论文标题
IWPT 2020共享任务的改编增强依赖解析器
The ADAPT Enhanced Dependency Parser at the IWPT 2020 Shared Task
论文作者
论文摘要
我们描述了2020年IWPT共享任务的适应系统，以解析17种语言中增强的通用依赖性。我们使用udpipe和udpipe-future实施管道方法，以提供注释的初始水平。增强的依赖图是由基于图的语义依赖解析器产生的，或者是使用一小部分启发式方法从基本树构建的。我们的结果表明，对于大多数语言，语义依赖解析器可以成功地应用于解析增强依赖性的任务。 不幸的是，我们没有确保连接的图作为管道方法的一部分，而我们的竞争提交则依靠最后一刻的修复程序来传递验证脚本，这损害了我们的官方评估得分。我们的提交在官方评估中排名第八，宏观平均的粗Elas F1为67.23，Treebank平均为67.49。后来，我们实施了自己的图形连接修复程序，该修复程序的得分为79.53（语言平均）或79.76（Treebank平均水平），这将在比赛评估中排名第四。
We describe the ADAPT system for the 2020 IWPT Shared Task on parsing enhanced Universal Dependencies in 17 languages. We implement a pipeline approach using UDPipe and UDPipe-future to provide initial levels of annotation. The enhanced dependency graph is either produced by a graph-based semantic dependency parser or is built from the basic tree using a small set of heuristics. Our results show that, for the majority of languages, a semantic dependency parser can be successfully applied to the task of parsing enhanced dependencies. Unfortunately, we did not ensure a connected graph as part of our pipeline approach and our competition submission relied on a last-minute fix to pass the validation script which harmed our official evaluation scores significantly. Our submission ranked eighth in the official evaluation with a macro-averaged coarse ELAS F1 of 67.23 and a treebank average of 67.49. We later implemented our own graph-connecting fix which resulted in a score of 79.53 (language average) or 79.76 (treebank average), which would have placed fourth in the competition evaluation.
