KGConv, a Conversational Corpus grounded in Wikidata
Quentin Brabant Gwenole Lecorve Lina M. Rojas-Barahona Claire Gardent
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.