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

我们应用主动学习来帮助解决手语中的数据稀缺问题，通过对法国手语的预训练分析，我们发现预训练可能可以利用手势的相似性，我们在美国、中国、德国和爱尔兰的拼字语料库上进行了测试，虽然确实从预训练中观察到了好处，但这可能是由于视觉而不是语言上的相似之处。

多语言手指拼写语料库的主动学习

Active Learning for Multilingual Fingerspelling Corpora

Neural-based models have achieved outstanding performance on slot filling and
intent classification, when fairly large in-domain training data are available.
However, as new domains are frequently added, creating sizeable data is
expensive. We show that lightweight augmentation, a set of augmentation methods
involving word span and sentence level operations, alleviates data scarcity
problems. Our experiments on limited data settings show that lightweight
augmentation yields significant performance improvement on slot filling on the
ATIS and SNIPS datasets, and achieves competitive performance with respect to
more complex, state-of-the-art, augmentation approaches. Furthermore,
lightweight augmentation is also beneficial when combined with pre-trained
LM-based models, as it improves BERT-based joint intent and slot filling
models.

研究发现，轻量级数据扩充可以缓解数据稀缺问题，并有效提高 ATIS 和 SNIPS 数据集中的随机填充性能，通过与预训练 LM 模型相结合，还可以改进 BERT-based 联合意图和插槽填充模型的表现。