Machine Translation

Deep learning seeks to artificially mimic the structure of the human brain’s neural networks in a computer. Neural networks control the brain’s thinking processes. By studying neural networks, scientists realized that once computers had become fast enough, and there was enough data on the internet, they could create an algorithm that trains large artificial neural networks. The wide adoption of neural machine translation has now resulted in the technology to be referred to as just machine translation. 

How Does Machine Translation Work?

Natural language machine translation is well on its way to becoming the single most important productivity enhancement technology for human translators. At the same time, fast improving quality means that stand-alone machine translation is rapidly finding new use cases.

In that sense, the pure play machine translation technology market relates to the development and deployment of machine translation technology without any human translation services. This differs from the human-in-the-loop models, such as post-editing of machine translation (where a human edits machine output) and interactive machine translation (where the machine provides prompts and the human accepts or modifies the suggested output).

The History Of Machine Translation?

The history of machine translation is divided into three main eras: Rules-based (1960s), Statistical (ca. 2007) and Neural (2016 onward). Academic research into neural machine translation (NMT) really only began in 2014, and NMT has since replaced SMT as the de facto standard in automated translation.

The first breakthrough in NMT was made through recurrent neural network (RNN) models, and eventually shifted into convolutional neural networks (CNN) with attention mechanisms. Today, the most popular model is the attention-based transformer, developed by Google.

Companies such as Google and Amazon Web Services have released free and open source neural libraries / frameworks for machine learning that further promote NMT research and use. Google, for instance, released the TensorFlow framework, a library for neural network-based machine learning applications. On top of that, they also released the widely used tensor2tensor neural library for sequence-to-sequence tasks, such as NMT.

Currently, approaches such as massive multilingual machine translation are being explored in order to remove the need for pivot languages (mostly English) and build more powerful models able to machine translate thousands of different language combinations.

Machine Translation Technology Ecosystem

Big Tech Machine Translation: Many of the big tech players have developed machine translation capabilities for their own purposes. Social platforms such as Facebook develop MT to make user-generated content accessible to other users in their own language and, more crucially, for their core business, to track content posted in hundreds of languages worldwide. Chinese e-commerce giant Alibaba, meanwhile, uses MT to solve the challenge of providing multilingual customer support to users of their products as well as making product information available in multiple languages. Typically, these solutions are not licensed to third-party corporates, although they theoretically could be.

Machine Translation Niche Players: Niche players are those companies whose main specialism is developing and deploying machine translation technology for licensing to enterprise customers, LSPs, and freelance linguists. They do not compete in the language services space or outside machine learning, and they are not research institutions. Examples of niche players in the machine translation space include the following:

Language Service Providers’ Machine Translation Offering: A fast growing number of LSPs offer human-in-the-loop services, where machine translation output is augmented by human translation / editing services and delivered to the customer. A number of LSPs develop proprietary MT solutions that are licensed to enterprise customers, third-party LSPs, and freelance linguists. These solutions are offered as stand-alone products or as part of a suite of products, without human translation services.

Machine Translation Research Community: The research community has always played an important role in the development of machine translation technology. Neural MT remained a fringe research topic until around 2016, when its adoption to the mainstream began in earnest. Since mid-2018, research activity on NMT published on the arXiv repository  has soared. Key contributions to machine translation research come not only from academia, but also from national and independent research institutes and big tech, as well as a handful of LSPs / machine translation providers