Biaffine Discourse Dependency Parsing
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Dependency ParsingSimilar Papers 제목 키워드 기반
Biaffine Discourse Dependency Parsing
We provide the first 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 C…
Dependency ParsingMulti-layer Biaffine Model for Neural Dependency Parsing
The biaffine model is a strong and efficient model for graph-based dependency parsing. However, previous work only used the biaffine method in single-layer form. In this paper, we propose a multi-layer biaffine model for…
Dependency ParsingFormMulti-Layer Pseudo-Siamese Biaffine Model for Dependency Parsing
Biaffine method is a strong and efficient method for graph-based dependency parsing. However, previous work only used the biaffine method at the end of the dependency parser as a scorer, and its application in multi-laye…
ARCDependency ParsingFormEnd-to-End Argument Mining as Biaffine Dependency Parsing
Non-neural approaches to argument mining (AM) are often pipelined and require heavy feature-engineering. In this paper, we propose a neural end-to-end approach to AM which is based on dependency parsing, in contrast to t…
Argument MiningDependency ParsingFeature EngineeringRelation+1Dependency Parsing is More Parameter-Efficient with Normalization
Dependency parsing is the task of inferring natural language structure, often approached by modeling word interactions via attention through biaffine scoring. This mechanism works like self-attention in Transformers, whe…
Dependency ParsingSentence