论文标题
有效的EUD解析
Efficient EUD Parsing
论文作者
论文摘要
我们介绍了FastParse团队在IWPT 2020年共享任务的系统提交。我们通过专注于效率来参与任务。为此，我们考虑了培训成本和推理效率。我们的模型是蒸馏神经依赖解析器和基于规则的系统的组合，该系统将UD树投射到EUD图中。我们的正式提交的平均ELA为74.04，排名第四。
We present the system submission from the FASTPARSE team for the EUD Shared Task at IWPT 2020. We engaged with the task by focusing on efficiency. For this we considered training costs and inference efficiency. Our models are a combination of distilled neural dependency parsers and a rule-based system that projects UD trees into EUD graphs. We obtained an average ELAS of 74.04 for our official submission, ranking 4th overall.
