Neural Machine Translation is comparatively faster and more accurate

Language is a beautiful way to convey message between humans. Since the machines intercourse in the language came to spotlight, translation technology has seen unprecedented changes. Translations technologies are a gift for people who seeks to convert the content form one language to another. As the years' pass, improvements in computational capacity, artificial intelligence (AI) and data analysis expand both in speed and accuracy of machine translation. One such breakthrough technology is neural machine translation (NMT).

What is Neural Machine Translation?

Neural Machine Translation (NMT) is a software used to translate words from one language to another. The mechanism applies neural network internal towards predicting the likelihood of a sequence of words, often in form of whole sentences. Unlike statistical machine translation which consumes more memory and time, neural machine translation trains its parts end-to-end to maximize performance.

NMT has been seeing great adoption among multinational institutions to aid it's in internal and external communication. NMT systems are quickly moving to the forefront of machine translation, recently outcompeting traditional forms of translation systems. The key benefit of the approach is that a single system can be trained directly on the source and target text, no longer requiring the pipeline of specialized systems used in statistical machine learning.

Previously, machine translations were stuck with multilayer perception neural network models that were limited to a fixed-length sequence where the output must be the same length. Today, the mechanism has updated its stance. It has added attention mechanisms which allowed these models to improve the translation of long sequences of words by permitting the model to learn where to place attention on the input sequence as each word of the output sequence is decoded.

Unlike traditional methods of machine translation that involve separately engineered components, NMT works cohesively to maximize its performance. Besides, NMT employs the use of vector representations for words and internal state through which the words are transcribed to a vector defined by a unique magnitude and direction. Generally, NMT uses artificial neural network to predict a sequence of numbers when given with a sequence of numbers. The NMT encodes every word into a sequence of numbers which is representing the translated target sentence. The NMT also uses bidirectional recurrent neural network also called as an encoder to process a source sentence into a vector for a second recurrent neural network called a decoder. This process is comparatively better at speed and accuracy.

History of machine translation

We became familiar with the translation technology only when Google introduced it in the search engine. Remarkably, the concept has been around for half a century now. Researchers trace the groundwork of machine translation in the early 1950s in the United States. These initial stage translation models relied on bilingual dictionaries, hand-coded rules, and universal principles underlying natural language.

The first public demonstration of machine translation happened in 1954. IBM introduced a system that was capable of translating 49 hand-picked Russian sentences to English. The machine only had a vocabulary of 250 words. Even though the numbers are pretty small, this initiative marked the milestone in the process of machine translation. In 1964, the Automatic Language Processing Advisory Committee (ALPAC) was established by the US government to evaluate the process in machine translation. The committee later published a report on the state of machine translation. After a long gap, a new system called the METEO System was deployed in Canada in 1981 to translate weather forecasts issued in French into English.

In 2016, Google introduced neural machine translation to increase its fluency and accuracy. Google's NMT is an end-to-end learning approach for automated translation, with the potential to overcome many of the weakness of conventional phrase-based translation systems. It consists of a deep LSTM network with eight encoder and eight decoder layers using attention and residual connections. Google NMT is capable of translating even complex sentences called 'zero-shot translations.' For example, previously, translating a Korean sentence to Spanish would involve translating it first to English and then to Spanish. But Google's NMT can directly translate it to Spanish without intervention.

The future ahead

Even though neural machine translation is futuristic, it still needs further improvement to match with other technologies. It takes a lot of time for systems that complex to learn new language pairs. Ways to further improve the accuracy of NMT is still being figured out by developers and researchers. Unfortunately, it will take a long time for NMT to achieve the capacity of human translators. Henceforth, in the coming years, more investment is expected in the translation technology with more researchers working intensely on it.