  We present an efficient framework of corpus for sign language translation.
Aided with a simple but dramatic data augmentation technique, our method
converts text into annotated forms with minimum information loss. Sign
languages are composed of manual signals, non-manual signals, and iconic
features. According to professional sign language interpreters, non-manual
signals such as facial expressions and gestures play an important role in
conveying exact meaning. By considering the linguistic features of sign
language, our proposed framework is a first and unique attempt to build a
multimodal sign language augmentation corpus (hereinafter referred to as the
KoSLA corpus) containing both manual and non-manual modalities. The corpus we
built demonstrates confident results in the hospital context, showing improved
performance with augmented datasets. To overcome data scarcity, we resorted to
data augmentation techniques such as synonym replacement to boost the
efficiency of our translation model and available data, while maintaining
grammatical and semantic structures of sign language. For the experimental
support, we verify the effectiveness of data augmentation technique and
usefulness of our corpus by performing a translation task between normal
sentences and sign language annotations on two tokenizers. The result was
convincing, proving that the BLEU scores with the KoSLA corpus were
significant.
