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Harry Potter and the Action Prediction Challenge from Natural Language
dc.contributor.author | Vilares, David | |
dc.contributor.author | Gómez-Rodríguez, Carlos | |
dc.date.accessioned | 2024-05-28T12:19:28Z | |
dc.date.available | 2024-05-28T12:19:28Z | |
dc.date.issued | 2019-06 | |
dc.identifier.citation | David Vilares and Carlos Gómez-Rodríguez. 2019. Harry Potter and the Action Prediction Challenge from Natural Language. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2124–2130, Minneapolis, Minnesota. Association for Computational Linguistics. | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/36674 | |
dc.description | The 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2019) was held in Minneapolis from June 2nd to June 7th, 2019. | es_ES |
dc.description.abstract | [Absctract]: We explore the challenge of action prediction from textual descriptions of scenes, a testbed to approximate whether text inference can be used to predict upcoming actions. As a case of study, we consider the world of the Harry Potter fantasy novels and inferring what spell will be cast next given a fragment of a story. Spells act as keywords that abstract actions (e.g. ‘Alohomora’ to open a door) and denote a response to the environment. This idea is used to automatically build HPAC, a corpus containing 82,836 samples and 85 actions. We then evaluate different baselines. Among the tested models, an LSTM-based approach obtains the best performance for frequent actions and large scene descriptions, but approaches such as logistic regression behave well on infrequent actions. | es_ES |
dc.description.sponsorship | This work has received support from the TELEPARES-UDC project (FFI2014-51978-C2- 2-R) and the ANSWER-ASAP project (TIN2017- 85160-C2-1-R) from MINECO, and from Xunta de Galicia (ED431B 2017/01), and from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150) | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431B 2017/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Association for Computational Linguistics | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/714150 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FFI2014-51978-C2-2-R/ES/TECNOLOGÍAS DE LA LENGUA PARA ANÁLISIS DE OPINIONES EN REDES SOCIALES: DEL TEXTO AL MICROTEXTO | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-85160-C2-1-R/ES/AVANCES EN NUEVOS SISTEMAS DE EXTRACCION DE RESPUESTAS CON ANALISIS SEMANTICO Y APRENDIZAJE PROFUNDO | es_ES |
dc.relation.uri | https://aclanthology.org/N19-1218/ | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Action prediction | es_ES |
dc.subject | Natural language inference | es_ES |
dc.subject | Textual descriptions | es_ES |
dc.subject | Text-Based Action Prediction in Narrative Text | es_ES |
dc.title | Harry Potter and the Action Prediction Challenge from Natural Language | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) | es_ES |
UDC.startPage | 2124 | es_ES |
UDC.endPage | 2130 | es_ES |
UDC.conferenceTitle | 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2019) | es_ES |
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