With globalization, the language barrier has become one of the key challenges to address. The advancement of artificial intelligence and machine translation enables you to tap into your global audience conveniently. Based on the neural network technology, the Neural Machine Translation (NMT) is one of the top rated approaches to AI and machine learning. The approach offers near-perfect to human translation to morphologically rich languages too. When used wisely, NMT is capable of producing some amazing results.

Why do you require machine translation?

Need to bridge the communication gap between clients and businesses are driving the growth of translation services” SpendEdge

  • Escalating online activity
  • Engagement in Multi-languages
  • Globalized customer service
  • Globalized customer relationship management
  • International business collaboration
  • Security and protection of data
  • Turnaround time

Machine translation then and now

The 70s – Rule based machine translation (RBMT)

  • RBMT is a classical approach to machine translation.
  • Domain-independent
  • Dictionary and grammar based translation
  • Works only with built-in manually developed linguistic rules
  • Works well with generic content and with specific language pairs and rule sets
  • Output is less fluid
  • Limited to few language pairs
  • Costly to develop and takes time to develop such a model

The 80s & early 90s – Statistical machine translation (SMT)

  • Used widely by several online translation services
  • Uses statistical translation models
  • Not restricted to any specific language pairs
  • Works well with nearly 50 languages
  • Can translate to multiple languages from one single source at any one point of time
  • Can be built in lesser time
  • Has limitation with training sets
  • Demands extensive hardware configuration
  • Slow
  • Output is more fluid

Transformation of Simple Machine Translation to NMT

Improving machine learning has always been highly challenging even for big players like Google, Microsoft, Baidu, Amazon, Apple, Facebook. Several types of research have gone behind the utilization of statistical learning techniques to develop a translation model. Google launched Google Translate back in 2006 which worked on the algorithm of Phrase-based machine translation. Since this announcement, enhancing the machine learning technology with key focuses was on speech recognition and image recognition was a prime task for the top players. Ten years later, in 2016, Google came up with the Google Neural Machine Translation system (GNMT), which projected huge improvements, in machine translation quality. Nearly 55-85% reductions in translation error are indicated in several language pairs. Since this launch, enough progress has happened to NMT. Google eventually released Open source neural machine translation (OpenNMT), a library to develop neural machine translation (NMT) models. It also announced Transformer, a model based on attention.

Facebook announced a new approach to NMT back in May 2017. The company came up with a model that is based on a convolutional neural network which it claimed to be nine times faster than Google’s NMT system. Also, a distinguishing component of this FAIR CNN architecture is multi-hop attention where the network takes repeated glimpses at the sentence resulting in a perfect and accurate translation. Much has been spoken about its gating mechanism too. The system promises to open up new possibilities for not only translation but other text processing process too.

Microsoft has been exploring much in neural processing units to enhance AI processes in text processing. The company’s Translator is a cloud-based automatic translation service. Translator app is available on multiple devices. It enables users to enjoy online quality level translations without internet too.

Salesforce too recently entered the race with their announcement of a new NMT model that is 10 times faster in terms of user wait time. The system is capable of translating an entire sentence at a time in a fully parallel way.

Lilt also announced Augmented Translators that leverage the neural feedback loop to produce higher quality translations.

New Approaches and Researchers Are Bringing New Possibilities in NMT

Benefits of NMT

  • Customer engagement in this digital time is tough. If you can sort out in real-time, you are the winner.
  • At home, at the office or anywhere, globalize your conversation by breaking all language barriers and interacting easily with anyone via NMT systems.
  • NMT allows you to offer extended customer support in a language that they can understand.
  • Helps trainers and trainees to succeed in the age of real-time.
  • NMT enables companies and education firms to carry out their training/learning sessions simultaneously in various locations without any language barrier.
  • Unmatched translation and fluidity in the automatic translation history.
  • Product build based on NMT technology is capable of delivering significant improvement on the quality of machine translation.
  • Several companies today use NMT product to gain the most accurate and secure automatic translations of the content of all verticals.
  • Organizations can access high machine translation.
  • Bridges images and texts
  • Simple to use.
  • Easy to extend.
  • Efficient
  • Accurate
  • Contextual
  • Can translate into a target language without knowing the source language.

Inform, Educate and Speak in any Language with NMT

The escalating growth of mobile and social media along with the increasing e-commerce industry can be the major reasons contributing towards the need for translation and localization services. NMT is mainstream today as it assures highest translation quality. When assessed through BLEU (bilingual evaluation understudy), NMT surpasses all other previous attempts to machine translation models. Given this situation, the Global Neural Network Market is expected to attain a market size of $45.8 billion by 2023. NMT systems are far superior to traditional statistical machine translation. NMT uses deep neural networks that are capable of translation not just phrases but the entire sentences at a time.

Applications of Neural Machine Translation

NMT is useful in all situations where there is a demand for local relevancy – call it service, product, support, sales, marketing or even training. Some of the industries that can benefit the best out of NMT include; customer service, healthcare, hospitality, retail, eLearning, automotive and banking.

When it comes to education/training, effective and relevant communication unlocks the potential of both individual and the organization. Neural machine translation can be of great use to personalize information and communication via any eLearning platform. The system could facilitate conversation without any barrier. Even in Travel Industry, to match up with the demand of the digital travellers, service providers need to work on localized resources. With effective translation systems that involve neural network, it is possible to deliver an effective experience to global customers. As for e-commerce industry is concerned, content is king. There is no one language fits all business here. Marketers should strategize for a custom fit flow – a localized dedicated resource for each target region. It influences decision making and purchase patterns. Whether it is for the expansion of your product, service or support across geographies or to access healthcare reports anywhere in any language or to initiate cross-language training, NMT is put to great use.

“Applying artificial neural networks to natural language processing are capable of producing a translation better than a native language speaker”

To Wrap

There is an increasing need for multi-lingual content today. Sales, service, product and support are extended in diverse languages. To get a near-perfect translation, neural machine translation systems gain strong consideration. Even languages like Hebrew, Arabic, Chinese etc have become much easier and simpler to translate via NMT systems. Considering the level of demand to address the global audience, neural machine translation poses to be the next game-changer in the near future.