How to Overcome Language Barrier with Machine Translation

How to Overcome Language Barrier with Machine Translation

How to Overcome Language Barrier with Machine Translation

Automatic or machine translation is perhaps one of the most challenging artificial intelligence tasks given the fluidity of human language. Classically, rule-based systems were in a popular use for this task, but statistical methods replaced them in the 1990s. More recently, deep neural network models achieve state-of-the-art results in a field that aptly uses the name of neural machine translation.

What is Machine Translation?

Machine translation is automatically converting source text in one language to the text in another language.

The fact is that accurate translation requires background knowledge to resolve ambiguity and establish the content of the sentence.

Classical machine translation methods often involve rules for converting text in the source language to the target language. Linguists develop the rules and they may operate at the lexical, syntactic, or semantic level.

The key limitations of the classical machine translation approaches are both the expertise required for developing the rules, and the vast number of rules and exceptions required.

What is Statistical Machine Translation?

Statistical machine translation, or SMT for short, is the use of statistical models that learn to translate text from a source language to a target language gives a large corpus of examples.

This approach does not need a complex ontology of interlingua concepts, nor does it need handcrafted grammars of the source and target languages, nor a hand-labeled treebank. All it needs is data—sample translations from which an expert can learn a translation model.

Quickly, the statistical approach to machine translation outperformed the classical rule-based methods to become the de facto standard set of techniques.

The most popular models for statistical machine translation have been sequence-based. In these models, the basic units of translation are words or sequences of words. These kinds of models are simple and effective, and they work well for man language pairs.

The most widely used techniques werephrase-based and focus on translating sub-sequences of the source text piecewise.

Statistical Machine Translation (SMT) has been the dominant translation paradigm for decades. Practical implementations of SMT are phrase-based systems (PBMT) which translate sequences of words or phrases where the lengths may differ.

Although effective, statistical machine translation methods suffered from a narrow focus on the phrases being translated, losing the broader nature of the target text.

The hard focus on data-driven approaches also meant that methods may have ignored important syntax distinctions known by linguists. Finally, the statistical approaches required careful tuning of each module in the translation pipeline.

What is Neural Machine Translation?

Individuals have a plethora of platforms that allow them to access consumers all over the globe and work with other companies in faraway places – if only they could speak the same language.

In an ironic twist, language has turned from something that first facilitated human cooperation and growth, to something that impedes our ability to work together.

Technology may finally be ready to abolish that barrier forever. Remarkably, in 2018, over 20 years after widespread use of the Internet began, we still rely almost only on humans to translate language in commercial formats.

But translation bears all the earmarks of those functions that artificial intelligence ought to replicate, and a technology called Neural Machine Translation (NMT) does just that.

The key benefit to the approach is that a single system can apply directly on the source and target text, no longer requiring the pipeline of specialized systems used in statistical machine learning.

Unlike the traditional phrase-based translation systems which comprise many small sub-components that operate separately, neural machine translation efforts to build and train a single, large neural network that reads a sentence and outputs a correct translation.

Contextual translation ability

By leveraging its contextual translation ability alongside its deep learning functions, NMT has achieved historic results in the journey to a post-language economy.

In a side-by-side comparison with human translators, in a technical domain translation for English-Korean, translators preferred SYSTRAN’s NMT translations 41 percent of the time.

That success can come from advancing language translation beyond rule-based translation methods.

NMT is a deep learning technology that translates within the context, not just one word at a time.

Encoder-Decoder Model

Multilayer Perception neural network models are good for machine translation although there are several factors that limit models such as a fixed-length input sequence where the output must be the same length.

These early models have improved upon recently through the use of recurrent neural networks organized into an encoder-decoder architecture that allows for the variable length input and output sequences.

An encoder neural network reads and encodes a source sentence into a fixed-length vector. A decoder then outputs a translation from the encoded vector.

The whole encoder-decoder system, which comprises the encoder and the decoder for a language pair, is in a great usage to maximize the probability of a correct translation given a source sentence.

