The Advancements of Neural Machine Translation in Hindi


1-StopAsia - The Advancements of Neural Machine Translation in Hindi

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Around 572 million people around the world speak Hindi as their mother tongue and this significant number means that translations in Hindi are becoming ever more needed. As India’s official language, translating English to Hindi can pose serious challenges. And this is where neural machine translation (NMT) comes in. But what is neural machine translation and what challenges does it pose when an organization needs to translate English to Hindi or translate Hindi to English? This article explores these questions in an attempt to provide more definitive answers.

Neural machine translation for the Hindi language

Translation in Hindi is becoming ever more important. With India being home to over one billion people and the country’s growing prominence on the global stage, English-to-Hindi translations are becoming ever more crucial for organizations – both public and private. Whether you want to read a translated newspaper in your mother tongue or you need to work with a huge quantity of documents that are translated from English to Hindi, or anything else in between, machine translation, or more specifically, neural machine translation will be required.

Although machine translation came to the fore in the 1950s, neural machine translation is a much more recent phenomenon. Much like the name implies, neural machine translation is an advanced method of carrying out translations using technology, software, hardware, and artificial intelligence. This is done by providing a corpus of language data in both the source and the target language, enabling the “machine” to carry out a translation. But this machine is capable of so much more. In fact, it’s designed to work like the human brain.

Just like the human brain, which when learning can make mistakes, these machines can also make mistakes that are then corrected for more accurate results. In fact, some studies indicate that neural machine translation can yield results with accuracy that goes up to 60%. Although this is not ideal, it’s an excellent way of processing huge quantities of data, which can then be addressed for quality control and accuracy using a human translator with the right proficiency in both the source and the target languages.

Deep learning and deep neural networks

As mentioned above, neural machine translation is a technology that tries to mimic the human brain’s ability to learn. As a subset of machine translation, we have what is commonly referred to as deep learning. Deep learning is used in translation in Hindi with regard to image applications, big data analysis, speech recognition and more. As a result, it can offer much more precise translations than were possible in the past, and enhance traditional systems to make them more efficient.

Deep neural networks (DNN), on the other hand, can be thought of as language networks. These networks operate in different layers. For example, we can have one shallow layer that only deals with one network in terms of languages, particularly translation in Hindi, and then there are other “deeper” neural networks that can handle more than one shallow layer of language information. Consequently, there are multiple “hidden” layers within deep learning, which help to learn language features.

Challenges that arise with translating in Hindi

Using Devanagari script to denote the written Hindi language system, deep learning, deep neural networks, and neural machine translation can oftentimes face significant challenges in the translation process when you want to translate English to Hindi or translate Hindi to English.

The most obvious part is punctuation. The Devanagari script uses different ways than English to denote the end of a sentence. But there are other challenges as well, particularly when it comes to expressing digits. Further challenges include the actual translation of the language pairs, phrases, and words. This is because Hindi has a much richer morphology than English. This creates a much more challenging aspect when it comes to the “sparse-data” problem. This is because of vocabulary that combines words from various etymological sources.

The future of neural machine translation when translating English to Hindi

Neural machine translation is here to stay with regard to translating in Hindi. The demand for such translations will continue to rise and the accuracy and quality of the translation will be of paramount importance for neural machine translation that utilizes deep learning and deep neural networks.

As a result, “training” the memory of the machines that perform the translations, especially when it comes to English to Hindi translations, will be a constantly evolving task. However, while the accuracy of translations does continue to improve, it’s still not the perfect translation tool, although it’s great for vast quantities of data, and will need to be supplemented by a human translator.

This person or team of professionals can help the machines with quality control and editing the final results for quality of data and accuracy of the translation.

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Desi Tzoneva

Desi Tzoneva has a Bachelor of Laws degree from the University of South Africa and a Master's in International Relations from the University of Johannesburg. For the past five years, she's been a content writer and enjoys unraveling the intricacies of the translation and localization industry. She loves traveling and has visited many countries in Asia, Europe, Africa, and the Middle East. In her spare time, she enjoys reading. She will also never say no to sushi.