Data-driven sentence generation with non-isomorphic trees

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• dc.contributor.author Ballesteros, Miguelca
• dc.contributor.author Bohnet, Berndca
• dc.contributor.author Mille, Simonca
• dc.contributor.author Wanner, Leoca
• dc.date.accessioned 2016-12-05T08:36:08Z
• dc.date.available 2016-12-05T08:36:08Z
• dc.date.issued 2015ca
• 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).ca
• 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.en
• 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).en
• dc.format.mimetype application/pdfca
• 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.ca
• dc.identifier.uri http://hdl.handle.net/10230/27690
• dc.language.iso engca
• dc.publisher ACL (Association for Computational Linguistics)ca
• 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.relation.projectID info:eu-repo/grantAgreement/EC/FP7/610411
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012
• dc.rights © 2015 Association for Computational Linguisticsca
• dc.rights.accessRights info:eu-repo/semantics/openAccessca
• dc.subject.other Tractament del llenguatge natural (Informàtica)ca
• dc.subject.other Lingüística computacionalca
• dc.title Data-driven sentence generation with non-isomorphic treesca
• dc.type info:eu-repo/semantics/conferenceObjectca
• dc.type.version info:eu-repo/semantics/publishedVersionca