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This story is an overview of the field of Machine Translation. The story introduces several highly cited literature and famous applications, but I’d like to encourage you to share your opinion in the comments. The aim of this story is to provide a good start for someone new to the field. It covers the three main approaches to machine translation as well as several challenges of the field. Hopefully, the literature mentioned in the story presents the history of the problem as well as the state-of-the-art solutions.

Machine translation (MT) is the task to translate a text from a source language to its counterpart in a target language. There are many challenging aspects of MT: 1) the large variety of languages, alphabets and grammars; 2) the task to translate a sequence (a sentence for example) to a sequence is harder for a computer than working with numbers only; 3) there is no one correct answer (e.g.: translating from a language without gender-dependent pronouns, he and she can be the same).

Machine translation is a relatively old task. From the 1970s, there were projects to achieve automatic translation. Over the years, three major approaches emerged:

• Rule-based Machine Translation (RBMT):
• Statistical Machine Translation (SMT):
• Neural Machine Translation (NMT):

Rule-based Machine Translation

A rule-based system requires experts’ knowledge about the source and the target language to develop syntactic, semantic and morphological rules to achieve the translation.

The Wikipedia article of RBMT includes a basic example of rule-based translation from English to German. The translation needs an English-German dictionary, a rule set for English grammar and a rule set for German grammar

An RBMT system contains a pipeline of Natural Language Processing (NLP) tasks including Tokenisation, Part-of-Speech tagging and so on. Most of these jobs have to be done in both source and target language.

RBMT examples

SYSTRAN is one of the oldest Machine Translation company. It translates from and to around 20 languages. SYSTRAN was used for the Apollo-Soyuz project (1973) and by the Europian Commission (1975) [1]. It was used by Google’s language tools until 2007. See more at its Wikipedia article or the company’s website. With the emerge of STM, SYSTRAN started using statistical models and recent publications show that they are experimenting with the neural approach as well [2]. The OpenNMT toolkit is also a work of the company’s researchers [3].

Apertium is open-source RBMT software released under the terms of GNU General Public License. It is available in 35 languages and it is still under development. It was originally designed for languages closely related to Spanish [4]. The image below is an illustration of the Apertium’s pipeline human translators statistical machine translation system.

Cross-Lingual Transfer Learning

Zoph et al. (2018) applied transfer learning in machine translation and proved that having prior knowledge in translation of a separate language pair can improve translating a low-resource language.

Figure 3 illustrates their idea of cross-lingual transfer learning. The researchers first trained an NMT model on a large parallel corpus — French–English — to create what they call the parent model. In a second stage, they continued to train this model, but fed it with a considerably smaller parallel corpus of a low-resource language. The resulting child model inherits the knowledge from the parent model by reusing its parameters for machine translation system.

Compared to a classic approach of training only on the low-resource language, they record an average improvement of 5.6% BLEU over the four languages they experiment with. They further show that the child model doesn’t only reuse knowledge of the structure of the high resource target language but also on the process of translation itself.

The high-resource language to choose as the parent source language is a key parameter in this approach. This decision is usually made in a heuristic way judging by the closeness to the target language in terms of distance in the language family tree or shared linguistic properties. A more sound exploration of which language is best to go for a given language is made in Lin et al. (2019).

 Advantages:

• No bilingual text required
• Domain-independent
• Total control (a possible new rule for every situation)
• Reusability (existing rules of languages can be transferred when paired with new languages)

Disadvantages

• Requires good dictionaries
• Manually set rules (requires expertise)
• The more the rules the harder to deal with the system

Multilingual Training

The path that was cleared by cross-lingual transfer learning led naturally to the use of multiple parent languages. The straightforward approach, first described by Dong et al. (2015), mixes all the available parallel data in the languages of interest and sends them into training as illustrated in Figure 4. What results from the example is one single model that translates from the four languages (French, Spanish, Portuguese and Italian) to English, machine translation engine translation quality.

Multilingual NMT offers three main advantages. Firstly, it reduces the number of individual training processes needed to one, yet the resulting model can translate many languages at once. Secondly, transfer learning makes it possible for all languages to benefit from each other through the transfer of knowledge. And finally, the model serves as a more solid starting point for a possible low-resource language.

Statistical Machine Translation

For instance, if we were interested in training MT for Galician, a low-resource romance language, the model illustrated in Figure 4 would be a perfect fit as it already knows how to translate well in four other high-resource romance languages machine translations linguistic rules.

A solid report on the use of multilingual models is given by Neubig and Hu (2018). They use a “massively multilingual” corpus of 58 languages to leverage MT for four low-resource languages: Azeri, Belarusian, Galician, and Slovakian. With a parallel corpus size of only 4500 sentences for Galician, they achieved a BLEU score of up to 29.1% in contrast to 22.3% and 16.2% obtained with a classic single-language training with statistical machine translation (SMT) and NMT respectively.

