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dc.contributor.author
Opedal, Andreas
dc.contributor.author
Tsipidi, Eleftheria
dc.contributor.author
Pimentel, Tiago
dc.contributor.author
Cotterell, Ryan
dc.contributor.author
Vieira, Tim
dc.contributor.editor
Bouamor, Houda
dc.contributor.editor
Pino, Juan
dc.contributor.editor
Bali, Kalika
dc.date.accessioned
2024-01-08T12:46:22Z
dc.date.available
2024-01-07T00:51:47Z
dc.date.available
2024-01-08T12:46:22Z
dc.date.issued
2023-12
dc.identifier.isbn
979-8-89176-060-8
en_US
dc.identifier.other
10.18653/v1/2023.emnlp-main.827
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/650674
dc.identifier.doi
10.3929/ethz-b-000650674
dc.description.abstract
The left-corner transformation (Rosenkrantz and Lewis, 1970) is used to remove left recursion from context-free grammars, which is an important step towards making the grammar parsable top-down with simple techniques. This paper generalizes prior left-corner transformations to support semiring-weighted production rules and to provide finer-grained control over which left corners may be moved. Our generalized left-corner transformation (GLCT) arose from unifying the left-corner transformation and speculation transformation (Eisner and Blatz, 2007), originally for logic programming. Our new transformation and speculation define equivalent weighted languages. Yet, their derivation trees are structurally different in an important way: GLCT replaces left recursion with right recursion, and speculation does not. We also provide several technical results regarding the formal relationships between the outputs of GLCT, speculation, and the original grammar. Lastly, we empirically investigate the efficiency of GLCT for left-recursion elimination from grammars of nine languages. Code: https://github.com/rycolab/left-corner
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Association for Computational Linguistics
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
An Exploration of Left-Corner Transformations
en_US
dc.type
Conference Paper
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.book.title
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
en_US
ethz.pages.start
13393
en_US
ethz.pages.end
13427
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.event
2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)
en_US
ethz.event.location
Singapore
ethz.event.date
December 6-10, 2023
en_US
ethz.publication.place
Stroudsburg, PA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09682 - Cotterell, Ryan / Cotterell, Ryan
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09462 - Hofmann, Thomas / Hofmann, Thomas
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09462 - Hofmann, Thomas / Hofmann, Thomas
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09682 - Cotterell, Ryan / Cotterell, Ryan
en_US
ethz.relation.isSupplementedBy
https://github.com/rycolab/left-corner
ethz.date.deposited
2024-01-07T00:51:47Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2024-01-08T12:47:03Z
ethz.rosetta.lastUpdated
2024-02-03T08:38:22Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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