# Building Korean Sign Language Augmentation (KoSLA) Corpus with Data Augmentation Technique

arXiv: https://arxiv.org/abs/2207.05261 · HTML: https://mlsanity.com/paper/2207.05261/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 12 Jul 2022.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text](https://mlsanity.com/paper/2104.11178/) (2021)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations](https://mlsanity.com/paper/1805.06201/) (2018)
- [Data Augmentation using Pre-trained Transformer Models](https://mlsanity.com/paper/2003.02245/) (2020)
- [Sequence-Level Mixed Sample Data Augmentation](https://mlsanity.com/paper/2011.09039/) (2020)
- [Including Signed Languages in Natural Language Processing](https://mlsanity.com/paper/2105.05222/) (2021)
- [Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech Translation](https://mlsanity.com/paper/2011.00747/) (2020)
- [Generative Data Augmentation for Commonsense Reasoning](https://mlsanity.com/paper/2004.11546/) (2020)
- [Multi-Encoder-Decoder Transformer for Code-Switching Speech Recognition](https://mlsanity.com/paper/2006.10414/) (2020)
- [Text Augmentation in a Multi-Task View](https://mlsanity.com/paper/2101.05469/) (2021)

## Abstract (arXiv)

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.