Transfer learning also enables what is called a zero-shot translation, when no training data is available for the language of interest. For Galician, the authors report a BLEU score of 15.5% on their test set without the model seeing any Galician sentences before.

If you’re localizing the content on your website, using machine translation (MT) can speed up the process and reduce the cost. But, is machine translation right for you?

In this post, we’ll look at what various machine translation strategies involve, the technology behind machine translation engines and what form of machine translation is right for your content.

How to Choose the Machine Translation Engine ?

Machine translation has a very long history and the first translation engines were developed in the 1950s. Due to the fact that these engines have evolved over time, different MT approaches are available and old systems have been replaced by newer and more efficient models.

Now, when it comes to selecting MT engines, businesses have two main choices: generic MT engines and custom MT engines.

What is the Generic MT Engine ?

Generic MT engines like Google Translate, Microsoft Translate and Amazon Translate are not trained with data for a specific domain or topic. As a result, they’re best for general translations or translations that aren’t specific to an industry. They can often provide you with a good idea about what a section of text in another language says, but the translation is likely to contain problems with syntax and grammar.

Machine Translation vs. Human Translation

Machine translation hits that sweet spot of cost and speed, offering a truly snappy path for brands to translate their records at scale without much overhead. Yet, that doesn’t mean it’s consistently relevant. On the other hand, human translation is incredible for those undertakings that require additional consideration and subtlety. Talented translators work on your image’s substance to catch the first importance and pass on that feeling or message basically in another assortment of work.

Leaning upon how much content should be translated, the machine translation can give translated content very quickly, though human translators will take additional time. Time spent finding, verifying, and dealing with a group of translators should likewise be considered.

Numerous translation programming providers can give machine translations at practically zero cost, making it a reasonable answer for organizations who will be unable to manage the cost of expert translations.

Machine Translation is the instant modification of text from one language to another utilizing artificial intelligence whereas a human translation, includes actual brainpower, in the form of one or more translators translating the text manually.

Machine translation is mainly equipment that assists marketers or translators to accomplish a motive. However, it is not a replacement for the old systems of translation, instead, it is a modification. Many machine-based translators considerably offer their services free of charge, making them much more alluring, especially to organizations and learners.

Machine translators analyze the structure of the first content, at that point, separate this content into single words or short expressions that can be easily translated. At last, they reconstruct those single words or short expressions using the very same structure in the picked target language.

Cost-Effective Translation

Machine translation increases productivity and the ability to deliver translations faster, reducing the time to market. There is less human involvement in the process as machine translation provides basic but valuable translations, reducing both the cost and time of delivery. For example, in high-volume projects, you can integrate machine translation with your content management systems to automatically tag and organize the content before translating it to different languages. 

What do you Need to Consider?

If machine translation is to add value to your business, it is important to be critical and carefully consider when it makes sense to use machine translation. Otherwise, unnecessary post-editing will have to be done and ultimately, it may harm the company and its brand as a result of poor and unprofessional communication.

In your considerations, you need to take into account what type of text it is, where it is to be used, who the recipient is and what language combination it is.

What Texts are Suited for Machine Translation?

Machine translation is best suited for texts where the language and terminology are relatively simple and which do not require the language to be creative and convincing as is the case for texts with a high branding value, such as website texts, brochures and other marketing texts.

The language combination also plays a role as the output of machine translation is certainly not good for all language combinations. Therefore, it might be a good idea to test the output for the individual language combinations to see if the result is good enough before you choose the language combinations for which you want to use machine translation.

In What Situations Can you Use Machine Translation?

Roughly speaking, machine translation makes sense when speed and price is more important than quality.

This could be when you have large volumes of content for internal use where it is only necessary to understand the outlines of the content. It might in particular be a valuable solution for large companies with foreign employees. In that case, machine translation can be of great help to the employees as suddenly, they will be able to understand the content of minutes, reports, etc. which are otherwise generally only prepared in the local language. In this example, machine translation helps ensure that communication does not stand in the way of collaboration translation process.

In addition to content only intended for internal use in the company, machine translation can also be used for content with a low branding value, such as automatic chat messages etc.

The volume of content that needs to be translated has been on a rise over the last years in line with the general globalisation. However, budgets rarely cover this need and the company might have to choose not to translate texts that they would in fact have liked to translate. With machine translation, it is now possible to translate content which would otherwise not have been translated machine translation software.

Is Combining Human and Machine an Advantage?

When you find that your text is suitable for machine translation, meaning that the output is good enough for further processing, but you do not want to compromise quality, you can choose a solution where you combine human and machine. There are different solutions for this. Your choice of solution should depend on what you expect from the quality and creativity of your text translation memory.

