Carlos Amaral


2024

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plain XAI Supported Multilingual Video Workflow Platform
Carlos Amaral | Catarina Lagrifa | Mirko Lorenz | Peggy van der Kreeft | Tiago Veiga
Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 2

2022

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plain X - AI Supported Multilingual Video Workflow Platform
Carlos Amaral | Peggy van der Kreeft
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

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.

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Monitio - Large Scale MT for Multilingual Media Monitoring
Carlos Amaral | Sebastião Miranda
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

Monitio is a real-time crosslingual global media monitoring platform which delivers actionable insights beyond human scale and capabilities. Our system continuously ingests a massive number of multilingual data sources that are automatically translated, filtered and categorized to generate intelligence reports specially geared towards media monitoring professionals’ needs.

2004

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Design and Implementation of a Semantic Search Engine for Portuguese
Carlos Amaral | Dominique Laurent | André Martins | Afonso Mendes | Cláudia Pinto
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)