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        Reasons to Be Cautious Before Adopting Machine Translation

        Deep learning & machine translation in providing business localization services are becoming popular. However, they are still not as effective as humans.It is rather surprising that with all the advancement in technology, machines are still not able to accurately translate the language. Though there have been breakthroughs in machine learning, machine translation, and neural network technologies, language remains a complicated aspect that machines are not yet able to decipher with 100% accuracy.

        Evidence of this phenomenon can be observed in language translation services and applications. Though they can translate the literal meanings of words, they are still not able to translate the contextual meaning of a foreign text. Language is a complicated aspect of human interaction. Sometimes words can be literal or contextual. The same can be observed in online and article text spinners. All these are artificial intelligence systems that use machine learning and complex algorithms. In most cases, text spinners cannot differentiate between contextual and literal meaning. Though some companies offer to spin text in a manner that makes it unique without losing meaning, they are not yet satisfactorily effective.

        The History of Machine Translation

        The first machine translation device was invented in the year 1933. Though it was rudimentary, it could still perform some basic translation functions. The creator of this machine was known as Peter Troyanskii, a Soviet scientist. It consisted of a simple typew4riter, cards in 4 different languages and a camera. It was quite remarkable, but despite the inventor's efforts to perfect this machine for twenty years, he died while still trying to perfect his creation.

        In 1954, the4 IBM 701 was unveiled. It was a clunky computer developed under a program that later on came to be regarded as the Georgetown-IBM experiment. It was able to translate about sixty Russian sentences into pure English. Though it was primitive, it evoked enough interest from both scientists and inventors to stimulate funding of machine learning initiatives the world over. Apart from the united states, countries such as Canada, Japan, and France all joined in the journey towards machine translation.

        Surprising, interest for this project died out rapidly when the scientist faced numerous semantic challenges that seemed impossible to overcome. Within the united states, this project died off due to a shortage of funds. Some European countries, in partnership with Canada, continued with research, but their efforts did not yield fruits until the advent of the world wide web in the 1990s. However, professional translation has remained the mainstream method of localizing content. Professional translators undergo extensive training on all aspects of a language and understand it in a deeper context. This opened a vast window of opportunity for making another try at translation.

        The Concept of Neural Machine Translation

        In 2016, Google made an interesting announcement that they had come up with groundbreaking technology. It was known as the Google Neural Machine Translation System or GNMT. They claimed it could translate whole sentences and semantics at a go instead of translating word for word. Through a large simulated neural network, the system uses multiple levels of processing to learn on its own. It uses information obtained from a former task to add to the existing information it has already gathered. This enables it to tweak and modify every translation each time continuously.

        This kind of learning is known as "deep learning". It is the most promising advancement in the realm of machine learning. It attempts to create a simulation of the mechanism of the human brain. For example, imagine a child learning to speak. Information obtained from a past session enhances successive sessions until, eventually, the child can talk in complete sentences that make sense. At first, the child might sound gibberish, but each time the child attempts to speak, his language gets better. Soon, he/she can begin conversations depending on the context. Even though the parents might claim to have taught the child how to speak, it is the internal neural networks of the child's brain that plays a huge role in the process. This is the same principle that neural machine translation operates upon.

        Online translation services also utilize some form of deep learning to connect the meanings of words from one language to another. One example is Google translate, which is among the most popular online service.

        Challenges Machine Translation Faces

        Even though neural machine translation has tremendously improved with regards to quality, there are still numerous challenges that hinder its perfection.

        • One of the most obvious obstacles is that artificial intelligence is still inferior to its creators, i.e., humans. There are some aspects of human qualities and diverse natures that it cannot replicate, at least at the moment.
        • The human mind is much more complex than even neural networking technologies are still trying to decipher.
        • In every language around the world, numerous words can have separate implications or even meanings depending on the context in which they are applied. However, it is not yet possible for machines to properly understand the different contexts in which words are written.
        • Emotions, gestures, and culture all play a role in the complexity of language. This greatly affects the contextual meaning of a work.
        • All the above factors cannot easily be understood by software and algorithms in a manner that a human can. It, therefore, requires human translation to fill in the contextual gap.

        Future Trends in Machine Translation

        There are still many challenges in machine translation. One of them is that Artificial intelligence is majorly based on reasoning and logic. However, language is not usually reasonable nor logical. It is highly dynamic and contextual.


        Machine language is still a long way from replacing professional translation. Even though they might still use software to assist them in translation, it is still a less developed tool. What's more, experienced human translators can never be replaced by algorithms or artificial intelligence. They are a lot better at offering creative solutions that meet the expectations and values of the target audience.