KGConv, a Conversational Corpus grounded in Wikidata

Quentin Brabant, Gwenole Lecorve, Lina M. Rojas-Barahona, Claire Gardent
8/29/2023
cs.CLcs.AI
benchmark-dataset

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Abstract

We present KGConv, a large, conversational corpus of 71k conversations where each question-answer pair is grounded in a Wikidata fact. Conversations contain on average 8.6 questions and for each Wikidata fact, we provide multiple variants (12 on average) of the corresponding question using templates, human annotations, hand-crafted rules and a question rewriting neural model. We provide baselines for the task of Knowledge-Based, Conversational Question Generation. KGConv can further be used for other generation and analysis tasks such as single-turn question generation from Wikidata triples, question rewriting, question answering from conversation or from knowledge graphs and quiz generation.

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Cite this paper

@article{brabant2023kgconv,
  title  = {KGConv, a Conversational Corpus grounded in Wikidata},
  author = {Quentin Brabant and Gwenole Lecorve and Lina M. Rojas-Barahona and Claire Gardent},
  year   = {2023},
  eprint = {2308.15298},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.15298v1}
}

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