Machine Translation involves the use of artificial intelligence to automatically translate text from one language to another without any human assistance. The machine translation used these days goes well beyond the simple word-to-word translation in order to communicate the full meaning of the original language text in the target language.
It also analyzes the text elements in addition to recognizing how the words influence one another. Although the best linguistic software can be used for translation, the use of MT has its own advantages for an enterprise’s content needs.
In machine translation, the words are mechanically substitute from one language to another through the use of translation software. The machine translation process can be described as having two steps;
- The meaning of the source text is decoded
- The source text is then recorded in the target language
However, the process of machine translation is more complex than this as it is not just about replacing the words but also entails making words, phrases, and whole sentences comprehensive in the target language. This is because not all words have an equivalent word in the target language, so using a “suitable” word requires work.
Owing to the complexity of translation tasks, the software used for machine translation is constantly being improved and updated to make translations as accurate as possible. Although machine translation does not involve any human input, it can still be trained by human intervention.
This emerging concept is known as “Adaptive Machine Translation ”, a new technology that allows machine translation systems to learn from correction input by humans. Here, training is not need as the system learns instantly from the editor’s corrections and this is how the MT system works–it can learn specific ways of interpreting things while a translator is performing the translation task and this improves the quality of results over time.
Types of Machine Translation
Although you can use the best linguistic software for your translation needs, MT is the best option if you want to handle large volumes of text. For instance, if you need professional technical translation services for a high volume of content, but can’t find a reliable one due to budget constraints, you can simply opt for MT.
Many translation companies are providing different types of machine translation services.
These are the most common types of machine translation your enterprise can use for the translation of high-volume text:
Statistical Machine Translation (SMT)
SMT builds a statistical model of relationships between text words, phrases, and sentences and then applies this translation model to a second language while converting the same elements to a new language. SMT decides the correspondence between a word from a source language and a word from the target language.
An example of SMT is Google Translate. The machine-learning algorithms in SMT analyze huge amounts of machine translations that already exist and search for statistical patterns. The software then makes an intelligent guess when it translates a new source text.
At present, SMT is used for basic translation, however, one of its most notable disadvantages is that it does not account for context and requires training on millions of words for every language pair.
The syntax-based translation is a subcategory of statistical machine translation that uses grammatical rules to translate syntactic units and analyzes sentences in order to incorporate syntax rules into statistical translation models.
Rule-based Machine Translation (RBMT)
RBMT is the earliest form of MT and translates on the basis of grammatical rules. It works by directing a grammatical examination of the source language into the target language to create the translated sentence.
Language experts and translators are responsible for developing linguistic rules as well as bilingual dictionaries for specific industries or topics. RBMT uses these dictionaries in order to translate content accurately. RBMT works through a two-step procedure:
- The MT software resolves input text into its component parts and creates a transitional representation
- It then converts the representation into the target language using grammar rules as well as dictionaries for reference.
Although there have been considerable advances in machine translation technology using RBMT, it still has a few disadvantages that need to be address. These drawbacks include the need for editing and adding languages by humans manually. Nevertheless, RBMT is useful in situations where a quick understanding of the source text is require.
Neural Machine Translation (NMT)
Neural machine translation uses Artificial Intelligence to learn languages and continuously improve knowledge through the use of neural networks. Working in combination with statistical translation methods, NMT strives to mimic the neural networks in the human brain. One particular advantage of NMT is that it provides a solitary system that can prepare to uncover the source and the target text. As a result, it does not rely on specific systems unlike other types of machine translation systems.
NMT works through the incorporation of training data and depending on the needs of the user, this data can be generic or custom:
- Generic Data:
Generic data is the sum of all data learn from translations performed over time by a Machine Translation Engine (MTE). This data provides a generalized translation tool for many applications, including text, voice, and documents.
- Specialized data:
Specialized or custom data is the training data fed to a machine translation engine in order to build specialization in a subject matter. Such specialized subjects include engineering, design, programming, or any other discipline that has its own specialized glossaries and dictionaries.
Many LSPs are also providing neural machine translation services. You can hire one for bulk translations.
Hybrid Machine Translation (HMT)
A blend of RBMT and SMT, hybrid machine translation leverages a translation memory and is a method that uses multiple machine translation approaches within a single machine translation system. HMT integrates the core existing methods of RBMT and SMT, making up for the deficiencies of the individual MT method.
Along with being a mix of SMT and RBMT, HMT also involves a translation memory, using it to improve the quality of translations. The tool is useful for translators who are trying to avoid translating the same exact sentence twice.
However, HMT still comes with a drawback–the need for human editing and changing. This is the reason why language experts still doubt the translation quality provided by HMT, and work needs to be done to achieve higher-quality translation through this tool. However, for improving the quality of results, MTPE services can also be use.
Bottom Line:
As companies explore international grounds for expansion, the need to provide accurate and timely translations of content has increased. The complexity of this content has led to the adoption of technology and other advanced measures that were not common in the past.If you need technical translation services, you can consider using NMT as it gives quality results. Likewise, other types of MT can be use depending on the nature of the content that requires translation.
