论文标题
Biaffine话语依赖解析
Biaffine Discourse Dependency Parsing
论文作者
论文摘要
我们提供了对使用Biaffine模型进行神经话语依赖性解析的研究，与基线解析器相比，进行了显着的性能改善。我们比较了任务中的Eisner算法和Chu-Liu-Edmonds算法，并发现使用Chu-Liu-Edmonds算法会产生更深的树木并实现更好的性能。我们还评估了平均最大路径长度和叶子节点的平均比例的解析器输出的结构，发现解析器产生的依赖树靠近金树。由于语料库允许非注射结构，我们分析了语料库的非注射性的复杂性，并发现该语料库中的依赖性结构最多具有间隙度，而边缘程度最多具有一个。
We provide a study of using the biaffine model for neural discourse dependency parsing and achieve significant performance improvement compared with the baseline parsers. We compare the Eisner algorithm and the Chu-Liu-Edmonds algorithm in the task and find that using the Chu-Liu-Edmonds algorithm generates deeper trees and achieves better performance. We also evaluate the structure of the output of the parser with average maximum path length and average proportion of leaf nodes and find that the dependency trees generated by the parser are close to the gold trees. As the corpus allows non-projective structures, we analyze the complexity of non-projectivity of the corpus and find that the dependency structures in this corpus have gap degree at most one and edge degree at most one.
