Title: VQuAnDa: Verbalization QUestion ANswering DAtaset
Authors: Kacupaj, Endri
Zafar, Hamid
Lehmann, Jens
Maleshkova, Maria 
Language: eng
Keywords: Dataset;Knowledge Graph;Question Answering;Verbalization
Issue Date: 27-May-2020
Publisher: Springer
Document Type: Conference Object
Journal / Series / Working Paper (HSU): Lecture Notes in Computer Science
Volume: 12123
Page Start: 531
Page End: 547
Published in (Book): The Semantic Web : 17th International Conference, ESWC 2020
Publisher Place: Berlin
Abstract: 
Question 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.
Organization Units (connected with the publication): Universität Bonn
ISBN: 9783030494605
ISSN: 03029743
Publisher DOI: 10.1007/978-3-030-49461-2_31
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