Recurrent neural network grammars

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

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dc.contributor.author	Dyer, Chris
dc.contributor.author	Kuncoro, Adhiguna
dc.contributor.author	Ballesteros, Miguel
dc.contributor.author	Smith, Noah A.
dc.date.accessioned	2016-12-12T10:24:20Z
dc.date.available	2016-12-12T10:24:20Z
dc.date.issued	2016
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.
dc.identifier.uri	http://hdl.handle.net/10230/27726
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.
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.
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).
dc.format.mimetype	application/pdf
dc.language.iso	eng
dc.publisher	ACL (Association for Computational Linguistics)
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.rights	© ACL, Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/3.0/
dc.subject.other	Tractament del llenguatge natural (Informàtica)
dc.subject.other	Lingüística computacional
dc.title	Recurrent neural network grammars
dc.type	info:eu-repo/semantics/conferenceObject
dc.relation.projectID	info:eu-repo/grantAgreement/EC/H2020/645012
dc.relation.projectID	info:eu-repo/grantAgreement/EC/FP7/610411
dc.rights.accessRights	info:eu-repo/semantics/openAccess
dc.type.version	info:eu-repo/semantics/publishedVersion