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This paper describes Microsoft's submission to the first shared task on signlanguage translation at WMT 2022, a public competition tackling sign languageto spoken language translation for Swiss German sign language. The task is verychallenging due to data scarcity and an unprecedented vocabulary size of morethan 20k words on the target side. Moreover, the data is taken from realbroadcast news, includes native signing and covers scenarios of long videos.Motivated by recent advances in action recognition, we incorporate full bodyinformation by extracting features from a pre-trained I3D model and applying astandard transformer network. The accuracy of the system is further improved byapplying careful data cleaning on the target text. We obtain BLEU scores of 0.6and 0.78 on the test and dev set respectively, which is the best score amongthe participants of the shared task. Also in the human evaluation thesubmission reaches the first place. The BLEU score is further improved to 1.08on the dev set by applying features extracted from a lip reading model.

Clean Text and Full-Body Transformer: Microsoft's Submission to the WMT22 Shared Task on Sign Language Translation