# KGConv, a Conversational Corpus grounded in Wikidata

arXiv: https://arxiv.org/abs/2308.15298 · HTML: https://mlsanity.com/paper/2308.15298/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 17 of 17)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text](https://mlsanity.com/paper/1809.00782/) (2018)
- [Large-scale Simple Question Answering with Memory Networks](https://mlsanity.com/paper/1506.02075/) (2015)
- [Text-to-Text Pre-Training for Data-to-Text Tasks](https://mlsanity.com/paper/2005.10433/) (2020)
- [Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph](https://mlsanity.com/paper/1801.10314/) (2018)
- [Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion](https://mlsanity.com/paper/1910.03262/) (2019)
- [Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus](https://mlsanity.com/paper/1603.06807/) (2016)
- [A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation](https://mlsanity.com/paper/2203.15108/) (2022)
- [Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types](https://mlsanity.com/paper/1802.06842/) (2018)
- [ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation](https://mlsanity.com/paper/2103.07771/) (2021)
- [Semantic Parsing for Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2301.12217/) (2023)
- [Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases](https://mlsanity.com/paper/2010.03157/) (2020)
- [CoQAR: Question Rewriting on CoQA](https://mlsanity.com/paper/2207.03240/) (2022)

## Built on by (most cited first, 1 of 1)

- [How Well Do Large Language Models Truly Ground?](https://mlsanity.com/paper/2311.09069/) (2023)

## Abstract (arXiv)

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.