A Simple yet Effective Joint Training Method for Cross-Lingual Universal Dependency Parsing

Danlu Chen, Mengxiao Lin, Zhifeng Hu, Xipeng Qiu

Abstract: This paper describes Fudan’s submission to CoNLL 2018’s shared task Universal Dependency Parsing. We jointly train models when two languages are similar according to linguistic typology and then ensemble the models using a simple re-parse algorithm. We outperform the baseline method by 4.4% (2.1%) on average on development (test) set in CoNLL 2018 UD Shared Task.