages), since you cannot simply replace a term with another, without considering case, tense, mode, and so on. DeepL is rather impressive for German because it gets the grammar right (well, often), as opposed to most other MT stuff, but do the tools GT4T and RYS also get the grammar right when replacing one term with another?

Michael Beijer wrote:

DeepL returns its translation, and the program simply looks for any word in the MT output that matches the words in your list and then replaces it with the one in your list. If it's a noun, as is usually the case in my case, the replacement usually won't affect the overall quality of the segment.

Fi2 n Co wrote:

That's good for freelancers indeed.

Daniel Frisano wrote:
A simple yes/no question to all MT fans: Would you read a novel or an article written by a machine?