The plain X platform is a toolbox for multilingual adaptation, for video, audio, and text content. The software is a 4-in-1 tool, combining several steps in the adaptation process, i.e., transcription, translation, subtitling, and voice-over, all automatically generated, but with a high level of editorial control. Users can choose which translation engine is used (e.g., MS Azure, Google, DeepL) depending on best performance. As a result, plain X enables a smooth semi-automated production of subtitles or voice-over, much faster than with older, manual workflows. The software was developed out of EU research projects and has recently been rolled out for professional use. It brings Artificial Intelligence (AI) into the multilingual media production process, while keeping the human in the loop.

The GoURMET project, funded by the European Commission’s H2020 program (under grant agreement 825299), develops models for machine translation, in particular for low-resourced languages. Data, models and software releases as well as the GoURMET Translate Tool are made available as open source.

In the media industry and the focus of global reporting can shift overnight. There is a compelling need to be able to develop new machine translation systems in a short period of time and in order to more efficiently cover quickly developing stories. As part of the EU project GoURMET and which focusses on low-resource machine translation and our media partners selected a surprise language for which a machine translation system had to be built and evaluated in two months(February and March 2021). The language selected was Pashto and an Indo-Iranian language spoken in Afghanistan and Pakistan and India. In this period we completed the full pipeline of development of a neural machine translation system: data crawling and cleaning and aligning and creating test sets and developing and testing models and and delivering them to the user partners. In this paperwe describe rapid data creation and experiments with transfer learning and pretraining for this low-resource language pair. We find that starting from an existing large model pre-trained on 50languages leads to far better BLEU scores than pretraining on one high-resource language pair with a smaller model. We also present human evaluation of our systems and which indicates that the resulting systems perform better than a freely available commercial system when translating from English into Pashto direction and and similarly when translating from Pashto into English.

We present the latest version of the SUMMA platform, an open-source software platform for monitoring and interpreting multi-lingual media, from written news published on the internet to live media broadcasts via satellite or internet streaming.

news.bridge provides a platform for multilingual video processing, including automated transcription and translation, subtitling, voice-over, and summarization, with post-editing facility of videos in a broad range of languages. The platform is currently in beta testing at Deutsche Welle for republishing of videos in other languages.

We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.