SemEval-2016 Task 2: Interpretable semantic textual similarity

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To access the full text documents, please follow this link: http://hdl.handle.net/10230/33533

dc.contributor.author	Agirre, Eneko
dc.contributor.author	Gonzalez Agirre, Aitor
dc.contributor.author	Lopez-Gazpio, Iñigo
dc.contributor.author	Maritxalar, Montserrat
dc.contributor.author	Rigau Claramunt, German
dc.contributor.author	Uria, Larraitz
dc.date	2016
dc.identifier.citation	Agirre E, Gonzalez-Agirre A, Lopez-Gazpio I, Maritxalar M, Rigau G, Uria L. SemEval-2016 Task 2: Interpretable semantic textual similarity. SemEval-2016. 10th International Workshop on Semantic Evaluation; 2016 Jun 16-17; San Diego, CA. Stroudsburg (PA): ACL; 2016. p. 512-24.
dc.identifier.uri	http://hdl.handle.net/10230/33533
dc.format	application/pdf
dc.language.iso	eng
dc.publisher	ACL (Association for Computational Linguistics)
dc.relation	SemEval-2016 Task 2: Interpretable semantic textual similarity. SemEval-2016. 10th International Workshop on Semantic Evaluation; 2016 Jun 16-17; San Diego, CA. Stroudsburg (PA): ACL; 2016. p. 512-24.
dc.relation	info:eu-repo/grantAgreement/ES/1PE/TIN2015-65308-C5-1-R
dc.rights	© ACL, Creative Commons Attribution 4.0 License
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by/4.0/
dc.subject	Lingüística computacional
dc.title	SemEval-2016 Task 2: Interpretable semantic textual similarity
dc.type	info:eu-repo/semantics/conferenceObject
dc.type	info:eu-repo/semantics/publishedVersion
dc.description.abstract	Comunicació presentada al 10th International Workshop on Semantic Evaluation (SemEval-2016), celebrat els dies 16 i 17 de juny de 2016 a San Diego, Califòrnia.
dc.description.abstract	The final goal of Interpretable Semantic Textual Similarity (iSTS) is to build systems that explain which are the differences and commonalities between two sentences. The task adds an explanatory level on top of STS, formalized as an alignment between the chunks in the two input sentences, indicating the relation and similarity score of each alignment. The task provides train and test data on three datasets: news headlines, image captions and student answers. It attracted nine teams, totaling 20 runs. All datasets and the annotation guideline are freely available.
dc.description.abstract	This material is based in part upon work supported a MINECO grant to the University of the Basque Country (TUNER project TIN2015-65308-C5-1-R). Aitor Gonzalez Agirre and Inigo Lopez-Gazpio are by doctoral grants from MINECO. The IXA group is funded by the Basque Government (A type Research Group).

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