configs:
- config_name: labels
data_files: data/labels.json
- config_name: templates
data_files: data/templates.json
- config_name: conversations.country
data_files:
- path: data/country/test.json
split: test
- path: data/country/dev.json
split: dev
- path: data/country/train.json
split: train
- config_name: conversations.historical_event
data_files:
- path: data/historical_event/test.json
split: test
- path: data/historical_event/dev.json
split: dev
- path: data/historical_event/train.json
split: train
- config_name: conversations.food
data_files:
- path: data/food/test.json
split: test
- path: data/food/dev.json
split: dev
- path: data/food/train.json
split: train
- config_name: conversations.space_object
data_files:
- path: data/space_object/test.json
split: test
- config_name: conversations.with_unseen_properties
data_files:
- path: data/with_unseen_properties/test.json
split: test
- config_name: conversations.taxon
data_files:
- path: data/taxon/test.json
split: test
- config_name: conversations.person
data_files:
- path: data/person/test.json
split: test
- path: data/person/dev.json
split: dev
- path: data/person/train.json
split: train
- config_name: conversations.ideology
data_files:
- path: data/ideology/test.json
split: test
- path: data/ideology/dev.json
split: dev
- path: data/ideology/train.json
split: train
- config_name: conversations.molecular_entity
data_files:
- path: data/molecular_entity/test.json
split: test
- path: data/molecular_entity/dev.json
split: dev
- path: data/molecular_entity/train.json
split: train
KGConv, a Conversational Corpus grounded in Wikidata
Table of Contents
Dataset Description
- Repository: https://github.com/Orange-OpenSource/KGConv/
- Paper: https://arxiv.org/abs/2308.15298
- Point of Contact: quentin.brabant@orange.com, gwenole.lecorve@orange.com, linamaria.rojasbarahona@orange.com, claire.gardent@loria.fr
Dataset Summary
KGConv is a large corpus of 71k english conversations where each question-answer pair is grounded in a Wikidata fact. The conversations were generated automatically: in particular, questions were created using a collection of 10,355 templates; subsequently, the naturalness of conversations was improved by inserting ellipses and coreference into questions, via both handcrafted rules and a generative rewriting model. The dataset thus provides several variants of each question (12 on average), organized into 3 levels of conversationality. 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.
Languages
English.
Dataset Structure
The dataset has three components:
- conversation configs, divided in several themes that correspond to configs of the form
conversations.theme
, where theme has to be replaced by one of the following: country, food, historical_event, ideology, molecular_entity, person, space_object, taxon, with_unseen_properties; - labels, a config that contains labels for all entities and properties involved in the conversations;
- templates, a config that contains the templates that where used for generating questions.
Data Instances
Instance from the configs with name of the form "conversations.theme" (e.g. "conversations.country") have the following form:
{
"conversation_id": "69795",
"root_neighbourhood": [
[
"Q6138903",
"P106",
"Q82955"
],
[
"Q6138903",
"P19",
"Q3408680"
],
...
],
"conversation": [
{
"triple": [
"Q691",
"P30",
"Q538"
],
"question variants": [
{
"out-of-context": "In which continent is Papua New Guinea located?",
"in-context": "In which continent is Papua New Guinea located?",
"in-context subject ref": "Papua New Guinea",
"synthetic-in-context": "In which continent is Papua New Guinea located?"
},
{
"out-of-context": "In what continent is Papua New Guinea in?",
"in-context": "In what continent is Papua New Guinea in?",
"in-context subject ref": "Papua New Guinea",
"synthetic-in-context": "In what continent is Papua New Guinea in?"
},
...
],
"answer": "Oceania"
},
{
"triple": [
"Q691",
"P38",
"Q200759"
],
"question variants": [
{
"out-of-context": "What is accepted as the currency of Papua New Guinea?",
"in-context": "What is accepted as the currency of Papua New Guinea?",
"in-context subject ref": "Papua New Guinea",
"synthetic-in-context": "What is accepted as the currency?"
},
{
"out-of-context": "What is the currency of Papua New Guinea?",
"in-context": "What is the currency of Papua New Guinea?",
"in-context subject ref": "Papua New Guinea",
"synthetic-in-context": "What is the currency?"
},
...
],
"answer": "kina"
},
...
Instances from the labels
config are like this:
{
"entity": "Q39",
"labels": [
"Swiss Confederation",
"CHE",
"Confoederatio Helvetica",
"Swiss",
"Schweiz",
"SUI",
"Switzerland",
"CH",
"Suisse",
"Svizzera"
],
"preferred_label": "Switzerland"
}
Instances from the templates
config are as follows.
{
"template_key": {
"p": "P1201",
"s_types": [
"Q149918"
],
"o_types": []
},
"templates": [
{
"left": "what is the space tug of ",
"right": "?",
"source": "interface:automatic labeler"
},
{
"left": "what was the space tug of ",
"right": "?",
"source": "interface:624dc1cd4432b5035ba082df"
},
...
]
}
Data Fields
The fields from the configs with name of the form "conversations.theme" (e.g. "conversations.country") are the following:
conversation
: list of dicts; each dict reprensent one question+answer and has the following fields:conversation_id
: stringroot_neighbourhood
: list of triples (each triple is itself represented by a list of 3 string elements) that constitute the neighbourhood of the conversation root entity in the knowledge graph (see the LREC publication for more details)triple
: triple on which the question is based (list of three string elements)question variants
: list of dict; each dict contain several forms of a question obtained via a given template (see the LREC publication for more details)out-of-context
: one form of the question variantin-context
: another form of the question variantin-context subject ref
: how the subject is referred to in the in-context formsynthetic-in-context
: yet another form of the question variant
answer
: answer to the question (string)
The fields from the labels
config are the following:
entity
: string, id of the entitylabels
: list of stringspreferred_label
: string
The fields from the templates
config are the following:
template_key
: a dict containing the conditions for using the templates listed intemplates
, with the following fields:p
: id of the propertys_types
: required types for subjecto_types
: require types for object
templates
: list of dicts representing templates; each dict has the following fields:left
: left part of the templateright
: right part of the templatesource
: origin of the template (string)
Additional Information
Licensing Information
This software is distributed under the Creative Commons Attribution 4.0 International, the text of which is available at https://spdx.org/licenses/CC-BY-4.0.html or see the "license.txt" file for more details.
Citation Information
@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},
primaryClass={cs.CL}
}