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The ADAPT enhanced dependency parser at the IWPT 2020 shared task

Barry, James orcid logoORCID: 0000-0003-3051-585X, Wagner, Joachim orcid logoORCID: 0000-0002-8290-3849 and Foster, Jennifer orcid logoORCID: 0000-0002-7789-4853 (2020) The ADAPT enhanced dependency parser at the IWPT 2020 shared task. In: 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies, 9 Jul 2020, Online.

Abstract
We describe the ADAPT system for the 2020 IWPT Shared Task on parsing enhanced Universal Dependencies in 17 languages. We implement a pipeline approach using UDPipe and UDPipe-future to provide initial levels of annotation. The enhanced dependency graph is either produced by a graph-based semantic dependency parser or is built from the basic tree using a small set of heuristics. Our results show that, for the majority of languages, a semantic dependency parser can be successfully applied to the task of parsing enhanced dependencies. Unfortunately, we did not ensure a connected graph as part of our pipeline approach and our competition submission relied on a last-minute fix to pass the validation script which harmed our official evaluation scores significantly. Our submission ranked eighth in the official evaluation with a macro-averaged coarse ELAS F1 of 67.23 and a treebank average of 67.49. We later implemented our own graph-connecting fix which resulted in a score of 79.53 (language average) or 79.76 (treebank average), which would have placed fourth in the competition evaluation.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Artificial intelligence
Computer Science > Computational linguistics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies. . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:https://doi.org/10.18653/v1/2020.iwpt-1.24
Copyright Information:© 2020 The Association for Computational Linguistics
Funders:Science Foundation Ireland (SFI) Research Centres Programme (Grant 13/RC/2106, European Regional Development Fund
ID Code:28289
Deposited On:27 Apr 2023 14:41 by Joachim Wagner . Last Modified 27 Apr 2023 14:41
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