Key to the encoder-decoder architecture is the ability of the model to encode the source text into an internal fixed-length representation called the context vector.

Interestingly, once encoded, different decoding systems are in use, in principle, to translate the context into different languages.

Encoder-Decoders with Attention

Although effective, the Encoder-Decoder architecture has problems with long sequences of text which demand translation.

The problem stems from the fixed-length internal representation that demand decoding each word in the output sequence.

The solution is to use of an attention mechanism.  It allows the model to learn where to place attention on the input sequence as each word of the output sequence is decoded.

Using a fixed-sized representation to capture all the semantic details of a very long sentence is very difficult. A more efficient approach, however, is to read the whole sentence or paragraph. The next step is to produce the translated words one at a time, each time focusing on a different part of the input sentence to gather the semantic details required to produce the next output word.

You would also be interested Obstacles on the Way to the Perfect Translation

References:

machinelearningmastery.com

www.itproportal.com

www.entrepreneur.com


Recent Articles about Translation  

Translating in the Sport Field
Translating in the Sport Field
Last Updated on November 25, 2020

Along with the increasing popularity of global tournaments, such as the Olympics and the World Cup events, demand for translating in the sport field is increasing along with time. This is one of the best methods available for the players to engage with a global audience. On the other hand, sporting event organizers can get in touch with customers all around the world with sports translations. The applications of translations in sport field are even expanding to the retail stores. That’s because the best method available to connect with other people is to use their own language. (more…)

Translating in the Medical Field
Translating in the Medical Field
Last Updated on November 18, 2020

Medical field is one of the most important fields that exist out there in the world as of now. Researchers who work for the industry are continuously working to invent new drugs, which can help them to cure life-threatening diseases. On the other hand, they also work hard to implement new medical devices, which can make the life easy for all people who are working for the industry and who struggle hard with illnesses to ensure a faster recovery. (more…)

Translating in the Financial Field
Translating in the Financial Field
Last Updated on November 11, 2020

Due to globalization, financial institutions have come across the need to work along with worldwide clients and partners. This is where they come across the need to communicate in many different languages. Translating in the financial field is playing a major role due to this reason. (more…)

Translating in the Commercial Field
Translating in the Commercial Field
Last Updated on November 4, 2020

Globalization is one of the key factors that drives businesses forward. However, businesses that open up their boundaries to get in touch with customers from all over the world are facing numerous challenges due to different languages being used out there in the world. If you are a business owner, you should learn how to overcome this barrier. That’s where services that offer translating in the commercial field can help you with. (more…)

Translating in the Art Field
Translating in the Art Field
Last Updated on October 28, 2020

The process of translating something is itself an art. This becomes prominent with related to the jobs that exist in translating in the art field.  The process of translating in the art field is unique and it is associated with a series of challenges. However, an expert translator is in a position to go through the overall translation process and provide people with amazing results. (more…)

Translating from an image File
Translating from an image File
Last Updated on October 21, 2020

Are you looking forward to translating an image? Then you must be looking for the most effective method to get the job done. Translating from an image is not the easiest thing that you can do. That’s because you will come across numerous challenges while you are trying to get the job done. However, you don’t need to worry too much because there is lots of help and support available. In the meantime, you can discover numerous methods that are available to end up with getting the translation job done as well.

We will share details about some of the best translation methods available to proceed with translating from an image file. It is up to you to go through these options and pick the best method based on your preferences. (more…)

Translating from a PDF File
Translating from a PDF File
Last Updated on October 14, 2020

Do you have a PDF document to be translated from one language to another? Then you might come across some challenges. That’s because PDF files are not designed to be edited. However, you don’t have to give up your hopes because there are some effective methods available to translate the PDF documents. From this article, we will share more details about the options available for you to proceed with translating form a PDF file. You can go through these options and make a decision to move forward. (more…)

Get The Best Translation Price