Machine Translation Technology

Corporations, businesses and language service providers, have benefited by a flexible approach that is user-centric and provides the highest levels of control, customization and ownership to the users. Our proprietary Deep Adaptive Machine Translation technology ensures you can impact machine learning at different rates and choose how you customize MT engines by client, by domain, by user.

PangeaMT is Pangeanic’s own, independent translation technology division with a clear focus on customized, domain-specific Machine Translation (MT)

Despite our initial statistical roots, we were able to overcome many of Moses shortcomings in order to fit the needs of the translation industry: our solutions go beyond text-based MT and are capable of taking input and producing output in industry-standards, such as TMX and XLIFF. PangeaMT provides API access to CAT tools, so you do not need to change your translation environment: you can benefit from adding your future translations in a virtuous re-training cycle.

Our solutions just avoid having you locked-in by expensive upgrades year after year

 

Another PangeaMT breakthrough is our inline mark-up parser. PangeaMT handles tags extremely efficiently. Statistical machine translation systems (as they come from open sources releases) usually produce plain text output because this is also the format they process. However, we are keen to see PangeaMT solutions in use and adapted to the most demanding language industry requirements. We focused our effort on developing SMT engines capable of handling in-line coding typical of other content formats used in localization production environments. Thanks to this parser, PangeaMT can identify in-lines without attempting to translate them, and it places them back in the resulting text, too. An in-line placeholder acts first by copying and transferring all XML and code information to a separate module. The translation engine does its work and then places the in-line back into the translated segment. At the time of its release, our in-line parser constituted an innovation well-above the current level of maturity of well-known SMT systems. We keep learning and improving with every development commissioned by an existing or new client and language combination. We therefore remain open as to apply new hybridization techniques, even ad-hoc rules, that we research and implement ourselves or co-develop in conjunction with our clients. We are aware of the fact that for some language combinations it will be necessary to resort to some linguistic-informative techniques that will be part of the pre- or post-processing phases. Right word and phrase reordering in the MT output is not an easy goal to achieve, especially when the languages involved are not closely linked from a linguistic family standpoint, or when one of the two languages is a really flexible and so MT-challenging word order (WO). Some language-specific fixing procedures may come handy. In some other cases, it may be useful to use one language as pivot to train engines in languages that are not close. These and other techniques may be used or taken as a basis for expanding our PangeaMT solution palette. Please visit our machine translation division website to learn more about PangeaMT.