Taking a business to a global scale involves navigation, not just through untraversed markets but also across language-based hurdles. Natural Language Processing (NLP) offers the utility of Machine Translation, wherein a source language is converted to a target language automatically with significant accuracy. It is the most viable solution to a fast and effective translation process without human intervention.
The branches of computational linguistics and language engineering are exploring new possibilities in translation that expedite the process and eliminate issues that arise in real-time translation. A 2006 study at Hacettepe University revealed that the sustenance of business communication on an international level requires an understanding of cultural and traditional ideologies that come from linguistic clarity.
In this article, we will explore the various NLP approaches to machine translation and how work is being put into new avenues for improving upon translation strategies. We will also talk about the real-world applications of machine translation.
Machine translation is the translation of text from one language to another, using computational models that understand the semantic grouping of objects clearly. Legacy translation language models used to require access to online dictionaries, dedicated transmission channels for texts, and terminology data stores, making them heavy and error-ridden.
Modern machine translation tools are far more lightweight and less complex. They are more versatile in figuring out the subtle nuances of local dialects, intonations, and variations to provide concise and fluid translations of texts.
As there is immense flexibility in human language, specific oddities, and inherent ambiguity, it is crucial to select the right machine translation tool that provides coverage for these factors. Up ahead we will discuss the different types of machine translation and how each fits into a different context of language. These are some of the most nuanced and innovative applications of AI.
The primary motive behind using NLP-based machine translation is to enable the smooth real-time translation of conversations as well as fast and secure translation of highly sensitive documents.
Based on how you choose to program the instructions, whether with reinforcement learning or supervised learning into a machine translation model, the translation would take on one of the following four types:
1)Rule-Based Machine Translation
This type of machine translation begins the translation process by consuming linguistic information and metadata about the source and target language texts. It is mainly used in online dictionaries and grammar engines. Rule-Based Machine Translation (RBMT) tools are programmed as to exactly how a word or phrase in the source language should show in the target language.
For translating, say, an English sentence to Italian, a rule-based program would use three types of analysis methods:
The entire vocabulary of the source and target languages as well as every delicate linguistic rule must be programmed into the RBMT tool for it to make accurate translations. RBMT, however, lacks sufficient adaptability to keep up with languages, which represent an ever-evolving construct. So, RBMT cannot quite keep up with highly dynamic languages, as the related rules have to be programmed prior to its application.
2)Statistical Machine Translation
The Statistical Machine Translation (SMT) model consumes translations produced by humans from existing data stores and applies the learnings to the translation at hand. While RBMT does this with a word-based approach, SMT applications take up phrases for analysis, which gives it its alternate name, Phrase-Based Machine Translation (PBMT).
When both the source and target language are fed into a machine translation, it is known as bilingual text corpora and monolingual text corpora for one of the languages. SMT analyzed both types of corpora to create statistical models for more accurate and precise translations.
Until recently, some of the most successful online tools for translation, such as Babel Fish and Google Translate were based on the SMT model, eventually moving to the neural model. A considerable limitation of the SMT model is that it can translate a particular phrase only if it exists in the reference texts fed to it beforehand.
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3)Neural Machine Translation
Neural Machine Translation (NMT) goes a few steps further than rule-based and statistical models. The NMT model learns from the reference texts that it analyzes, using each learned translation task and applying it to eventual translation problems.
In ways similar to a neural network, the NMT model identifies patterns in the reference texts to arrive at possible contexts and scenarios that could predict the probability of a resultant sequence of words or phrases. Large neural networks are built, allowing the translation systems to learn to function without human intervention.
Following each instance of translation, the system perfects its capability, reducing the margin of error every time. The system is modeled on the human brain with its neural net-based method of learning new source-to-target language correlations.
4)Hybrid Machine Translation
The Hybrid Machine Translation (HMT) technique involves a single system encapsulating several different machine translation processes at the same time. Multiple translation systems run in parallel with each other producing a combined output of all sub-systems. This can be done in the following ways:
Organizations require highly-specific NLP language model data, sometimes even pre-trained, to be translated when taking their business to an international level. They opt for customizable multi-domain translation solutions to have coverage of as many aspects of their business as possible.
Source: Google AI Blog
Some of the major applications of machine translation are:
1)Consumer Translation Applications
Open-source translation applications such as Google Translate use NMT to translate languages in a fast and accurate manner. The encoder-decoder mechanism works simultaneously to generate translated sentences one word at a time. Inline translation tools like Facebook Translate help translate comments in real-time.
2)Image-To-Text
Real-time capture and translation is an emergent technology that is gaining popularity in the startup domain. It combines Optical Character Recognition (OCR) with a machine translation model, digitizing images from any surface and translating the text almost simultaneously. You can click a picture and the model allows you to select the text from the image.
3)National Defense
National governments of the world are increasingly experiencing the ease that NLP-driven machine translation brings to national defense. Defense and intel use cases are aplenty in the machine translation domain. Real-time translation of social feeds of international problem elements requires fast methodologies such as NMT or HMT. Actionable insights from these translations can allow governments to take the necessary steps to reduce the impact of a potential national threat.
4)Navigating Emergency Healthcare
In emergency care, every little bit of information from the patient about the pain they are going through needs to get to the doctor. For patients and doctors speaking different languages, quickly navigating through these differences is important. However, in the current market, only one-way real-time translation apps are being applied, while there is still a long way to go for rapid two-way translation to become a reality.
5)Video Conference Translation
Several video calling software providers have recently released voice translation features in their prototype stages. These use a combination of speech recognition software and statistical machine translation. Translated web pages, closed captions, and previously translated one-on-one conversations serve as sources of learning for these machine translation models. There is ongoing research for training these models to identify the nuances of human language, understand them, and make unsupervised changes when required.
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Choosing the right machine translation methods depend on the language pair as well as the complexity of the domain of application. While several machine translation digital applications are in their prototype stages, the ones that work are revolutionizing multiple domains such as law, finance, education, bipartisanship, and entertainment. If you are seeking NLP-based solutions to transform your business, you can avail of Daffodil's expertise in NLP Software Solutions.