Datasets:
c3

	---
	annotations_creators:
	- expert-generated
	language_creators:
	- expert-generated
	language:
	- zh
	license:
	- other
	multilinguality:
	- monolingual
	size_categories:
	- 1K<n<10K
	source_datasets:
	- original
	task_categories:
	- question-answering
	task_ids:
	- multiple-choice-qa
	paperswithcode_id: c3
	pretty_name: C3
	dataset_info:
	- config_name: dialog
	features:
	- name: documents
	sequence: string
	- name: document_id
	dtype: string
	- name: questions
	sequence:
	- name: question
	dtype: string
	- name: answer
	dtype: string
	- name: choice
	sequence: string
	splits:
	- name: train
	num_bytes: 2039779
	num_examples: 4885
	- name: test
	num_bytes: 646955
	num_examples: 1627
	- name: validation
	num_bytes: 611106
	num_examples: 1628
	download_size: 2073256
	dataset_size: 3297840
	- config_name: mixed
	features:
	- name: documents
	sequence: string
	- name: document_id
	dtype: string
	- name: questions
	sequence:
	- name: question
	dtype: string
	- name: answer
	dtype: string
	- name: choice
	sequence: string
	splits:
	- name: train
	num_bytes: 2710473
	num_examples: 3138
	- name: test
	num_bytes: 891579
	num_examples: 1045
	- name: validation
	num_bytes: 910759
	num_examples: 1046
	download_size: 3183780
	dataset_size: 4512811
	configs:
	- config_name: dialog
	data_files:
	- split: train
	path: dialog/train-*
	- split: test
	path: dialog/test-*
	- split: validation
	path: dialog/validation-*
	- config_name: mixed
	data_files:
	- split: train
	path: mixed/train-*
	- split: test
	path: mixed/test-*
	- split: validation
	path: mixed/validation-*
	---
	# Dataset Card for C3

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: []()
	- Repository: [link]()
	- Paper: []()
	- Leaderboard: []()
	- Point of Contact: []()

	### Dataset Summary

	Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations.
	We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text.

	### Supported Tasks and Leaderboards

	[More Information Needed]

	### Languages

	[More Information Needed]

	## Dataset Structure

	[More Information Needed]

	### Data Instances

	[More Information Needed]

	### Data Fields

	[More Information Needed]

	### Data Splits

	[More Information Needed]

	## Dataset Creation


	### Curation Rationale

	[More Information Needed]

	### Source Data

	[More Information Needed]

	#### Initial Data Collection and Normalization

	[More Information Needed]

	#### Who are the source language producers?

	[More Information Needed]

	### Annotations

	[More Information Needed]

	#### Annotation process

	[More Information Needed]

	#### Who are the annotators?

	[More Information Needed]

	### Personal and Sensitive Information

	[More Information Needed]

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed]

	### Discussion of Biases

	[More Information Needed]

	### Other Known Limitations

	Dataset provided for research purposes only. Please check dataset license for additional information.

	## Additional Information

	### Dataset Curators

	[More Information Needed]

	### Licensing Information

	[More Information Needed]

	### Citation Information

	```
	@article{sun2019investigating,
	title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension},
	author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire},
	journal={Transactions of the Association for Computational Linguistics},
	year={2020},
	url={https://arxiv.org/abs/1904.09679v3}
	}
	```


	### Contributions

	Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.