Grow Your Business with Moses

This month the MoseCore project is coming to its end. From 2012, members of the project consortium put a great collaborative effort to position Moses as a state-of-art machine translation toolkit that helps ensure flexibility and choice for users and fosters a healthy competitive landscape, as well as improve translation processes and capacity and create new business opportunities.


Today, we would like to take this opportunity to express our sincere gratitude to all our colleagues and partners for the confidence they have placed in the work of the MosesCore consortium and Moses toolkit during the last three years.


Here is some interesting Data:

For Real Techies - News on the Moses toolkit

During the second half of 2014, the MosesCore continued its work on advancing the Moses toolkit. Here is the overview of the main achievements:


1. Exploitation of the feature function framework introduced in release 2;

2. Integration and extension of the work from the JHU workshop on Domain Adaptation;

3. Merging of Moses and Moses_chart, as per MosesCore deliverable. Continue this work, we created a framework to enable other translation models and decoding algorithms to be easily added so that the divergence we saw between moses and moses_chart would not be necessary in the future;

4. More complex syntactic translation models (Synchronous Tree Substitution Grammar);

5. Faster, less memory SCFG decoding algorithm.

Moses in 2015

WMT15

17-18 September (Lisbon, Portugal)

This year we are going to run a 'tuning' task. The participants will be given complete hierarchical MT system (Moses with models) and the task will be to submit a moses.ini with weights optimized using whatever optimizer and MT evaluation method they like.

MT Marathon
7-12 September (Prague, Czech Republic)
The support for the 2015 and 2016 marathons will come no longer from MosesCore but another EU project, CRACKER, which aims at high quality translation.

MT Talks
At the end of 2014 we had a successful introduction of the online MT Talks. Now three short videos are out and we add a new one every fortnight together with exercises.

Moses Use Case

We continue to explore the Moses possibilities and its implementation by some of the market leaders. In the January issue we would like to thank our colleagues at the Ireland-based company, KantanMT for sharing their lessons learnt about Moses.


COMPANY NAME

KantanMT.com is a registered trademark of Xcelerator Machine Translations Ltd.


TIME IN MT BUSINESS

The platform was launched commercially in Q4 2013, however, we have been rigorously testing KantanMT.com in academic and commercial settings since 2012. In the beginning the product was offered as a free trial to the KantanMT Community, and their feedback was instrumental in shaping and improving the platform to what it is today.


MOSES EXPERIENCE

The Moses technology has improved immensely over the past 12-18 months. Developer documentation and support materials, while initially very basic, have matured into a more structured, comprehensive and helpful resource. Additionally, the management of software distributions has made it easier to work with, understand and deploy. These are key elements in maintaining and supporting any open-source technology and have made Moses a key technology for the localization industry.


WHY MOSES

The rise of the global economy and the driving demand for multilingual translation created a gap in the market for a sustainable translation method that could automatically scale to accommodate fluctuating translation needs. The KantanMT Development team was able to utilize the open source Moses decoder to develop a cloud-based statistical machine translation platform, where clients could build and manage their own customized MT engines without compromizing on the ownership of their data. The flexibility, scalability and security of the Moses toolkit made this possible.


The Moses toolkit offers the most flexibility in implementing an SMT solution for commercial purposes, as it allows the system’s training and decoding process to be modified. This has enabled the KantanMT team to create a high-value product that is dynamic and commercially relevant.  


To ensure the product could scale and adapt to user needs the KantanMT team needed a decoder that could be built and managed on the cloud. The Moses system enabled this functionality.


Parallel language data is required to train an SMT engine. This data is an important resource for companies, and current generic SMT engines do not guarantee the security or safeguard the ownership of these assets. In using the Moses decoder, the KantanMT team created a product that could ensure its clients’ data was kept private, and not repurposed or reused in anyway. 


Many global companies have large repositories of bilingual data, however, they often do not wish to deploy and maintain their own version of the Moses decoder. The KantanMT Development team was able to develop the sophisticated Moses SMT technology into a package that could be easily accessible to companies wishing to translate their content, and over time achieve localization cost savings. 


MT STAFF

The current machine translation development team consists of four people, who maintain the platform and build machine translation engines for clients. Due to significant growth in the company over the past year, KantanMT.com will be hiring more staff over the course of the next few months to build engines for clients.


MT SYSTEM INFRASTRUCTURE

Insource or Outsource Moses/Implementation

Based on research, the demands of the language services industry and enterprise machine translation buyers, KantanMT has implemented and customized the Moses decoder in house to create a robust and commercially viable machine translation product that can scale and adapt to our clients’ needs. The original/base KantanAnalytics™ technology was co-developed with the CNGL Centre for Global Intelligent Content, an academic-industry research Centre based in Dublin City University, Ireland. However, all other KantanMT.com technologies have been developed in house by an in house expert development team. 


Number of Engines

As of January 2015, the total number of MT engines built on KantanMT.com by the KantanMT community is 6,777 engines. 


Volumes

As of January 2015, the total number of training words uploaded to the platform by the KantanMT Community has surpassed 50 billion, and the number of translated words on the platform is now more than 600 million


USE SCENARIO

bmmt GmbH is a German language service provider with a strong focus on machine translation. It needed a Machine Translation provider, which would give the bmmt team full control of their Machine Translation training data and MT engine customization process at a low investment point. They also required a system which could correctly handle format-specific tagging and transparent transfer of mark-up information. In early 2013, bmmt joined the KantanMT Community and began testing different customization processes using client specific training data. The team initially experienced minor problems with their SDLXLIFF files. However, the KantanMT development team were able to quickly solve this problem by restructuring some of its tokenizers.  


The company began deploying production engines in mid-2013. These were showing particularly high Quality Evaluation (QE) scores due to the quality of their training data and resulted in a considerable increase in translation productivity. bmmt MT technicians found that domain specificity is a better basis for predictable output than sheer input size. bmmt is currently using approximately 20 KantanMT engines in production across technical and automotive domains. These production ready engines are experiencing high quality metric scores for each language combination.


MARKET POSITIONING

KantanMT.com is one of the market leaders of cloud-based machine translation services. It provides cloud-based SMT services to major global enterprises and software companies wishing to translate large volumes of data. It works directly with companies to develop and implement a long term machine translation strategy, or it works with a select number of language service providers (preferred MT supplier partner program) to supply MT services to large enterprises. 


VIEWS ON CURRENT STATE OF MT

Machine translation is now much more widely accepted in the industry, than it was just a few years ago. Since KantanMT.com entered the market in its testing phase in 2012, we have seen an enormous change in the attitudes and perception of MT in the language community. Access to technology such as smart-phones and tablets in non-English speaking nations has driven the global marketplace, and this in turn has increased the need for on-demand translation services – driving demand for MT services. The MosesCore Project has facilitated this demand with an open source solution that made it possible for smaller companies, and startups like us to compete against bigger MT providers, to solve the problem of language.


QUOTE

“The KantanMT platform sets a new industry benchmark in terms of analytics and development tools used to build and measure the quality of Statistical MT Engines. The KantanMT expert development team has introduced some of the industry’s most exciting and valuable technologies built on the Moses decoder, which are helping language and enterprise clients to translate more efficiently and reduce costs.” KantanMT.com founder and Chief Architect, Tony O’Dowd.


MOSES RESOURCES:


This is a MosesCore project newsletter supported by the European Commission Grant Number 288487 under the 7th Framework Program.


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