This article will mainly focus on machine translation, detailing its development process, including the characteristics of translation technologies in different stages; elaborating on people's concerns about machine translation and machine translation post - editing (MTPE); analyzing the reasons for the increased use of machine translation and the future outlook of MTPE, covering various aspects from the technological development to the application impact of machine translation.
The Development Process of MT
MT was introduced in the 1950s with Rule-Based Machine Translation (RBMT). This rigid form of MT relied on rules developed by human language experts. Statistical Machine Translation (SMT) came into its infancy in the late 80s as computational power increased. SMT creates statistical models by analyzing large sets of bilingual data to create the desired MT output. SMT would learn from each data set and was an early example of machine learning. For many years SMT remained at the forefront of industry standards regarding MT.
However, in 2016, Google announced the development of their Neural Machine Translation (NMT) system. Since then, the development of NMT exploded and became the MT model of choice across the industry. SMT broke the input sentence into short strings of words, translated them independently and tried to reorder the pieces. NMT, on the other hand, attempts to build a representation of the meaning of the whole input and uses that to produce the output. This helps it produce fluent output that works well between languages with different grammatical structures. In summary? It was a game changer!
Most recently AMT, or Adaptive Machine Translation, joined the family of MT offspring being used by linguists, adding another layer of accuracy to MT output. As a live linguist translates and even uses standard MT engines in crafting their output, AMT learns from the linguist’s work and can provide better suggestions in the future. Like any good partnership, AMT holding hands with a linguist only gets better as the two work together and grow their relationship over time.
Concerns about MT and MTPE
According to an April 2020 study conducted by CSA Research, only 37% of freelance translators believed raw MT output quality was “good.” This opinion was likely influenced by the fact that 81% of those same linguists noted that raw MT output varied significantly from client to client, making even the prospect of working with it unpredictably painstaking.
However, by October 2022, another comprehensive industry study released by Smartcat garnered a response where 55% of the linguists said their experience with AI translation (which includes MT) was “good, very good, or excellent.”
A notable caveat is that linguists who work with lower-resource languages or rare language combinations readily assert that for many of their purposes, MT is still not ready for prime time, and this is simply because robust, reliable engines in many languages and combinations are still being trained. Professional translators, then—those who know and understand the nuances of a “good translation” best—still have valid concerns and varying opinions about what the use of MT and post-editing means for them and their clients, especially with MT being increasingly implemented across industries. These findings emphasize the need for more education and training in order to dispel any myths and to explain the advantages of MT, MTPE and other emerging technologies.
In particular, large LSPs and language technology platforms that include a freelancer “marketplace” seem to be leading the charge in this regard, or at least the conversation. Likewise, then, a “new standard” for correctly handling MT and producing proper MTPE output (and eventually, other AI-driven output) is vital to keeping talented linguists relevant and gainfully employed.
Reasons for the Increase in MT Use and the Future Outlook of MTPE
The increase in the use of MT has been driven by a number of factors. There are just not enough human translators to handle the astounding amount of textual data that is being produced every day, driven by increasing globalization and an avalanche of information of all types being published online. That’s good news! MT has become an indispensable tool to help expedite the delivery of translated content and has gained increasing acceptance thanks to marked advancements made in the output quality of NMT, AMT and AI.
It’s important to keep in mind that MT does a whole lot more than PE. Who knew last year that the cryptic acronym “GPT” would be on everyone’s tongue, or how useful it could become when properly used for many personal and professional purposes? The takeaway is that, even as we specifically discuss MTPE today, future flexibility and continued career development are part of the new normal.
About Glodom
Glodom is a Global Top 100 Language Service Provider. It has accumulated profound professional knowledge in the field of game localization and has reached long-term and stable cooperation with many well-known game companies around the world. Glodom has more than 300 full-time employees, and its service network covers over 40 countries worldwide, supporting more than 150 languages. We always attach equal importance to innovation and service. With advanced technology and rich project management experience, we have successfully helped numerous enterprises achieve their global strategic layouts. Whether in the accuracy of localized translation or the efficiency of multilingual processing, Glodom has always been at the forefront of the industry.
