Biaffine Discourse Dependency Parsing
- URL: http://arxiv.org/abs/2201.04450v1
- Date: Wed, 12 Jan 2022 12:56:13 GMT
- Title: Biaffine Discourse Dependency Parsing
- Authors: Yingxue Fu
- Abstract summary: We use the biaffine model for neural discourse dependency parsing and achieve significant performance improvement compared with the baselines.
We compare the Eisner algorithm and the Chu-Liu-Edmonds algorithm in the task and find that using the Chu-Liu-Edmonds generates deeper trees.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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