IWPT 2021共享任务中的DCU-EPFL增强型依赖关系解析器
The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task
摘要：
我们描述了DCU-EPFL向IWPT 2021共享任务提交的关于解析为增强的通用解析的报告。该任务涉及解析增强的UD图，它是基本依赖树的扩展，旨在更方便地表示语义结构。评估是在17种语言的29个树库上进行的，要求参与者从原始字符串开始解析每种语言的数据。我们的方法使用Stanza管道来预处理文本文件，XLMRoBERTA来获得上下文化的令牌表示，以及边缘评分和标签模型来预测增强的图形。最后，我们运行一个后处理脚本，以确保我们所有的输出都是有效的增强型UD图。我们的系统在9名参与者中排名第6，粗略的增强标记依恋评分（ELAS）为83.57。我们进行额外的截止日期后的实验，其中包括使用Trankit的预处理，XLM-RoBERTa-LARGE，树库连接，和多任务学习之间的基本和增强的依赖关系解析器。所有这些修改都提高了我们的初始分数，我们最终的系统具有88.04的粗略ELAS。
英文摘要：
We describe the DCU-EPFL submission to the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies. The task involves parsing Enhanced UD graphs, which are an extension of the basic dependency trees designed to be more facilitative towards representing semantic structure. Evaluation is carried out on 29 treebanks in 17 languages and participants are required to parse the data from each language starting from raw strings. Our approach uses the Stanza pipeline to preprocess the text files, XLMRoBERTa to obtain contextualized token representations, and an edge-scoring and labeling model to predict the enhanced graph. Finally, we run a post-processing script to ensure all of our outputs are valid Enhanced UD graphs. Our system places 6th out of 9 participants with a coarse Enhanced Labeled Attachment Score (ELAS) of 83.57. We carry out additional post-deadline experiments which include using Trankit for pre-processing, XLM-RoBERTa-LARGE, treebank concatenation, and multitask learning between a basic and an enhanced dependency parser. All of these modifications improve our initial score and our final system has a coarse ELAS of 88.04.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
