Recent advances in multilingual dependency parsing have brought the idea of a
truly universal parser closer to reality. However, cross-language interference
and restrained model capacity remain major obstacles. To address this, we
propose a novel multilingual task adaptation approach based on contextual
parameter generation and adapter modules. This approach enables to learn
adapters via language embeddings while sharing model parameters across
languages. It also allows for an easy but effective integration of existing
linguistic typology features into the parsing network. The resulting parser,
UDapter, outperforms strong monolingual and multilingual baselines on the
majority of both high-resource and low-resource (zero-shot) languages, showing
the success of the proposed adaptation approach. Our in-depth analyses show
that soft parameter sharing via typological features is key to this success.

该研究提出了一种基于文本参数生成和适配器模块的新型多语言任务适应方法，该方法通过语言嵌入来学习适配器，同时在各种语言之间共享模型参数，可有效地集成现有的语言类型学特征到分析网络中，并在高资源和低资源语言中均显着优于强的单语言和多语言基线，这表明了所提出的适应方法的成功。