@inproceedings{brabant-etal-2024-kgconv-conversational,
title = "{KGC}onv, a Conversational Corpus Grounded in {W}ikidata",
author = "Brabant, Quentin and
Rojas Barahona, Lina M. and
Lecorv{\'e}, Gw{\'e}nol{\'e} and
Gardent, Claire",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.850",
pages = "9732--9742",
abstract = "We present KGConv, a large corpus of 71k English 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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<abstract>We present KGConv, a large corpus of 71k English 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.</abstract>
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%0 Conference Proceedings
%T KGConv, a Conversational Corpus Grounded in Wikidata
%A Brabant, Quentin
%A Rojas Barahona, Lina M.
%A Lecorvé, Gwénolé
%A Gardent, Claire
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F brabant-etal-2024-kgconv-conversational
%X We present KGConv, a large corpus of 71k English 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.
%U https://aclanthology.org/2024.lrec-main.850
%P 9732-9742
Markdown (Informal)
[KGConv, a Conversational Corpus Grounded in Wikidata](https://aclanthology.org/2024.lrec-main.850) (Brabant et al., LREC-COLING 2024)
ACL
- Quentin Brabant, Lina M. Rojas Barahona, Gwénolé Lecorvé, and Claire Gardent. 2024. KGConv, a Conversational Corpus Grounded in Wikidata. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9732–9742, Torino, Italia. ELRA and ICCL.