论文标题
Køpsala：通过有效训练和有效编码的基于过渡的图形解析
Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding
论文作者
论文摘要
我们介绍了Køpsala，即IWPT 2020 IWPT共享任务的增强通用依赖性任务的哥本哈根-Uppsala系统。我们的系统是一条管道，该管道包括用于所有事物的现成模型，但增强的图形解析，而对于后者，一种过渡的基于过渡的图形parser parser从Che等人改编而成。 （2019）。我们每种语言训练单个增强的解析器模型，使用黄金句子拆分和代币化进行培训，仅依靠令牌化的表面形式和多语言BERT来编码。根据平均ELAS的数据，虽然提交之前引入的错误导致精度严重下降，但其次要修复程序将使我们在正式排名中排名第四。我们的解析器表明，统一管道对含义表示解析和增强的普遍依赖性都有效。
We present Køpsala, the Copenhagen-Uppsala system for the Enhanced Universal Dependencies Shared Task at IWPT 2020. Our system is a pipeline consisting of off-the-shelf models for everything but enhanced graph parsing, and for the latter, a transition-based graph parser adapted from Che et al. (2019). We train a single enhanced parser model per language, using gold sentence splitting and tokenization for training, and rely only on tokenized surface forms and multilingual BERT for encoding. While a bug introduced just before submission resulted in a severe drop in precision, its post-submission fix would bring us to 4th place in the official ranking, according to average ELAS. Our parser demonstrates that a unified pipeline is effective for both Meaning Representation Parsing and Enhanced Universal Dependencies.