You can choose that a translator makes what we call post-editing of the machine translated text. With this solution, the translator spends time editing the text to make it correct and understandable. If you want an extra pair of eyes on you text, it is a good idea to have an extra proofreader read the text to eliminate any overlooked errors or misinterpretations. If your text requires the language to be fluent and creative, it is also possible to have a copywriter go through the text. In situations where a copywriter is needed, it generally makes most sense to choose a translator rather than a machine from the start.

How Do You Get Started?

Talk to your translation agency about whether machine translation can become part of your translation strategy and how to get started. Before that, you can make a list of the types of content you need to have translated and into what languages. Also consider the recipients of the texts.

When you contact Ad Hoc Translations, we will help you make a plan for how to best integrate machine translation into your strategy. We will define the types of text for which we can generally use pure machine translation if the machine output is good enough. And then we will determine when machine translation should be combined with post-editing and proofreading and what types of texts should always be translated by a human translator who can relate critically and creatively to the text.

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Machine translation is an important technology for the global economy. However, in the past, the development of machine translation was very difficult. As we all know, human beings have a very high language barrier. Therefore, the development of machine translation has always relied on the assistance of many people, and it took a lot of time to develop. However, the machine translation history has now entered a new stage.

At present, the development of machine translation is very rapid, and there are many applications of machine translation. At the same time, the development of machine translation has become increasingly mature. More and more companies are developing a variety of machine translation products. In addition, the use of machine translation is becoming more and more popular in the example based machine translation when machine translation began.

Machine Translation History along with Statistical Machine Translation

Machine translation history is the history of machine translation, including all of its major milestones and innovations. There are many terms that are used to describe the history of machine translation, including “history of MT”, “MT history”, and “MT history timeline”. Machine translation history is usually written as a timeline, but it can also be written in a more narrative form using the machine translation system or google translate.

Machine translation history is the story of the development of machine translation from the beginnings of the field in the early 1960s up until today. The earliest applications of machine translation can be traced back to the 1930s. At that time, the idea of machine translation was not really considered as a viable concept, and the applications of machine translation were limited to a handful of domains.

What is the Machine Translation Technology ?

Machine translation is the process of converting text from one natural language to another. This conversion process requires a translator to understand the meaning of the source text in the first language (the language into which the text is being translated) as well as the meaning of the target text in the second language (the language into which the text is being translated). This type of translation is also referred to as machine translation because computers execute the translation process.

However, this type of translation does not necessarily involve human beings. Machine translation technology has existed since the early 1950s. During that time, the first computerized machine translation systems were developed by researchers at the United States’ Defense Advanced Research Projects Agency (DARPA). The aim of the project was to produce machine-generated translations of English-language military documents for use in the event that an English speaker became lost in a foreign country. These machines were also designed to help the military make sense of foreign languages spoken by their enemies. The technology behind these early attempts to create machine translations was based on the same principles as modern-day machine translations.

The first generation of machine translation systems worked by translating text directly from one language to another. These early models did not possess a knowledge base and had no understanding of the source text. In the case of military applications, this meant that they could only translate text that was written in the target language. The second generation of machine translation systems added a knowledge base, enabling them to translate text that was not originally written in the target language via machine translation output with human translation.

How Machine Translation Became An Industry ?

Machine translation has become an essential part of human communication. However, it wasn’t always the case. In the past, translations were only done by humans. Nowadays, most of the translation services are provided by machines. Before we delve into how machine translation became an industry, let’s first see why this was needed in the first place. In the past, translation services were only provided by humans.

The reason behind this is the complexity of language. There are multiple reasons why people need to communicate using words instead of just speaking. One reason is that people want to be understood. We want to be understood by our peers, our family, and our friends. If we speak, it’s harder to be understood. This is because of the complexity of the language.

Another reason is that sometimes, the message being communicated cannot be conveyed by a single word. If someone says “I am hungry”, we know exactly what they are saying. But, if they say “I am hungry because I am very hungry”, it’s difficult to comprehend the message. This is because of the context of the sentence. If we try to translate these sentences to another language, it would be easier to understand the meaning.

Who Invented the Machine Translation? Here Are A Few Amazing Facts

Machine translation has made a huge impact on how people communicate in the modern world. It is the process of translating words and sentences between two different languages. This technology has allowed people to communicate with people in different parts of the world without having to learn a new language. However, it is not a perfect system, and it can sometimes translate incorrectly.

Here are some amazing facts about machine translation. It has been around for centuries. There have been instances where a person would read the Bible in their own language and then ask someone to translate it to them. In the early 19th century, the first mechanical translation device was created. It was a contraption that translated text from German into English.

This was an early example of machine translation. Machine translation started becoming widely used in the late 20th century. In the 1960s, the computer was the main focus of research, but it wasn’t until the 1980s that computer algorithms were developed to make machine translations possible. In the 21st century, machine translation has become increasingly more popular. Some of the most important advancements include the use of neural networks and big data. In addition, the industry is now embracing open-source and crowdsourcing models.