Businesses have adopted deep adaptive machine translation to help reduce translation costs. Compared to human translation, this new technology has helped businesses save on time and money.
Usually, human translators would take tons of time to translate, derailing the performance of a company. However, things are easier when deep adaptive machine translation is applied. It’s not only fast but also top technology growing day by day.
Currently, the machine translation market is valued at USD 130-USD 400, a sign of a promising technological advancement. This also tells you that this AI sector is snowballing and is here to disrupt the world. So, it’s unclear what exactly is a deep adaptive machine translation.
It’s automated text translation from one language to another with the help of computer software. With deep adaptive machine translation in the picture, it’s possible to translate whole texts or use human brains as human translators for post-editing.
Types of Machine Translation
Machine translation has evolved over the years to streamline the needs of businesses. The typical kinds include rule-based, statistical, and neural machine translation.
Let’s look at each component below:
1. Rule-Based Machine Translation
This kind of machine learning works behind the grammatical and syntax rules of a language. Typically, the rule-based machine translation connects the language source to its target language.
As a result, the results are produced based on the language rules. Businesses rely on rule-based machine translation for the high-quality translation since the linguists add terminology, making the translation easier. However, rule-based machine translation is somehow challenging as it needs heavy proofreading for it to be effective.
2. Statistical Machine Translation
It applies the translated texts and statistical models alongside algorithms to perform translations. An excellent example of a statistical machine translation is Google Translate.
Statistical machine translation is based on multilingual and needs 2 million words to fit the engine of a given domain. On top of this, it establishes the relationship between the source language and one from the target language.
Statistical machine translation works best in technical, medical, and financial fields thanks to the availability to run it online. Businesses using statistical machine translation experience challenges as they are designed in real-time to follow rules that make corporations outdated. It also never factors the context of the text so that the translations might be erroneous at times.
3. Neural Machine Translation
Neural machine translation is the latest deep adaptive machine translation form. It involves the use of deep neural networks that mimic human brains to perform translations. Usually, NMT is categorized into an encoder and a decoder.
The former spells out the input sentence and comes up with the best translation, while the latter does the actual translation. Neural machine translation produces fluent and accurate translations compared to other machine translation approaches.
However, its capability to translate rare words and technology is outdone by the previous machine translation systems. Today, businesses have a challenge in using the NMT due to its expensive training models. As such, organizations have to spend on the high training costs involved in NMT.
Custom vs. Generic Translation
Generic machine translation is designed for general purposes. Therefore, it never uses domain-specific data. Some common examples of generic machine translation include Google Translate and Microsoft Translator.
On the other hand, a custom translation has a design for a specific domain and thus generates quality translations. You’d find custom machine translations highly-priced and also producing accurate translations.
Businesses need to perform regular retraining on both generic and custom machine translations to enhance the translation quality. Deep adaptive machine translation comes in handy in real-time system updates. The system is thus learning and improving thanks to the regular edits on the content.
How Businesses Apply Deep Adaptive Machine Translation
In the internet age, businesses are taking advantage of artificial intelligence to improve processes. Most importantly, leveraging the use of machine translation in the workplace streamlines processes.
Additionally, it saves time and money, especially when large volumes of data are used. Usually, a combination of human translation with machine translations yields good results.
Here’s How Machine Translations Can Be Used
When companies need to translate large volumes of data with a short turnaround time, human translation doesn’t help. This is where machine translations come in real-time. However, human translators might help with post-editing to fine-tune everything.
Businesses that don’t require human post-editing, referred to as raw machine translation, can get suitable translations with any focus on accuracy. Deep adaptive machine translation is applicable when translating customer reviews, news monitoring, internal documents, and product descriptions.
Pros of Deep Adaptive Machine Translation
Over time, deep adaptive machine translation gives numerous benefits to translation workflows. First, the whole process is fast, making operations run normally. Deep adaptive machine translation has the capability to translate multiple languages at a go. This helps reduce the manpower, hence saving on the business resources.
Machine translation eases the workload in such a way that the process enables translators to focus on other detailed aspects of the translation. As a result, no time is wasted waiting around for the translation process to end. Besides, machine translation is getting better, thanks to the latest technology. Thus, eliminate the need to invest in post-editing processes.
Cons of Deep Adaptive Machine Translation
As much as deep adaptive machine learning has benefits, it also poses a sheer number of challenges. For instance, machine translation sometimes produces poor quality translations. Such an occurrence is caused by grammar rules in RBMT or failure to consider the context for SMT.
Another drawback that deep adaptive machine learning poses is the lack of consideration of the translation context. Consequently, the translations might be erroneous. So, machine translations are not reliable in everything.
Deep adaptive machine translation is a solution to businesses today. Apart from saving on time, this AI helps with streamlining business processes. When a company needs to translate vast volumes of text, human translations will fail.
Deep adaptive machine translation comes in handy in translating the data in real-time. Essentially, businesses need to adopt this technology for excellent operations.