datasheet.md

Datasheet of CC-Riddle

by Fan Xu, Yunxiang Zhang and Xiaojun Wan

Paper: CC-Riddle: A Question Answering Dataset of Chinese Character Riddles

Data and code: https://github.com/pku0xff/CC-Riddle

This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The full text of the license can be accessed at the following link: https://creativecommons.org/licenses/by-nc-sa/4.0/.

We bear all responsibility in case of violation of rights, etc., and confirmation of the data license.

Motivation

1. For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.

   The dataset is created for Chinese character riddle question answering task, i.e., the task of solving Chinese character riddles. Solving Chinese character riddles is a challenging task for both humans and language models. As far as we know, there has been no open-resource Chinese character datasets before CC-Riddle. We hope that the CC-Riddle dataset will contribute to the development of more advanced Chinese and multilingual language models, and provide valuable assistance to researchers in the field of Chinese language studies.

2. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?

   The dataset is created by Fan Xu, Yunxiang Zhang and Xiaojun Wan on behalf of the Text Mining and Linguistic Computing Group, Wangxuan Institute of Computer Technology, Peking University.

3. Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.

   It is funded by the Text Mining and Linguistic Computing Group, Wangxuan Institute of Computer Technology, Peking University.

Composition

1. What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions be tween them; nodes and edges)? Please provide a description.

   The dataset is comprised of pure text data mainly in Chinese, represented in .jsonl form. The file is composed of a list of instances.

2. How many instances are there in total (of each type, if appropriate)?

   	Total	Training	Validation	Test
   # Characters	7279	4367	1457	1455
   # Riddles	27517	16626	5480	5411

3. Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).

   The dataset contain all possible instances. It is composed of both web-crawled and model-generated character riddles.

4. What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.

   Each instance is a Chinese character riddle represented in python dictionary form and has the following keys:

   • id, a unique identification number of the riddle
   • answer, a single Chinese character, solution of the riddle
   • question, a sentence, description of the riddle
   • source, the value is human or gen, representing whether this riddle is designed by human or generated by our model
   • split, the value is train or valid or test, split with a ratio of 6:2:2

5. Is there a label or target associated with each instance? If so, please provide a description.

   Yes. The source mentioned above.

6. Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.

   No.

7. Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? If so, please describe how these relationships are made explicit.

   Yes. A single Chinese character can have multiple riddle descriptions but they are independent with each other.

8. Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.

   Yes. The split item mentioned in Question 4. The split ratio (60%:20%:20%) is a common practice in machine learning.

9. Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.

   Almost no. We have cleaned the crawled data and manually filtered the generated data.

10. Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a dataset consumer? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.

    The CC-Riddle dataset is self-contained.

11. Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals’ non-public communications)? If so, please provide a description.

    No.

12. Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why.

    No.

Collection Process

1. How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If the data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how.

   74.08% of the riddles are crawled from the web. 25.92% of the riddles are generated by models and have been verified manually.

2. What mechanisms or procedures were used to collect the data (e.g., hardware apparatuses or sensors, manual human curation, software programs, software APIs)? How were these mechanisms or procedures validated?

   We first use web crawler to obtain manually designed character riddles from some websites. Then we train a riddle generation model and use it to generate brand new riddles and hire annotators to filter the generated data.

3. Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)?

   We hired crowdworkers and compensated them with 0.1 yuan for each entry they checked, which is higher than the statutory minimum wage.

4. Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the time frame in which the data associated with the instances was created.

   The data was collected in 2021. The riddles are not related to collecting time.

5. Were any ethical review processes conducted (e.g., by an institutional review board)? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.

   No. The Chinese character riddles are always ethical.

Preprocessing/cleaning/labeling

1. Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? If so, please provide a description. If not, you may skip the remaining questions in this section.

   Yes. After obtaining the data from the web and from our riddle generation model, we removed duplicate data and deleted irrelevant characters to enhance data quality.

2. Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? If so, please provide a link or other access point to the “raw” data.

   No. This is not necessary for a dataset of Chinese character riddles.

3. Is the software that was used to preprocess/clean/label the data available? If so, please provide a link or other access point.

   We have not used any specific software to preprocess/clean/label the data.

Uses

1. Has the dataset been used for any tasks already? If so, please provide a description.

   Not yet.

2. Is there a repository that links to any or all papers or systems that use the dataset? If so, please provide a link or other access point.

   There has not been such a paper or system yet.

3. What (other) tasks could the dataset be used for?

   It can be used to develop better language models and build AI systems for education or Chinese language research. It can be used to evaluate language models, especially their understanding of Chinese character glyph and figurative language.

4. Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? For example, is there anything that a dataset consumer might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other risks or harms (e.g., legal risks, financial harms)? If so, please provide a description. Is there anything a dataset consumer could do to mitigate these risks or harms?

   No.

5. Are there tasks for which the dataset should not be used? If so, please provide a description.

   The dataset can only be used in academic area.

Distribution

1. Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? If so, please provide a description.

   The dataset is publicly available.

2. How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)?

   The dataset is available on GitHub(https://github.com/pku0xff/CC-Riddle).

3. When will the dataset be distributed?

   It has been distributed.

4. Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use(ToU)? If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions.

   This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The full text of the license can be accessed at the following link: https://creativecommons.org/licenses/by-nc-sa/4.0/.

5. Have any third parties imposed IP-based or other restrictions on the data associated with the instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions.

   No.

6. Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation.

   No.

Maintenance

1. Who will be supporting/hosting/maintaining the dataset?

   The first author is hosting and maintaining the dataset.

2. How can the owner/curator/manager of the dataset be contacted (e.g., email address)?

   Email: xufan2000@pku.edu.cn

3. Is there an erratum? If so, please provide a link or other access point.

   No.

4. Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to dataset consumers (e.g., mailing list, GitHub)?

   The Chinese character riddle is a traditional Chinese cultural entertainment game. The existing character riddle data does not change over time. Also, in the real world, it is not naturally possible to produce riddles in bulk. We welcome visitors to provide us with new data through contact methods such as email. If there is a plan for systematic updates, we will announce it at the earliest opportunity.

5. If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? If so, please provide a description. Will these contributions be validated/verified? If so, please describe how. If not, why not? Is there a process for communicating/distributing these contributions to dataset consumers? If so, please provide a description.

   You can use this repository and cite our paper.