Shaping the Path to Neural Machine Translation: Interview with Tony O’Dowd
October 01, 2018Tony O'Dowd
What is Neural Machine Translation (NMT) all about?
Neural Machine Translation is an approach to machine translation that uses large neural networks to produce translations that are more natural sounding and achieve greater levels of fluency. These networks are trained on sequences (or sentences), which means they solve many of the syntactical and grammatical errors previously associated with Phrase-Based Statistical Machine Translation.
With the emergence of relatively cheap, super-powerful computers, coupled with practically unlimited storage capacity due to the emergence of “the Cloud”, we can now compute these complex NMT models in several hours.
These highly efficient models can then provide fast and fluent translations, and at an economically advantageous price. Today, over 90% of the daily traffic on the KantanMT platform is processed by our NMT services. This gives you an indication of the high regard our customers have for the efficacy of our NMT platform.
Why is there so much hype around NMT?
Simply because it provides a demonstrable improvement in machine translation outputs, resolving many of the problems associated with the statistical machine translation (SMT) model. Effectively, we have in the last two years solved many of the translation shortcomings of the SMT system; deficiencies that we’ve been trying to resolve for the last two decades! So, you can imagine how excited we are to be able to move with such speed compared to the pace of development we were lumbered with when working with SMT.
An interesting factoid about NMT is that we actually don’t train them using “whole words”, but we in fact train them using “word pieces”. And even if we don’t have parallel training data for a language combination, we can build a zero-shot network that will be capable of producing translations in these languages! It’s completely amazing what we can do once we set up the deep learning approach and throw super-computers at the problem. In Deep Learning (DL) we use highly sophisticated, multi-layered, pattern of ‘neurons’ to process huge chunks of data looking to refine the information contained within that data. The DL process can take an abstract jungle of information (word pieces), as is contained with data, and using the power of super-computation refine the data in to clearly understood language.
Can you imagine how good Neural MT will be in a further two years? Will Moore’s Law of exponential technological growth apply to Neural Machine Translation too? I believe it will. It will be amazing to see then how powerful NMT will be. It is certainly something that excites us here at KantanMT.com.
Which languages have made the greatest progress for NMT?
Any language that has a deep and complex grammatical structure can now be efficiently modelled using Deep Leaning and Neural Networks. For example, take the grammatical characteristic of the humble German verb – under normal circumstances it needs to be positioned at the end of a sentence. That would seem a straight forward enough challenge? However, SMT struggled to position the German verb accurately. To overcome this, we at KantanMT.com used advanced part-of-speech reordering approaches to improve this accuracy. This was a very complex, time-consuming and computationally intensive approach. However, NMT (because we train the engines on full sentences) almost always correctly positions that illusive German verb. This methodology also allows us to meet the challenges of languages such as Hungarian and Finnish. These are now well within our capabilities, allowing us to produce very good translation outputs using NMT.
Where do you see the translation industry in the next 5 years?
What an exciting time to be in the Localization Industry! We are on the cusp of a massive explosion in Artificial Intelligence (AI), which will impact all facets of the localization industry’s workflow and processes.
The industry will use automated translation technology to process even more content, in to more languages, and faster than ever before. Translators should not fear, as they will be the main beneficiary of this transformation. As this technological evolution grows, translators will be able to produce more words per day and consequently, significantly improve their income levels. I envisage a scenario whereby translation from scratch will be viewed as old-school and passé. The translation model will change in the same way as Computer Aided Translation (CAT) transformed the industry for the better. In the new NMT paradigm, the post-editing of a constantly improving machine translation output will be seen and accepted as the modern, progressive way of working. And the industry will be the better for it.
AI will also enable better job matching and candidate selection – so translators will be selected based on their relevant skill sets, domain knowledge and previous job performances. This is not to be feared, as essentially this is they way we choose our dentists and doctors today. AI wil
l become a driver for greater competition and increased professionalism in our industry.
I also see AI being becoming part of the project management workflow system, and the project management role. PM systems will be expected to handle real-time translation workflows. A system that will combine automated translation and “human touch post-editing” to provide almost instantaneous results.
On the quality side, translation errors and problems will be identified by AI checkers and automatically routed for automatic recovery and fixing. The time between job arrival and completion will reduced in some cases to seconds. These “micro-jobs” will be driven by the requirement for new content to be translated in effectively “real time”. This fast system will be required for content such as blogs, wikis, live user forums, reviews, internal corporate content, help chat lines etc.
What should we expect from KantanMT in the next few months?
We’re working on a new type of Neural Network that will provide even better translation outputs than before, with a significantly reduced training time. These new networks are already in testing with one of the largest eCommerce companies; so, stay tuned for further news of this major step forward in the evolution of NMT.
Additionally, we have figured out a way of measuring the quality of an automatically generated translation. This Quality Estimation Score system was developed by KantanMT.com for the European Commission. The good news is, we shall be open-sourcing this technology in early 2019.
You’re also going to also see a new, improved version of KantanLQR that will support multi-lingual quality projects. It will give you the means to measure how individual language arcs are performing across your enterprise.
Tony O'Dowd - Founder of KantanMT
Tony is Founder and Chief Architect at KantanMT.com, a cloud based platform used by some of the largest companies in the world to machine translate billions of words of online content.
Prior to this he was Founder of Alchemy Software Development, developers of Alchemy CATALYST, the market leading CAT tool for software and web site localisation.
Tony spent three years as a lecturer at Trinity College Dublin, teaching Microprocessor Design and Assembly Language Programming.
Aged 50, Tony has a BSC Computer Science from Trinity College Dublin, is a Fellow of the University of Limerick, and is a founder of FIT Ltd., a million euro government training organization for the long term unemployed.