We present UDify, a multilingual multi-task model capable of accurately
predicting universal part-of-speech, morphological features, lemmas, and
dependency trees simultaneously for all 124 Universal Dependencies treebanks
across 75 languages. By leveraging a multilingual BERT self-attention model
pretrained on 104 languages, we found that fine-tuning it on all datasets
concatenated together with simple softmax classifiers for each UD task can
result in state-of-the-art UPOS, UFeats, Lemmas, UAS, and LAS scores, without
requiring any recurrent or language-specific components. We evaluate UDify for
multilingual learning, showing that low-resource languages benefit the most
from cross-linguistic annotations. We also evaluate for zero-shot learning,
with results suggesting that multilingual training provides strong UD
predictions even for languages that neither UDify nor BERT have ever been
trained on. Code for UDify is available at
this https URL

UDify 是一个多语言多任务模型，可以准确预测 75 种语言中 124 个通用依赖树库的通用词性、形态特征、词形和依赖树，无需任何循环或语言特定组件，并且在跨语言注释方面对低资源语言起到关键作用，并且可以进行零 - shot 学习。