Before revision: In natural language processing (NLP), cross-lingual transfer learning is as essential as in-domain learning due to the unavailability of annotated resources for low-resource languages. In this paper, we use the ability of a pre-training task that extracts universal features without supervision. We add two pre-training tasks as the auxiliary task into dependency parsing as multi-tasking , which improves the performance of the model in both in-domain and  cross-lingual aspects.
After revision: In natural language processing (NLP), cross-lingual UD parsing framework for transferring parser from only one source monolingual treebank to any other target languages without treebank available. To reach satisfactory parsing accuracy among quite different languages, we introduce two language modeling tasks into dependency parsing as multi-tasking , which improves the performance of the model in both in-domain and cross-lingual aspects.