As one observes the continuing evolution of MT use in the professional translation industry, we see that we have reached a point where we have some useful insights about producing successful outcomes in our use of MT. From my perspective as a long-term observer and expert analyst of enterprise MT use, some of these include:
- Adaptation and customization of a generic MT engine done with expertise generally produces a better outcome than simply using a generic public MT system.
- Working with enhanced baseline engines built by experts is likely to produce better outcomes than dabbling with Open Source options with limited expertise. While it has gotten easier to produce MT systems with open-source platforms, real expertise requires long-term exposure and repeated experimentation.
- The algorithms underlying Neural MT have become largely commoditized and there is little advantage gained by jumping from one NMT platform to another.
- More data is ONLY better if it is clean, relevant, and applicable to the enterprise use case in focus. It can be said today that (training) data often matters more than the algorithms used, but data quality and organization is a critical factor for creating successful outcomes.
- A large majority of translators still view MT with great skepticism and see it as marginally useful, mostly because of repeated exposure to incompetently deployed MT systems that are used to reduce translator compensation. Getting active and enthusiastic translator buy-in continues to be a challenge for most MT developers and getting this approval is a clear indicator of superior expertise.
- Attempts to compare different MT systems are largely unsuccessful or misleading, as they are typically based on irrelevant test data or draw conclusions based on very small samples.
- A large number of enterprise use cases are limited by scarce training data resources and thus adaptation and customization attempts have limited success.
- A close and symbiotic relationship between a relevant translation memory and MT, even at the translator UX level
- An MT system that is constantly updated and can potentially improve with every single interaction and unit of corrective feedback
- Immediate project startup possibilities as no batch MT training process is necessary
- Translator control over all steering data used in a project means very straightforward control over terminology and term consistency, mirroring the latest TMs and linguistic preferences
- Corrective feedback given to the MT system is dynamic and continuous and can have an immediate impact on the next sentence produced by the MT system
- One of very few MT systems available today that can provide a context-sensitive translation
- Measurable and palpable reduction in post-editing effort and translator UX compared to other MT platforms
- Continuing free access to the CAT tool needed to integrate MT with TM, and interact proactively with MT with the option to use other highly regarded CAT tools if needed.
In response to a question on data privacy, Davide Caroselli, VP of Product, ModernMT responded: "Any content sent to ModernMT, whether a “TMX” memory or an MTPE correction from a professional translator, is saved in the user’s private data area. In fact, only you will be able to access your resources and make ModernMT adjust to them; in no way will another user be able to utilize that same inventory for his/her system, nor will ModernMT itself be able to use those contents, other than to exclusively offer your personalized translation service.
In addition, ModernMT uses state-of-the-art encryption technologies to provide its cloud services. Our data centers, employee processes and office operations are ISO 27001:2013 certified."
"ModernMT is currently our favorite MT engine, especially in patent translations and in the Life Science sector, because it proves reliable, efficient, qualitatively better than its competitors, easily customizable and advantageous in terms of cost."
Domenico Lombardini, CEO ASTW
It is neither. Language is a means of communication and an information-sharing protocol that employs sounds, symbols, and gestures. Language can sometimes use technology to enable amplification, extend the reach of messages, and accelerate information and knowledge sharing. Language can create a culture when shared with(in) a group and used with well-understood protocols and norms. Intercultural communication can also mean cross species, e.g., when communicating with dogs and horses.