Hi Pascal Zotto 

Yes its called Language Cloud Edge.

While it is not for the every day sort of user, more information about it can be found here: https://www.rws.com/language-weaver/edge/

Have a good day

Lyds

Hi Lydia Simplicio, well, it would certainly help if the LWE support would get back to people if they ask for further information. Right now, due to lacking information and feedback to my answer/request from support I can't still tell if it is what I'm looking for or not.

Pascal Zotto 

Adding Arnaud Simon  who may be able to offer some advice here.

Thanks Paul ,

I hope to get contacted quite soon …

I also got a private message (I wonder why this information was not posted here) to have a look at Opus-CAT MT which also seems to work with Trados Studio. On a first glance over the information on their homepage and without having to rely on a slow support request this seems to be what I’m looking for.

There are a few more questions about possibly missing information/features to be discussed with devs but the first information (especially on how it's used and how it can be refined) on their page seems quite good and not so cryptic and scarce as on the LWE page .

Br,

Pascal

Pascal Zotto 

Pascal Zotto said:

I also got a private message (I wonder why this information was not posted here) to have a look at Opus-CAT MT

Indeed... that is a  great tool.  I almost mentioned it here but thought you wanted a Language Weaver solution for this.  If by "cloud" you mean anything not on your desktop then Opus may be the best solution for you anyway.  Language Weaver Edge doesn't use the Cloud, but it is still server based even if you had it on-premise for your own use.

 Paul,

I didn't ask for Language Weaver explicitly, I only said that I know and use LW but was looking for an NMT that can be trained with own MTs AND is not hosted in a cloud but on my own server or on local server. I guess you misread that part. ;)

I have an own server on wich I can install whatever I want, so LWE might still be an option, but in order to decide, I would still need quite a lot of information from support or someone using it. I’m also open for any other tool that offers NMT and that can be customized and trained with the own TMS without using any other external online resources but my own online server.

Regarding LWE it might be interesting in some other way (if like LW it can be matched with another language for languages with smaller TMs) by using customizable replace x by y rules (if that feature is even available).

Hi Pascal Zotto ,

Language Weaver Edge is our  Machine Translation solution which can be deployed on premise. It supports all features currently available in the Cloud, including the ability to train language pairs with your own translation memories. Language Pairs that can be trained are called Adaptable Language Pairs: you can manually train them; i.e. control when the training takes place, upload your training data using *.tmx, define your own test data set for evaluation, and fully control deployment. This currently requires a GPU and Linux.

In our next release (8.6 - to be released in October 22), we will support:

• Adaptation on CPU (the GPU will not longer be a requirement - training will be longer, but will be possible). Training on CPU will be possible on both Windows and Linux.
• Auto adaptive Language Pairs: with auto adaptive language pairs, the models are constantly trained with data available, including the uploaded translation memories, the dictionaries and also the user feedback provided (collected through the LW Edge UI). The training happens in the background and there is no need to perform manual operations such as deployment of trained models. In Studio, you only reference the auto-adaptive model once in the Language Pair Mapping and you are good to go.

Feel free to join the Language Weaver Edge section of the community  Language Weaver Edge to get more information. We have a few videos highlighting the User Feedback process - and there is also a recording of a session from Connect 2019 (a bit old but still relevant) on how to best adapt NMT Models using our on-premise solution.

I hope this is helpful and please let me know if you need any more information! 

Hi Pascal Zotto ,

Language Weaver Edge is our  Machine Translation solution which can be deployed on premise. It supports all features currently available in the Cloud, including the ability to train language pairs with your own translation memories. Language Pairs that can be trained are called Adaptable Language Pairs: you can manually train them; i.e. control when the training takes place, upload your training data using *.tmx, define your own test data set for evaluation, and fully control deployment. This currently requires a GPU and Linux.

In our next release (8.6 - to be released in October 22), we will support:

• Adaptation on CPU (the GPU will not longer be a requirement - training will be longer, but will be possible). Training on CPU will be possible on both Windows and Linux.
• Auto adaptive Language Pairs: with auto adaptive language pairs, the models are constantly trained with data available, including the uploaded translation memories, the dictionaries and also the user feedback provided (collected through the LW Edge UI). The training happens in the background and there is no need to perform manual operations such as deployment of trained models. In Studio, you only reference the auto-adaptive model once in the Language Pair Mapping and you are good to go.

Feel free to join the Language Weaver Edge section of the community  Language Weaver Edge to get more information. We have a few videos highlighting the User Feedback process - and there is also a recording of a session from Connect 2019 (a bit old but still relevant) on how to best adapt NMT Models using our on-premise solution.

I hope this is helpful and please let me know if you need any more information! 

Hi Arnaud Simon ,

just a quick question regarding the Auto Adaptive Language Pairs in release 8.6:

Will those who use it in Trados Studio via plugin also be able to provide feedback for the automatic retraining of the models?

Thanks, 

Natalie