chitchat-dataset
Open-domain conversational dataset from the BYU Perception, Control & Cognition lab's Chit-Chat Challenge.
install
pip3 install chitchat_datasetor simply download the raw dataset:
curl -LO https://raw.githubusercontent.com/BYU-PCCL/chitchat-dataset/master/chitchat_dataset/dataset.jsonusage
import chitchat_dataset as ccc
dataset = ccc.Dataset()
# Dataset is a subclass of dict()
for convo_id, convo in dataset.items():
print(convo_id, convo)See examples/ for other languages.
stats
- 7,168 conversations
- 258,145 utterances
- 1,315 unique participants
format
The dataset is a mapping from conversation UUID to a conversation:
{
"prompt": "What's the most interesting thing you've learned recently?",
"ratings": { "witty": "1", "int": 5, "upbeat": 5 },
"start": "2018-04-20T01:57:41",
"messages": [
[
{
"text": "Hello",
"timestamp": "2018-04-19T19:57:51",
"sender": "22578ac2-6317-44d5-8052-0a59076e0b96"
}
],
[
{
"text": "I learned that the Queen of England's last corgi died",
"timestamp": "2018-04-19T19:58:14",
"sender": "bebad07e-15df-48c3-a04f-67db828503e3"
}
],
[
{
"text": "Wow that sounds so sad",
"timestamp": "2018-04-19T19:58:18",
"sender": "22578ac2-6317-44d5-8052-0a59076e0b96"
},
{
"text": "was it a cardigan welsh corgi",
"timestamp": "2018-04-19T19:58:22",
"sender": "22578ac2-6317-44d5-8052-0a59076e0b96"
},
{
"text": "?",
"timestamp": "2018-04-19T19:58:24",
"sender": "22578ac2-6317-44d5-8052-0a59076e0b96"
}
]
]
}how to cite
If you extend or use this work, please cite the paper where it was introduced:
@article{myers2020conversational,
title={Conversational Scaffolding: An Analogy-Based Approach to Response Prioritization in Open-Domain Dialogs},
author={Myers, Will and Etchart, Tyler and Fulda, Nancy},
year={2020}
}
