We present our contribution to the SIGMORPHON 2019 Shared Task:
Crosslinguality and Context in Morphology, Task 2: contextual morphological
analysis and lemmatization.
We submitted a modification of the UDPipe 2.0, one of best-performing systems
of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal
Dependencies and an overall winner of the The 2018 Shared Task on Extrinsic
Parser Evaluation.
As our first improvement, we use the pretrained contextualized embeddings
(BERT) as additional inputs to the network; secondly, we use individual
morphological features as regularization; and finally, we merge the selected
corpora of the same language.
In the lemmatization task, our system exceeds all the submitted systems by a
wide margin with lemmatization accuracy 95.78 (second best was 95.00, third
94.46). In the morphological analysis, our system placed tightly second: our
morphological analysis accuracy was 93.19, the winning system's 93.23.

我们在 SIGMORPHON2019 共享任务中对 UDPip2.0 进行修改，使用预训练的上下文化嵌入 (BERT) 作为网络的附加输入，使用单独的形态特征作为正则化，并合并了同一语言的选定语料库，在词形还原任务中，我们的系统的准确性达到了 95.78，超过所有提交的系统，且在词态分析方面取得了 93.19 的准确度，是一个非常有前途的研究。