- Conference Paper
Rights / licenseCreative Commons Attribution 4.0 International
The connection between dependency trees and spanning trees is exploited by the NLP community to train and to decode graph-based dependency parsers. However, the NLP literature has missed an important difference between the two structures: only one edge may emanate from the root in a dependency tree. We analyzed the output of state-of-the-art parsers on many languages from the Universal Dependency Treebank: although these parsers are often able to learn that trees which violate the constraint should be assigned lower probabilities, their ability to do so unsurprisingly de-grades as the size of the training set decreases.In fact, the worst constraint-violation rate we observe is 24%. Prior work has proposed an inefficient algorithm to enforce the constraint, which adds a factor of n to the decoding runtime. We adapt an algorithm due to Gabow and Tarjan (1984) to dependency parsing, which satisfies the constraint without compromising the original runtime. Show more
Book titleProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Pages / Article No.
PublisherAssociation for Computational Linguistics
Organisational unit09682 - Cotterell, Ryan / Cotterell, Ryan
NotesDue to the Coronavirus (COVID-19) the conference was conducted virtually.
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