Publication:
VQuAnDa: Verbalization QUestion ANswering DAtaset

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cris.virtual.departmentData Engineering
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cris.virtual.departmentbrowseData Engineering
cris.virtual.departmentbrowseData Engineering
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dc.contributor.authorKacupaj, Endri
dc.contributor.authorZafar, Hamid
dc.contributor.authorLehmann, Jens
dc.contributor.authorMaleshkova, Maria
dc.date.issued2020-05-27
dc.description.abstractQuestion Answering (QA) systems over Knowledge Graphs (KGs) aim to provide a concise answer to a given natural language question. Despite the significant evolution of QA methods over the past years, there are still some core lines of work, which are lagging behind. This is especially true for methods and datasets that support the verbalization of answers in natural language. Specifically, to the best of our knowledge, none of the existing Question Answering datasets provide any verbalization data for the question-query pairs. Hence, we aim to fill this gap by providing the first QA dataset VQuAnDa that includes the verbalization of each answer. We base VQuAnDa on a commonly used large-scale QA dataset – LC-QuAD, in order to support compatibility and continuity of previous work. We complement the dataset with baseline scores for measuring future training and evaluation work, by using a set of standard sequence to sequence models and sharing the results of the experiments. This resource empowers researchers to train and evaluate a variety of models to generate answer verbalizations.
dc.description.versionNA
dc.identifier.doi10.1007/978-3-030-49461-2_31
dc.identifier.eissn1611-3349
dc.identifier.isbn9783030494605
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-85086138779
dc.identifier.urihttps://openhsu.ub.hsu-hh.de/handle/10.24405/15235
dc.language.isoen
dc.publisherSpringer
dc.relation.journalLecture Notes in Computer Science
dc.relation.orgunitUniversität Bonn
dc.rights.accessRightsmetadata only access
dc.subjectDataset
dc.subjectKnowledge Graph
dc.subjectQuestion Answering
dc.subjectVerbalization
dc.titleVQuAnDa: Verbalization QUestion ANswering DAtaset
dc.typeConference paper
dcterms.bibliographicCitation.booktitleThe Semantic Web : 17th International Conference, ESWC 2020
dcterms.bibliographicCitation.originalpublisherplaceBerlin
dspace.entity.typePublication
hsu.peerReviewed
hsu.uniBibliographyNein
oaire.citation.endPage547
oaire.citation.startPage531
oaire.citation.volume12123
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