metadata
configs:
- config_name: main
data_files:
- split: train
path: train.jsonl
- split: test
path: test.jsonl
- split: validation
path: val.jsonl
license: apache-2.0
task_categories:
- text-retrieval
language:
- en
source_datasets:
- original
pretty_name: QUEST
annotations_creators:
- wikipedia-sourced
size_categories:
- 1K<n<10K
Dataset Card for QUEST
Dataset Description
- Repository: https://github.com/google-research/language/tree/master/language/quest
- Paper: https://arxiv.org/abs/2305.11694
- Point of Contact: [email protected]
Dataset Summary
We provide here the data accompanying the paper: QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations .
Dataset Structure
Data Instances
QUEST contains 6307 training queries, 323 examples for development, and 1727 examples for testing.
Data Fields
Each examples file contains newline-separated json dictionaries with the following fields:
query
- Paraphrased query written by annotators.docs
- List of relevant document titles.original_query
- The original query which was paraphrased. Atomic queries are enclosed by<mark></mark>
. Augmented queries do not have this field populated.scores
- This field is not populated and only used when producing predictions to enable sharing the same data structure.metadata
- A dictionary with the following fields:template
- The template used to create the query.domain
- The domain to which the query belongs.fluency
- List of fluency ratings for the query.meaning
- List of ratings for whether the paraphrased query meaning is the same as the original query.naturalness
- List of naturalness ratings for the query.relevance_ratings
- Dictionary mapping document titles to relevance ratings for the document.evidence_ratings
- Dictionary mapping document titles to evidence ratings for the document.attributions
- Dictionary mapping a document title to its attributions attributions are a list of dictionaries mapping a query substring to a document substring.
The document corpus is at https://storage.googleapis.com/gresearch/quest/documents.jsonl. Note that this file is quite large
(899MB). The format is newline separated json dicts containing title
and
text
.
Citation Information
@inproceedings{malaviya23expertqa,
title = {QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations},
author = {Chaitanya Malaviya and Peter Shaw and Ming-Wei Chang and Kenton Lee and Kristina Toutanova},
booktitle = {ACL},
year = {2023},
url = "https://arxiv.org/abs/2305.11694"
}