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Dataset Card for "doqa"
Dataset Summary
DoQA is a dataset for accessing Domain Specific FAQs via conversational QA that contains 2,437 information-seeking question/answer dialogues (10,917 questions in total) on three different domains: cooking, travel and movies. Note that we include in the generic concept of FAQs also Community Question Answering sites, as well as corporate information in intranets which is maintained in textual form similar to FAQs, often referred to as internal “knowledge bases”.
These dialogues are created by crowd workers that play the following two roles: the user who asks questions about a given topic posted in Stack Exchange (https://stackexchange.com/), and the domain expert who replies to the questions by selecting a short span of text from the long textual reply in the original post. The expert can rephrase the selected span, in order to make it look more natural. The dataset covers unanswerable questions and some relevant dialogue acts.
DoQA enables the development and evaluation of conversational QA systems that help users access the knowledge buried in domain specific FAQs.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
cooking
- Size of downloaded dataset files: 4.19 MB
- Size of the generated dataset: 11.31 MB
- Total amount of disk used: 15.51 MB
An example of 'train' looks as follows.
This example was too long and was cropped:
{
"answers": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"background": "\"So, over mixing batter forms gluten, which in turn hardens the cake. Fine.The problem is that I don't want lumps in the cakes, ...",
"context": "\"Milk won't help you - it's mostly water, and gluten develops from flour (more accurately, specific proteins in flour) and water...",
"followup": "n",
"id": "C_64ce44d5f14347f488eb04b50387f022_q#2",
"orig_answer": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"question": "Ok. What can I add to make it more softer and avoid hardening?",
"title": "What to add to the batter of the cake to avoid hardening when the gluten formation can't be avoided?",
"yesno": "x"
}
movies
- Size of downloaded dataset files: 4.19 MB
- Size of the generated dataset: 3.17 MB
- Total amount of disk used: 7.36 MB
An example of 'test' looks as follows.
This example was too long and was cropped:
{
"answers": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"background": "\"So, over mixing batter forms gluten, which in turn hardens the cake. Fine.The problem is that I don't want lumps in the cakes, ...",
"context": "\"Milk won't help you - it's mostly water, and gluten develops from flour (more accurately, specific proteins in flour) and water...",
"followup": "n",
"id": "C_64ce44d5f14347f488eb04b50387f022_q#2",
"orig_answer": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"question": "Ok. What can I add to make it more softer and avoid hardening?",
"title": "What to add to the batter of the cake to avoid hardening when the gluten formation can't be avoided?",
"yesno": "x"
}
travel
- Size of downloaded dataset files: 4.19 MB
- Size of the generated dataset: 3.22 MB
- Total amount of disk used: 7.41 MB
An example of 'test' looks as follows.
This example was too long and was cropped:
{
"answers": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"background": "\"So, over mixing batter forms gluten, which in turn hardens the cake. Fine.The problem is that I don't want lumps in the cakes, ...",
"context": "\"Milk won't help you - it's mostly water, and gluten develops from flour (more accurately, specific proteins in flour) and water...",
"followup": "n",
"id": "C_64ce44d5f14347f488eb04b50387f022_q#2",
"orig_answer": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"question": "Ok. What can I add to make it more softer and avoid hardening?",
"title": "What to add to the batter of the cake to avoid hardening when the gluten formation can't be avoided?",
"yesno": "x"
}
Data Fields
The data fields are the same among all splits.
cooking
title
: astring
feature.background
: astring
feature.context
: astring
feature.question
: astring
feature.id
: astring
feature.answers
: a dictionary feature containing:text
: astring
feature.answer_start
: aint32
feature.
followup
: astring
feature.yesno
: astring
feature.orig_answer
: a dictionary feature containing:text
: astring
feature.answer_start
: aint32
feature.
movies
title
: astring
feature.background
: astring
feature.context
: astring
feature.question
: astring
feature.id
: astring
feature.answers
: a dictionary feature containing:text
: astring
feature.answer_start
: aint32
feature.
followup
: astring
feature.yesno
: astring
feature.orig_answer
: a dictionary feature containing:text
: astring
feature.answer_start
: aint32
feature.
travel
title
: astring
feature.background
: astring
feature.context
: astring
feature.question
: astring
feature.id
: astring
feature.answers
: a dictionary feature containing:text
: astring
feature.answer_start
: aint32
feature.
followup
: astring
feature.yesno
: astring
feature.orig_answer
: a dictionary feature containing:text
: astring
feature.answer_start
: aint32
feature.
Data Splits
cooking
train | validation | test | |
---|---|---|---|
cooking | 4612 | 911 | 1797 |
movies
test | |
---|---|
movies | 1884 |
travel
test | |
---|---|
travel | 1713 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@misc{campos2020doqa,
title={DoQA -- Accessing Domain-Specific FAQs via Conversational QA},
author={Jon Ander Campos and Arantxa Otegi and Aitor Soroa and Jan Deriu and Mark Cieliebak and Eneko Agirre},
year={2020},
eprint={2005.01328},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Contributions
Thanks to @mariamabarham, @thomwolf, @lhoestq for adding this dataset.
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