Welcome to the UPF Digital Repository

Data-driven sentence generation with non-isomorphic trees

Show simple item record

dc.contributor.author Ballesteros, Miguel
dc.contributor.author Bohnet, Bernd
dc.contributor.author Mille, Simon
dc.contributor.author Wanner, Leo
dc.date.accessioned 2016-12-05T08:36:08Z
dc.date.available 2016-12-05T08:36:08Z
dc.date.issued 2015
dc.identifier.citation Ballesteros M, Bohnet B, Mille S, Wanner L. Data-driven sentence generation with non-isomorphic trees. In: Mihalcea R, Chai J, Anoop S, editors. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; 2015 May 31 - June 5; Denver, Colorado, United States. [Stroudsburg]: ACL; 2015. p. 387-97.
dc.identifier.uri http://hdl.handle.net/10230/27690
dc.description Comunicació presentada a la 2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015), celebrada del 31 de maig al 5 de juny 2015 a Denver (CO, EUA).
dc.description.abstract Abstract structures from which the generation naturally starts often do not contain any functional nodes, while surface-syntactic structures or a chain of tokens in a linearized tree contain all of them. Therefore, data-driven linguistic generation needs to be able to cope with the projection between non-isomorphic structures that differ in their topology and number of nodes. So far, such a projection has been a challenge in data-driven generation/nand was largely avoided. We present a fully stochastic generator that is able to cope with projection between non-isomorphic structures. The generator, which starts from PropBank-like structures, consists of a cascade/nof SVM-classifier based submodules that map in a series of transitions the input structures onto sentences. The generator has been evaluated for English on the Penn-Treebank and for Spanish on the multi-layered AncoraUPF corpus.
dc.description.sponsorship Our work on deep stochastic sentence generation is partially supported by the European Commission under the contract numbers FP7-ICT-610411 (project MULTISENSOR) and H2020-RIA-645012 (project KRISTINA).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher ACL (Association for Computational Linguistics)
dc.relation.ispartof Mihalcea R, Chai J, Anoop S, editors. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; 2015 May 31 - June 5; Denver, Colorado, United States. [Stroudsburg]: ACL; 2015. p. 387-97.
dc.rights © ACL, Creative Commons Attribution 4.0 License
dc.source.uri http://creativecommons.org/licenses/by/4.0/
dc.subject.other Tractament del llenguatge natural (Informàtica)
dc.subject.other Lingüística computacional
dc.title Data-driven sentence generation with non-isomorphic trees
dc.type info:eu-repo/semantics/conferenceObject
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/610411
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account

Statistics

Compliant to Partaking