论文标题
通过二阶推断和训练数据的混合物增强了通用依赖性解析
Enhanced Universal Dependency Parsing with Second-Order Inference and Mixture of Training Data
论文作者
论文摘要
本文介绍了我们提交给\ textit {iwpt 2020共享任务}的系统。我们的系统是具有二阶推断的基于图的解析器。对于低资源的泰米尔语料库，我们特别将泰米尔语的训练数据与其他语言混合在一起，并显着改善了泰米尔语的表现。由于我们对提交要求的误解，我们提交了未连接的图表，这使我们的系统仅在10个团队中排名\ TextBf {6th}。但是，在解决此问题之后，我们的系统比在官方结果中排名\ TextBf {1st}的团队高0.6 ELA。
This paper presents the system used in our submission to the \textit{IWPT 2020 Shared Task}. Our system is a graph-based parser with second-order inference. For the low-resource Tamil corpus, we specially mixed the training data of Tamil with other languages and significantly improved the performance of Tamil. Due to our misunderstanding of the submission requirements, we submitted graphs that are not connected, which makes our system only rank \textbf{6th} over 10 teams. However, after we fixed this problem, our system is 0.6 ELAS higher than the team that ranked \textbf{1st} in the official results.
