James Turner


2024

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Exploring NMT Explainability for Translators Using NMT Visualising Tools
Gabriela Gonzalez-Saez | Mariam Nakhle | James Turner | Fabien Lopez | Nicolas Ballier | Marco Dinarelli | Emmanuelle Esperança-Rodier | Sui He | Raheel Qader | Caroline Rossi | Didier Schwab | Jun Yang
Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)

This paper describes work in progress on Visualisation tools to foster collaborations between translators and computational scientists. We aim to describe how visualisation features can be used to explain translation and NMT outputs. We tested several visualisation functionalities with three NMT models based on Chinese-English, Spanish-English and French-English language pairs. We created three demos containing different visualisation tools and analysed them within the framework of performance-explainability, focusing on the translator’s perspective.

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The MAKE-NMTViz Project: Meaningful, Accurate and Knowledge-limited Explanations of NMT Systems for Translators
Gabriela Gonzalez-Saez | Fabien Lopez | Mariam Nakhle | James Turner | Nicolas Ballier | Marco Dinarelli | Emmanuelle Esperança-Rodier | Sui He | Caroline Rossi | Didier Schwab | Jun Yang
Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)

This paper describes MAKE-NMTViz, a project designed to help translators visualize neural machine translation outputs using explainable artificial intelligence visualization tools initially developed for computer vision.