Neural Translation of Sign Language

Betreuer/in: Sarah Ebling, Mathias Müller


The aim of this project is to conduct research into neural machine translation (NMT) between sign language and spoken language text. You will be involved in preparing an existing sign language dataset for use in NMT and performing NMT experiments.

Aim and Purpose

Important contributions will include:

  • Deriving suitable representations for capturing the multilinear nature of sign languages
  • Training NMT models using state-of-the-art frameworks


Completion of the following lectures:

  • Machine Translation
  • Advanced Techniques of Machine Translation


Danielle Bragg, Oscar Koller, Mary Bellard, Larwan Berke, Patrick Boudrealt, Annelies Braffort, Naomi Caselli, Matt Huenerfauth, Hernisa Kacorri, Tessa Verhoef, Christian Vogler, and Meredith Ringel Morris. Sign language recognition, generation, and translation: An interdisciplinary perspective. In ASSETS 2019. ACM, October 2019.

Necati Cihan Camgöz, Simon Hadfield, Oscar Koller, Hermann Ney, and Richard Bowden. Neural Sign Language Translation. In Proceedings of CVPR 2018, 2018.