Machine translation (MT), to put it simply, is the process of translating text automatically using a computer software from one source language to another. Since the 1950s, machine translation has had a long and fascinating history.
The technology has improved over time into a workable option for quick and precise translations. Machine translation entered the mainstream thanks to developments in Artificial Intelligence (AI), Natural Language Processing (NLP), and computing power.
A Few Advantages of Machine Translation (MT)
An essential instrument in the translation process is machine translation. It may be used independently or in conjunction with post-editing by people. MT provides your translation workflows with three main advantages:
Speedy Translation Speed
For large-scale translation projects, machine translation can translate millions of words. As more text is translated, MT uses AI to get smarter. Additionally, to organize and tag high-volume content, MT can collaborate with a Translation Management System (TMS). This will keep you organized when you need to swiftly translate text into several languages.
Excellent Choice of Languages
The majority of significant machine translation services can translate between 50 and 100 languages. These applications are strong enough to translate numerous languages simultaneously so you may release updated documentation and items worldwide. Language pairs like English to French or English to Spanish are good candidates for MT.
MT reduces translation delivery times and costs even when human translators are required for post-editing. MT handles the initial labor-intensive tasks by generating simple but helpful translations that a human translator can then polish and improve. In this manner, the content can be successfully localized and the end copies will follow more closely to the text’s original intent.
Machine Translation Types
Rule-based Machine Translation (RBMT), Statistical Machine Translation (SMT), Hybrid Machine Translation (HMT), and Neural Machine Translation (NMT) are the four types of machine translation. Here is the description of each kind:
Rule-based Machine Translation (RBMT)
The earliest type of machine translation, RBMT, translates text based on grammatical rules. Since RBMT was created, there have been substantial improvements in machine translation technology as a whole, therefore RBMT has some drawbacks. These drawbacks include the requirement for extensive manual post-editing and language addition. Despite its poor translation quality, RBMT is helpful in straightforward scenarios when a quick comprehension of meaning suffices.
Statistical Machine Translation (SMT)
Building a statistical model of the connections between text words, phrases, and sentences is how SMT functions. The identical pieces are then translated into a second language using this translation approach. SMT differs from RBMT in some ways yet still has many of the same issues.
Hybrid Machine Translation (HMT)
A combination of RBMT and SMT is HMT. HMT uses a translation memory, which greatly improves the quality of its performance. The major disadvantage of HMT, however, is the requirement for human editing.
Neural Machine Translation (NMT)
NMT uses artificial intelligence to continuously learn new languages and expand its linguistic understanding. It makes an effort to emulate the neural networks found in the human brain in this way. Compared to the other methods of AI translation, NMT is more accurate. It is simpler to add languages and translate material with NMT. NMT is quickly replacing other translation methods as the industry standard since it offers superior translations.
Training data are incorporated into NMT to operate. The data may be general or specific, depending on the user’s requirements. Generic Data is the entirety of all information the machine translation engine has learned through translations carried out over time (MTE). This information results in a generalized translation tool that may be used for text, speech, and document applications.
Meanwhile, Customized or Specialized Data is the training data for a machine translation engine that helps it become more knowledgeable about a particular subject. Engineering, design, programming, and any other subjects with specialized glossaries and dictionaries are among the topics covered.
Machine Translation Considerations
When selecting an MT engine for your project, you should take into account the following factors:
- Budget: Although NMT training is occasionally more expensive than SMT training, the improvement in translation quality can help to offset the cost.
- Industry: NMT’s sophisticated processing is necessary for several businesses that translate complicated and technical terminology.
- Language Pairs: SMT performs well with specific language combinations. For instance, Latin-based languages with comparable syntax and grammatical norms are the ones that lend themselves to machine translation the best.
- Quantity of Content: NMT is not suitable for small projects because it needs a lot of source material to process and learn from.
Internal vs. Customer-Facing Content: The most complex combination of machine translation and human post-editing by skillful translators is required for customer-facing content, such as sales or marketing materials that represent brand quality. Basic MT can be used to translate employee communications or corporate documentation when time and money are constraints.
What is the best machine translation engine?
Google, Amazon, and Microsoft are well-known IT companies that employ NMT to power their machine translation engines (MTEs). It is crucial to remember that engines are always learning and developing while comparing various engines. Learn more about the best machine translation engines by reading on.
The first MT engine to use neural language processing and machine learning from repeated use was Google Translate. Based on usage, language support, and search integration, it is typically regarded as one of the top machine translation engines.
Microsoft Translator works with programs like Skype and Microsoft Office. Instant translation access is made available by this capability in documents and compatible software.
IBM’s Watson Language Translator
It works together with IBM Watson Studio and IBM Watson Data. These tools support the creation of AI models and data management.
A small German business created the independent MT engine DeepL Translate. DeepL offers accurate and nuanced translations as a result of the company’s unique neural AI. DeepL has had a sharp growth in usage globally in recent years.