Machine Translation Options for Tranlsators
Thread poster: Kirti Vashee

Kirti Vashee  Identity Verified
United States
Local time: 15:19
Jul 1, 2016

This is yet another post triggered by conversations in Rio at the ABRATES conference in early June. As I mentioned in my initial conference post, the level of interest in MT was unusually high and there seemed to be real and serious interest in finding out ways to engage with MT beyond just the brute force and numbing corrective work that is typical of most PEMT projects with LSPs.

MT has been around in some form for decades, and I am aware that there have always been a few translators who found some use for the technology. But since I was asked by so many translators about the options available today I thought it would be useful to write about it.

The situation today for many translators is to work with low quality MT output produced by LSP/enterprise MT practitioners with very limited MT engine development experience, and typically they have no say in how the MT engines evolve, since they are so far down the production line. Sometimes they may work with expert developed MT systems where there is some limited feedback and steering possible, but generally the PEMT experience involves:

1. Static MT engines that are developed offline somewhere, and that may have periodic if any updates at all to improve the engine very marginally if at all.
2. Post-editors work on batches of MT output and provide periodic feedback to MT developers.

This is beginning to change in the very recent past with innovative new MT technology that is described as Adaptive Interactive Dynamic Learning MT (quite a mouthful). The most visible and elegant implementation of this approach is from a startup called Lilt. This allows the translator-editor to tune and adjust the engine dynamically in real time, and thus make all subsequent MT predictions more intelligent, informed and accurate. This kind of an MT implementation is something that has to be cloud based to allow the core engine to be updated in real time. Additionally, when used in workgroups, this technology can also leverage the individual efforts of translators by spreading the benefit of a more intelligent MT engine with the whole team of translators. Each user benefits from the previous edits and corrective actions of every other translator-editor and user as this case study shows. This allows a team to build a kind of communal edit synergy in real time and theoretically allows 1+1+1 to be 5 or 7 or even higher. The user interface is also much more translator friendly and is INTERACTIVE so it changes moment to moment as the editor makes changes. Thus you have a real-time virtuous cycle which is in essence an intelligent learning TM Engine that learns with every single corrective interaction.

CSA tells us that the SDL Language Cloud also has similar abilities but my initial perusal suggests it is definitely less real-time, and less dynamic than Lilt i.e. it is not updating phrase tables in real time. There are several little videos that explain it and superficially it looks like an equivalent but I am going to bet it is not yet at this point in time anyway.

So for a translator who wants to get hands on experience with MT and understand the technology better, what are the options? The following chart provides a very rough overview of the options available ranked by my estimation of the best options to learn valuable new skills. The simplest MT option for an individual translator has always been a desktop RbMT system like Systran or ProMT, and it still is a viable option for many, especially with Romance languages. But there has never been much that could be done to tune these older systems beyond building dictionaries, a skill that in my opinion will have low value in the future.

You can read this with more graphics at the Empty Pages blog -- should come up in search. I will place the link below but I am not sure if it will go through.


LegalTransform  Identity Verified
United States
Local time: 18:19
Member (2002)
Spanish to English
+ ...
Jul 1, 2016

[Edited at 2016-07-01 17:37 GMT]


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Machine Translation Options for Tranlsators

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