There are numerous speculations about what the future holds concerning machine translation. Some are optimistic, while others are skeptical. This article highlights a couple of expected trends.
What a time to be alive! The year 2017 saw the launching of two artificial intelligence systems possessing the ability to self-learn any dialect or language on earth. Some industry experts have even predicted that the translations industry is likely to be valued at 1.5 billion U.S.D by the year 2024, in addition to the capability of translating hundreds of languages right to the particular customers’ tastes. What’s more, translators in various highly advanced speech combinations and language are needed. This will offer tremendous opportunities within this industry. Both humans and AI systems will be on-demand in highly specialized solutions in language & translations, thus fulfilling the ever-increasing global demand. The following are some things you ought to know about future trends in machine translation technology:
The Technology Will Not Render Human Translators Obsolete
Before going deeper into machine translation mechanisms, it is imperative to know that these developments are not geared towards getting rid of professional human translators. South Korea serves a good example in which there are a vast number of robots, yet unemployment levels are extremely low. Increased automation normally results in more human jobs that require higher cognitive capabilities. Furthermore, translation is a highly lucrative profession which is aligned with the increasing popularity of online work, especially among the millennials. The mantra driving this novel innovation is coexistence and reducing the burden of working on excessive amounts of text by human translators.
Future Forecasts for Machine Translation
Looking at the growth in worldwide machine translation, change within markets, in addition to the competitive intelligence arena, four emerging innovations shall bear tremendous influence on the entire machine translation industry. These are as follows:
- Rule-based machine translation
- Statistical machine translation
- Hybrid machine translation
- Neural machine translation
Statistical Machine Translation
It utilizes algorithms to learn about a certain language or languages. It utilizes the algorithm to help in translating a phrase it has never come across before. This technology is highly efficient for documents that are based on a given topic. Some of its merits include a vast array of pre-existing algorithms as well as platforms. This makes the application less costly and a lot faster. Another advantage is that very little space is needed- it does not need a dedicated server. All its training is undertaken within the CPU-based servers. What’s more, it is extremely easy to deploy. The process of decoding information is amazingly quick and has much room for translation memory.
Up until the year 2016, most common systems used the statistical model. Nevertheless, one disadvantage of statistical machine translation technologies is that they can only function within specific contexts, and are not good at slang terms or idioms. Additionally, they work better within closed-form speech, but are relatively ineffective in terms of rearranging syntaxes; E.g., when undertaking the translation from English to Japanese or from English to German.
Neural Machine Translation
This is the current industry standard implemented by all the major providers such as Microsoft, DeepL, Google, and IBM. Even at translate.com, this is the system that is used. It provides users with the option to undertake a translation of entire files using machine-translation, or to post-edit the content that has already been translated by the machine through a human translator.
This technology simplifies the task of post-editing since it mainly enhances fluency. Neural systems capitalize more on multi-layered statistical translation, which undertakes data processing. It also has nodes that relay information together with ascertaining accuracy. This system is referred to as neural because it functions similarly to neurons within the human body.
Some features within neural networks assist it in understanding word similarities, analysis of whole sentences, in addition to evaluation of how fluent the sentences in target languages are. It achieves this goal through painstakingly evaluating a few phrases at a time. Neural machine translation comprises one of the numerous amazing ways to stimulate fluently and naturally sounding translations. Most of its outcomes are astonishingly accurate in terms of evaluating and processing human speech. This is because of a rich morphology in the different calculations, the gradients utilized and weights; not forgetting the attention models that have been utilized even between unrelated dialects or languages.
Rule-Based Machine Translation
As the earliest machine translation technology, it dates back to the 1950s. If fundamentally makes use of grammatical rules, processing rules, software, & lexicon. It translates text by using specific pattern-matching rules. A typical feature of this technology is that it allows a system to avoid matching unsuccessful rules. One key strength of rule-based translation is the capacity to thoroughly analyze a language both at the syntactic level as well as the semantic level. One demerit is that the numerous amount of rules governing each language might eventually cause contradictions with each other.
Hybrid Machine Translation
Hybrid machine translation employs two or more translation techniques simultaneously. Various types of machine translation exist. One common approach is the combination of statistical engines with rule-based approaches. These normally occur at the pre- and post-processing levels. Hybrid translation provides a higher flexibility level, precision, as well as control. By design, it was aimed at attaining the best of data-driven machine translation and rule-based machine translation.
In conclusion, machine translation does not steer towards the replacement of professional human services. On the contrary, it aims to boost the precision and pace of the translation exercise. It opens up a vast market for speedy and on-demand translation assistance, especially when extremely large text and data volumes are supposed to be translated within a limited timeframe. Editors can also use the technology to correct and proofread text quickly. All these will assist businesses, organizations, or even individuals to achieve their objectives. For business, this means a bolstered connection with the locals, hence more sales.