Recurrent neural network grammars

Dyer, Chris; Kuncoro, Adhiguna; Ballesteros, Miguel; Smith, Noah A.

Recurrent neural network grammars

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dc.contributor.author Dyer, Chrisca
dc.contributor.author Kuncoro, Adhigunaca
dc.contributor.author Ballesteros, Miguelca
dc.contributor.author Smith, Noah A.ca
dc.date.accessioned 2016-12-12T10:24:20Z
dc.date.available 2016-12-12T10:24:20Z
dc.date.issued 2016ca
dc.description Comunicació presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 de juny 2016.ca
dc.description.abstract We introduce recurrent neural network grammars,/nprobabilistic models of sentences with/nexplicit phrase structure. We explain efficient/ninference procedures that allow application to/nboth parsing and language modeling. Experiments/nshow that they provide better parsing in/nEnglish than any single previously published/nsupervised generative model and better language/nmodeling than state-of-the-art sequential/nRNNs in English and Chinese.en
dc.description.sponsorship This work was sponsored in part by the Defense/nAdvanced Research Projects Agency (DARPA)/nInformation Innovation Office (I2O) under the/nLow Resource Languages for Emergent Incidents/n(LORELEI) program issued by DARPA/I2O under/nContract No. HR0011-15-C-0114; it was also supported/nin part by Contract No. W911NF-15-1-0543/nwith the DARPA and the Army Research Office/n(ARO). Approved for public release, distribution/nunlimited. The views expressed are those of the authors/nand do not reflect the official policy or position/nof the Department of Defense or the U.S. Government./nMiguel Ballesteros was supported by the/nEuropean Commission under the contract numbers/nFP7-ICT-610411 (project MULTISENSOR) and/nH2020-RIA-645012 (project KRISTINA).en
dc.format.mimetype application/pdfca
dc.identifier.citation Dyer C, Kuncoro A, Ballesteros M, Smith NA. Recurrent neural network grammars. In: Knight K, Lopez A, Mitchell M, editors. Human Language Technologies. 2016 Conference of the North American Chapter of the Association for Computational Linguistics; 2016 June 12-17; San Diego (CA, USA). [place unknown]: Association for Computational Linguistics (ACL); 2016. p. 199-209.ca
dc.identifier.uri http://hdl.handle.net/10230/27726
dc.language.iso engca
dc.publisher ACL (Association for Computational Linguistics)ca
dc.relation.ispartof Knight K, Lopez A, Mitchell M, editors. Human Language Technologies. 2016 Conference of the North American Chapter of the Association for Computational Linguistics; 2016 June 12-17; San Diego (CA, USA). [S.l.]: Association for Computational Linguistics (ACL); 2016. p. 199-209.
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012ca
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/610411
dc.rights.accessRights info:eu-repo/semantics/openAccessca
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/
dc.subject.other Tractament del llenguatge natural (Informàtica)ca
dc.subject.other Lingüística computacionalca
dc.title Recurrent neural network grammarsca
dc.type info:eu-repo/semantics/conferenceObjectca
dc.type.version info:eu-repo/semantics/publishedVersionca

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