KantanMT tSteve Gotz headshotook the time out to interview Steve Götz, Design & Innovation Lab Director at CNGL to find out a little more about his role in CNGL, his future outlook for the localization industry, and some of the projects he is working on.

Originally from the US, Steve holds degrees in Computer Science, Biomolecular Science, and an MBA in Technology and Innovation Strategy. After completing his master’s degree at Said Business School, University of Oxford, Steve joined the CNGL Centre for Global Intelligent Content in 2008, where he was drawn to the unique business and research ecosystem supporting Irish entrepreneurs.

Steve took on the role of Director within the CNGL Design & Innovation Lab (d.Lab), where he leads designers, developers and researchers in developing innovative products for established industry partners like Cisco, Intel and McAfee, and startups like; KantanMT, Scream technologies, and Emizar.

What is CNGL?

Steve: The CNGL Centre for Global Intelligent Content is an academia-industry partnership supported by Science Foundation Ireland, and CNGL researchers are focused on developing new ‘disruptive’ technologies that will have a positive impact on both society and industry. Its goal is to bridge the gap between research and products by fostering a unique ecosystem between academic research and the commercial industry. Researchers come from four Irish universities; Dublin City University, Trinity College Dublin, University College Dublin and the University of Limerick, CNGL’s academic partners.

CNGL has 17 industry partners, these partners range from large companies like Symantec and Microsoft who have a strong global foothold in the technology industry, to commercial startups like KantanMT that have the potential to become strong industry leaders.

Can you explain the Commercial/Research Ecosystem?

Steve: The ecosystem created and maintained through CNGL provides solutions to the industry in a number of different ways. Large established companies approach CNGL when they need a business or technology solution. They want to find a solution through research that they can either implement themselves or outsource.

When an entrepreneur approaches CNGL with a business idea, CNGL works with the entrepreneur to develop a plan and product roadmap to turn their idea into a business realization. In cases where the business idea is compatible with the research, or a larger company wishes to outsource, the large company then becomes a first reference customer for the new startup.

How was KantanMT a part of this ecosystem?

Steve: KantanMT became part of this ecosystem when entrepreneur Tony O’Dowd approached CNGL with his business idea – an automated Statistical Machine Translation (SMT) service that operates on the cloud. This was a good fit with the type research undertaken by CNGL; natural language parsing, text analytics, machine learning and predictive analytics.

Through market analysis Tony used the Minimum Viable Product (MVP) strategy to prove there was a need in the market for his idea. Together Tony and I worked out a suitable product roadmap and plan of what was to be done to get the lean startup up and running.

As Tony developed the KantanMT platform infrastructure, CNGL researchers were also working with the same timeline to develop an analytics feature that would fit into the platform adding value to the product and Tony’s business idea. When the platform was ready, the KantanWatch™ technology just dropped into its place on the platform.

The cooperation and open communication channels between Tony and the CNGL research team meant a viable product was ready to deploy in the market in a very short time frame, making the best possible use of critical resources; funding and time.

KantanWatch technology now licensed to KantanMT is an analytics feature that gives members the ability to monitor the performance of their cloud-based customized KantanMT engines. The key point of KantanWatch is to highlight areas where quality improvements to the engine can be made, and tracking the engine’s progress over time. This opened up a new flexibility for MT users not offered by existing MT vendors.

How do you see the localization industry developing over the next few years?

Steve: The future of the localization industry will most likely be driven by disruptive innovation, the industry is worth approx. $9 billion and this number is set to increase. As with other industries, changes in the localization industry may come from investment outside the industry.

An example of a successful ‘disruptive innovation’ that changed how we use mobile phones came from the iPhone. Apple used the iPhone to shift the focus of the mobile phone from its traditional call and text functions to a more interactive user experience. This disruptive innovation had a monumental impact on how we use and interact with mobile phones and it came from a computer manufacturer rather than a mobile phone or service provider.

The same may happen with the localization industry where the disruption might not come from the industry itself, but instead outside traditional channels. Within the language services industry, companies like Gengo, are playing a part to change and shape the industry by successfully introducing crowdsourcing models, and other Language Service Providers (LSPs) are beginning to follow suit. They are adapting to the demand for real-time translations that technology and the web are driving.

The concept of the ‘Digital native translator’ is becoming more popular and this concept is being fuelled by the need for not only real-time translations, but also the developments of micro blogging.  Micro blogging gained popularity through Tumblr, Twitter, and Facebook where social media users post updates and share small or ‘micro’ pieces of information with their friends and the wider online community.

These developments in technology, and how information is created and consumed has not only made an impact in our everyday lives but it has been an instrumental tool in disaster relief and crisis management. This was evident during the Japanese earthquake in March 2011, when the Pacific Disaster Centre in Hawaii posted information about the earthquake on Twitter before it was reported by CNN.

Social media and text messages proved the most reliable methods of communication. This was also the case during the Haiti Earthquake in January 2010, where social media and SMS were used to identify people trapped or injured.

The first International Workshop on Social Web for Disaster Management held in conjunction with WWW2012 in Lyon, France brought researchers and relevant organizations together to discuss the use of communication channels in large-scale events.

One of the presentations by Julie Dugdale, University of Grenoble 2, Bartel Van de Walle, Tilburg University, and Corinna Koeppinghoff, Tilburg University was a study on ‘Social Media and SMS in the Haiti Earthquake’. While the study found social media and SMS communications useful in the aftermath of the earthquake, it also highlighted a couple of issues. One of those issues was “the difficulties of processing information in a non-standard format from different sources and in various languages”.

Providing real-time translations is an area that will develop in the future and CNGL have been working on projects that may help facilitate this.

What other CNGL projects are there?

Steve: One project CNGL is currently working on is called Kanjingo, which is a real-time, mobile, and micro-crowdsourcing platform for hyper-local social translation.

Kanjingo app

The platform is a tool that translators can use to translate and post-edit small chunks of text or microposts from social media such as Twitter on their mobile device. This translation and post-editing service facilitated by Kanjingo is an example of how CNGL research contributes to society by providing a medium for valuable real-time translations to a variety of users including disaster relief organizations.

A product like Kanjingo has a lot of potential, not only in disaster situations but in any situation where small chunks of text require real-time translations like news reporting or international events. Language is always changing and the way we communicate through social media is very different to standard writing styles. The Kanjingo platform will be able generate high quality ‘social media speak’ bilingual language assets that can be incorporated into existing CAT tools i.e. training MT engines.

For more information on the CNGL Centre for Global Intelligent Content please go to www.cngl.ie

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