Machine translation (MT) is ever-present in the translation industry. The technology is being used to shorten project timelines for language service providers (LSPs) and reduce costs for clients as they localize content around the globe. At this point it has become obvious, however, that MT cannot be used as a sole means of translation due to issues with its accuracy. Applications such as Google Translate have proven to be a successful way to translate documents or segments of words generally, but are not able to capture specific vernacular traits that human translators are capable of deciphering.
MT is a relatively new technology, having origins in the early 20th century and not being considered a completely realized tool until advancements in the field in the 1980 and 1990s. George Artsrouni and Petr Smirnov-Troyanskii worked individually on ways to create MT in the 1930s, with Smirnov-Troyanskii laying the groundwork for what was needed in an MT system. Namely, Smirnov-Troyanskii suggested the system would require an editor familiar with a source language to convert words to base forms before sending them to a machine to turn them into equivalent forms in the target language. After this, another editor familiar with the target language would edit the machine translations.
Attempts to create a successful MT system continued into the 1950s and 1960s with the advent of computers and, in 1954, IBM partnered with Georgetown University in an MT a demonstration widely circulated in the media and considered to be quite a feat at the time. Although advancements continued in the field, the thought of translating languages digitally seemed less and less like a possibility and more like a flawed experiment.
Hopes were diminished to the point that the United States government created an advisory committee on the technology, ALPAC, that released an unflattering report of MT’s progress, saying research wasn’t progressing at it should. The report put a stop to MT research until important developments resurfaced in the 1980s.
MT research and development boomed in the 1990s and 2000s as we moved into the digital age. Neural machine translation (NMT) is making waves in the industry today.
MT serves a number of functions for companies within different verticals, including legal, life sciences, manufacturing, information technology, finance and consumer products. Businesses attempting to localize content have had success using MT to reduce costs and more efficiently convey global messages.
MT has many features that help to expedite translation projects, including:
- Language Identification. A function that can quickly go through a large number of documents and decipher what languages the text in these documents is written in
- Keyword search. This allows users to search for certain terms that come up often in documents.
- Ocular Character Recognition. OCR is a technology that allows for the recognition of printed characters using digital technology
Right now, MT allows users to get the gist of a translation without having to use a human translator to do so. There are instances in which the new technology can be a boon for a business, but, at the same time, its limitations outweigh its abilities if used the wrong way.
Very recently Google and Microsoft started using NMT with their translation systems, a relatively new technology that is on pace to replace phrase-based machine translation (PBMT). NMT employs artificial intelligence that can understand entire sentences or ideas using neural networks, while PBMT can only decipher words, or segments of a sentence, at a time.
When MT works for business
Before using solo MT — without any linguistic editing — for business, users should ask themselves a handful of questions to see if the solution will be a successful one. The decision of whether to use MT should rely heavily on the following:
- Is quality an important part of this project?
- Is a quick turnaround time necessary?
- Will this be distributed externally?
- Is cost-effectiveness a priority?
Right away, companies should identify what kind of project they need to complete when deciding whether to use MT. If quality of utmost importance, it will be impossible to use solo MT to receive accurate translations without the help of a human interpreter.
At the same time, if a law firm needs to quickly identify a large number of documents and find out which need to be translated for trial, using MT as a tool can be very helpful. A strong language identification tool can act as a time-saving and cost-effective feature for assignments that require going through a large amount of foreign language text before translation.
This feature can also be helpful for those facing tough deadlines, as MT can go through text much faster than an interpreter to sort what needs translation and what doesn’t. But if a translation project is going to be distributed externally or to clients, it is imperative that one uses a human translator or a combination of machine translation and post-editing.
If cost is an issue, companies can leverage certain MT tools to mitigate their expenses. For language identification or in order to basically understand foreign language documents, MT is far cheaper than the use of a certified LSP.
When MT doesn’t work
The biggest setback of MT is its inability to pick up on linguistic nuance and metaphors as humans can. This truism was proven by AlSukhni, Al-Kabi and Alsmadi in their study on using Google and Bing Translate to translate passages from the Quran.
For projects that necessitate quality, MT cannot be used as the only translation tool for businesses. Projects that should not rely only on MT include:
- Those in highly regulated fields such as medical device
- Projects that will be distributed for external use
- Projects that require the translation of nuanced, complicated texts
In Ryoko, Hirono, MJ and Takahiro’s article examining the usability of MT among nurses in Japan, it was discovered that respondents found MT was “not useful enough” when deciphering medical texts in a foreign language. The study also emphasized the fact that a stronger knowledge of technical terms in other languages made for a better experience using MT.
Know your audience
MT is an extremely valuable asset to businesses that know how to use it properly. MT tools used in a secure environment can be strong assets for those in any vertical. Maybe most importantly, businesses interested in MT need to know how they want to use the technology and what the scope of their project is to employ the resource effectively.
MT should never be used as the only resource for translating documents unless they will stay inside of a company. Even still, companies should always be aware of the fact that mistakes are likely to occur when only using MT.