RoyMarie wrote:
https://www.modernmt.com/translate/ allows you to do this with cut & Paste
But it also allows you to add your translation memory and improve. This is described in detail in some blog posts on the ModernMT website.
ModernMT: An MT system designed for the translator
The modern-era translator work experience often involves the use of translation memory (TM). Since it improves translator productivity when the TM is related and relevant to any new translation work that a translator may undertake.
MT is used less often by professional translators in general because of the following reasons:
Generic MT output is of limited value.
Most MT systems have a very limited ability to customize and adapt the generic system to the translator's area of focus and specialization.
The typically complex customization process often requires that translators have skills that are typically outside of the scope of translator education.
A large volume of data (more than most translators can summon) is needed to have any impact on generic engine performance. This also makes it difficult for most LSPs to also customize an MT engine as most of the MT models in the market require tens of thousands or more segments of training data to have an impact.
The very slow rate of improvement of most MT engines means that translators must correct the same errors over and over again. The whole improvement process can itself be a significant engineering undertaking and task.
The open admission of MT use is often penalized with lower compensation and lower word rates.
The inability to control and improve MT output predictably means that translators themselves have a higher level of uncertainty about the utility of MT given project deadlines and thus fallback to traditional approaches.
For MT to be useful to a translator it needs the following attributes:
Tight integration with CAT tools that are the primary work environment for translators.
Easy to start using without geeky technical preparation and ML-customization-related work.
Rapid learning of new material and incorporation of any corrective feedback so that the MT system is continuously improving, by the day or even the hour.
The ability to handle project-related terminology with ease.
Keep translator data private and secure.
ModernMT is an MT system that is designed to adapt to the unique needs and focus of an individual translator in essentially the same way that TM does. In many ways, it is a next-generation TM technology that has predictive capabilities.
ModernMT is a translator-focused MT architecture that has been built and refined over a decade with active feedback and learning from a close collaboration between translators and MT researchers.
ModernMT has been used intensively in all the production translation work done by Translated Srl for over 15 years and was a functioning human-in-the-loop (HITL) machine-learning system before the term was even coined.
ModernMT is perhaps the only MT system that was designed by translators for translators rather than by pure technologists working in isolation with data and algorithms.
This long-term engagement with translators and continuous feedback-driven improvement process also results in creating a superior training data set over the years. This superior training data enables users to have an efficiency and quality advantage that is not easily or rapidly replicated.
This is also the reason why ModernMT does so consistently well in third-party MT system comparisons, even though evaluators do not always measure its performance optimally. ModernMT simply has more informed translator feedback built into the system.
The following is a summary of features in a well-designed Human-in-the-loop (HITL) system, such as the one underlying ModernMT:
Easy setup and startup process for any and every new adapted MT system that allows even a single translator to build hundreds of domain-focused systems.
Responsive: Active and continuous corrective feedback is rapidly processed so that translators can see the impact of corrections in real-time and the system improves continuously without requiring the translator to set up a data collection and re-training workflow.
An MT system that is continuously training and improving with this feedback (by the minute, day, week, month). Small volumes of correction can improve the ongoing MT performance.
Tightly integrated into the foundational CAT tools used by translators who provide the most valuable system-enhancing feedback.
Different engagement and interaction with MT than a typical PEMT experience.
I recently interviewed several translators who are active ModernMT users and have summarized their comments (+ve and -ve) below. Their comments contain pearls of wisdom and anecdotal experience that may be useful to other translators who are still considering MT.
Subject focus by those who shared their usage patterns with me included accounting/finance, legal contracts, complex engineering equipment-related content, marketing content, product manuals, newsletters & press releases, medical information for patients, and even Buddhism & meditation-related content. Many simply provided categories like Law, Medical, and Technical.
The extent of use: Used in the large majority of work they did, except for DTP or very specialized domain content that they did on an infrequent basis. Many said that the real benefits start to accrue after one builds up some TM and that over time ModernMT learns to support your primary workload.
How is MT engaged: CAT Tools (Trados), ModernMT GUI, and MateCat
Why: Work volumes and turnaround requirements and high-level data privacy and availability of TM to enable adaptation.
Competitive systems evaluated: Google, DeepL, Systran, Kantan
“I have used DeepL and Google, which can be very useful, although I still find ModernMT to have better overall accuracy compared to both of them. DeepL is a good alternative for comparing output, although it is much less consistent compared to ModernMT when working on large documents e.g. consistency of terminology etc.”
“I can tell you this with peace in my mind that nothing can replace ModernMT. ModernMT has magic that no one can describe. It really adapts to contexts and stores my previous translations and yields me 99% accurate translations.”
Improvements needed: Word case handling for acronyms and abbreviations, handling of short phrases and titles, the lack of persistence of terms across documents, better format preservation, and better dashboard.
Desirable New features: Glossary and terminology handling, a dashboard on data and usage, more robust punctuation handling, real-time predictive capabilities, and pre-translation quality assessment.
”I consider MT as a developed tool, making our job easier, but not a tool that gives the final product. It is like an advanced medical tool used by a surgeon during surgery, which helps the surgeon to make fewer mistakes, to save time, and to save the life of the patient.”
A strong positive comment by a translator who also provided constructive areas of improvement content: “I have noticed incredible improvement [in the MT quality] as if it is my roommate who was trying to get to know me and my translation style and way of constructing the sentences.”
Many were surprised to find out that glossary and terminology terms are best introduced to ModernMT in sentence form rather than as short phrases as the context and variants shown in sentence context ensure a faster pick-up and learning.
Several expressed surprise that more translators did not realize the cost/benefit and productivity advantages to be gained by using a responsive MT system like ModernMT and also mentioned that success with ModernMT required investment in one or all of the following: time, corrective feedback, and personal TM but can yield surprisingly good results in as little as a few weeks.
To close this post I include a podcast done with ProZ last year, that I got very positive feedback on, from many translators.
Conversation with Paul Urwin of Proz on MT
Paul talks with machine translation expert Kirti Vashee about interactive-adaptive MT, linguistic assets, freelance positioning, how to add value in explosive content situations, e-commerce translation, and the Starship Enterprise.
Login to reply/comment 
