  End-to-end sign language generation models do not accurately represent the
prosody in sign language. A lack of temporal and spatial variations leads to
poor-quality generated presentations that confuse human interpreters. In this
paper, we aim to improve the prosody in generated sign languages by modeling
intensification in a data-driven manner. We present different strategies
grounded in linguistics of sign language that inform how intensity modifiers
can be represented in gloss annotations. To employ our strategies, we first
annotate a subset of the benchmark PHOENIX-14T, a German Sign Language dataset,
with different levels of intensification. We then use a supervised intensity
tagger to extend the annotated dataset and obtain labels for the remaining
portion of it. This enhanced dataset is then used to train state-of-the-art
transformer models for sign language generation. We find that our efforts in
intensification modeling yield better results when evaluated with automatic
metrics. Human evaluation also indicates a higher preference of the videos
generated using our model.
