Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation (MAHT) or interactive translation) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.
On a basic level, MT performs simple substitution of words in one language for words in another, but that alone usually cannot produce a good translation of a text because recognition of whole phrases and their closest counterparts in the target language is needed. Solving this problem with corpus statistical, and neural techniques is a rapidly growing field that is leading to better translations, handling differences in linguistic typology, translation of idioms, and the isolation of anomalies.
Current machine translation software often allows for customization by domain or profession (such as weather reports), improving output by limiting the scope of allowable substitutions. This technique is particularly effective in domains where formal or formulaic language is used. It follows that machine translation of government and legal documents more readily produces usable output than conversation or less standardised text.
Improved output quality can also be achieved by human intervention: for example, some systems are able to translate more accurately if the user has unambiguously identified which words in the text are proper names. With the assistance of these techniques, MT has proven useful as a tool to assist human translators and, in a very limited number of cases, can even produce output that can be used as is (e.g., weather reports).
The progress and potential of machine translation have been debated much through its history. Since the 1950s, a number of scholars have questioned the possibility of achieving fully automatic machine translation of high quality, first and most notably by Yehoshua Bar-Hillel. Some critics claim that there are in-principle obstacles to automating the translation process.
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F.A.Q. about Machine Translation
What is Machine Translation?
Machine translation (MT) is an automated translation by computer software. MT can be used to translate entire texts without any human input or can be used alongside human translators. The concept of MT started gaining traction in the early 50s and has come a long way since. Many used to consider MT an inadequate alternative to human translators, but as the technology has advanced, more and more companies are turning to MT to aid human translators and optimize the localization process.
How Does Machine Translation Work?
Well, that depends on the type of machine translation engine. There are several different kinds of MT software which work in different ways. We will introduce Rule-based, Statistical, and Neural.
Rule-based machine translation (RBMT) is the forefather of MT software. It is based on sets of grammatical and syntactical rules and phraseology of a language. RBMT links the structure of the source segment to the target segment, producing a result based on analysis of the rules of the source and target languages. The rules are developed by linguists and users can add terminology to override the MT and improve the translation quality.
Statistical MT (SMT) started in the age of big data and uses large amounts of existing translated texts and statistical models and algorithms to generate translations. This system relies heavily on available multilingual corpora and an average of two millions words are needed to train the engine for a specific domain – which can be time and resource-intensive. When using domain-specific data, SMT can produce good quality translations, especially in the technical, medical, and financial fields.
Neural MT (NMT) is a new approach that is built on deep neural networks. There are a variety of network architectures used in NMT but typically, the network can be divided into two components: an encoder which reads the input sentence and generates a representation suitable for translation, and a decoder which generates the actual translation. Words and even whole sentences are represented as vectors of real numbers in NMT. Compared to the previous generation of MT, NMT generates outputs which tend to be more fluent and grammatically accurate. Overall, NMT is a major step in MT quality. However, NMT may slightly lack previous approaches when it comes to translating rare words and terminology. Long and/or complex sentences are still an issue even for state-of-the-art NMT systems.
What are the Pros and Cons of Machine Translation?
- MT is incredibly fast and can translate thousands of words per minute.
- It can translate into multiple languages at once which drastically reduces the amount of manpower needed.
- Implementing MT into your localization process can do the heavy lifting for translators and free up their valuable time, allowing them to focus on the more intricate aspects of translation.
- MT technology is developing rapidly and is constantly advancing towards producing higher quality translations and reducing the need for post-editing.
There are many advantages of using MT but we can’t ignore the disadvantages. MT does not always produce perfect translations. Unlike human translators, computers can’t understand context and culture, therefore MT can’t be used to translate anything and everything. Sometimes MT alone is suitable, while others a combination of MT and human translation is best. Sometimes it is not suitable at all. MT is not a one-size-fits-all translation solution.
When Should You Use Machine Translation?
When translating creative or literary content, MT is not a suitable choice. This can also be the case when translating culturally specific-texts. A good rule of thumb is the more complex your content is, the less suitable it is for MT.
For large volumes of content, especially if it has a short turnaround time, MT is very effective. If accuracy is not vital, MT can produce suitable translations at a fraction of the cost. Customer reviews, news monitoring, internal documents, and product descriptions are all good candidates.
Using a combination of MT along with a human translator post-editor opens the doors to a wider variety of suitable content.
Which MT Engine Should You Use?
Not all MT engines are created equal, but there is no specific MT engine for a specific kind of content. Publicly available MT engines are designed to be able to translate most types of content, however, with custom MT engines the training data can be tailored to a specific domain or content type.
Ultimately, choosing an MT engine is a process. You need to choose the kind of content you wish to translate, review security and privacy policies, run tests on text samples, choose post-editors, and several other considerations. The key is to do your research before making a decision. And, if you are using a translation management system (TMS) be sure it is able to support your chosen MT engine.
Using Machine Translation and a Translation Management System
You can use MT on its own, but to get the maximum benefits we suggest integrating it with a TMS. With these technologies integrated, you will be able to leverage additional tools such as translation memories, termbases, and project management features to help streamline and optimize your localization strategy. You will have greater control over your translations, and be able to analyze the effectiveness of your MT engine.
A Beginner’s Guide to Machine Translationhttps://www.memsource.com/blog/2018/08/13/a-beginners-guide-to-machine-translation/