Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7403
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dc.contributor.authorLai, Sunnyen_US
dc.contributor.authorLo, Chun Heien_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorLeung, Yeeen_US
dc.date.accessioned2023-02-22T03:16:24Z-
dc.date.available2023-02-22T03:16:24Z-
dc.date.issued2019-
dc.identifier.citationCoNLL 2019 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning, 2020, pp. 104-113en_US
dc.identifier.isbn978-195073760-4-
dc.identifier.urihttps://aclanthology.org/K19-2010-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7403-
dc.description.abstractThis paper describes our system (RESOLVER) submitted to the CoNLL 2019 shared task on Cross-Framework Meaning Representation Parsing (MRP). Our system implements a transition-based parser with a directed acyclic graph (DAG) to tree preprocessor and a novel cross-framework variable-arity resolve action that generalizes over five different representations. Although we ranked low in the competition, we have shown the current limitations and potentials of including variable-arity action in MRP and concluded with directions for improvements in the future.en_US
dc.language.isoenen_US
dc.publisherAssociation for Computational Linguisticsen_US
dc.titleCUHK at MRP 2019: Transition-based parser with cross-framework variable-arity resolve actionen_US
dc.typeConference Paperen_US
dc.relation.conferenceCoNLL 2019 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learningen_US
dc.identifier.doi10.18653/v1/K19-2010-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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