Active Learning for Multilingual Fingerspelling Corpora
This work addresses data scarcity for sign language processing, but it is incremental as it builds on existing active learning methods with a novel analysis.
The study tackled data scarcity in sign languages by applying active learning and analyzing the effect of pre-training across multiple fingerspelling corpora, finding a benefit that may stem from visual similarities rather than linguistic ones.
We apply active learning to help with data scarcity problems in sign languages. In particular, we perform a novel analysis of the effect of pre-training. Since many sign languages are linguistic descendants of French sign language, they share hand configurations, which pre-training can hopefully exploit. We test this hypothesis on American, Chinese, German, and Irish fingerspelling corpora. We do observe a benefit from pre-training, but this may be due to visual rather than linguistic similarities