Dataset Card for PersiNLU (Query Paraphrasing)

Dataset Summary

A Persian query paraphrasng task (deciding whether two questions are paraphrases of each other). The questions are partially generated from Google auto-complete, and partially translated from the Quora paraphrasing dataset.

Supported Tasks and Leaderboards

[More Information Needed]


The text dataset is in Persian (fa).

Dataset Structure

Data Instances

Here is an example from the dataset:

  "q1": "اعمال حج تمتع از چه روزی شروع میشود؟",
  "q2": "ویار از چه روزی شروع میشود؟",
  "label": "0",
  "category": "natural"

Data Fields

  • q1: the first question.
  • q2: the second question.
  • category: whether the questions are mined from Quora (qqp) or they're extracted from Google auto-complete (natural).
  • label: 1 if the questions are paraphrases; 0 otherwise.

Data Splits

The train/dev/test splits contains 1830/898/1916 samples.

Dataset Creation

Curation Rationale

For details, check the corresponding draft.

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[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

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

CC BY-NC-SA 4.0 License

Citation Information

    title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
    authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
    journal = {arXiv e-prints},
    eprint = {2012.06154},    


Thanks to @danyaljj for adding this dataset.

Models trained or fine-tuned on persiannlp/parsinlu_query_paraphrasing

None yet