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Dataset Card for X-CSR

Dataset Summary

To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and test in other languages, we create two benchmark datasets, namely X-CSQA and X-CODAH. Specifically, we automatically translate the original CSQA and CODAH datasets, which only have English versions, to 15 other languages, forming development and test sets for studying X-CSR. As our goal is to evaluate different ML-LMs in a unified evaluation protocol for X-CSR, we argue that such translated examples, although might contain noise, can serve as a starting benchmark for us to obtain meaningful analysis, before more human-translated datasets will be available in the future.

Supported Tasks and Leaderboards

https://inklab.usc.edu//XCSR/leaderboard

Languages

The total 16 languages for X-CSR: {en, zh, de, es, fr, it, jap, nl, pl, pt, ru, ar, vi, hi, sw, ur}.

Dataset Structure

Data Instances

An example of the X-CSQA dataset:

{
  "id": "be1920f7ba5454ad",  # an id shared by all languages
  "lang": "en", # one of the 16 language codes.
  "question": { 
    "stem": "What will happen to your knowledge with more learning?",   # question text
    "choices": [
      {"label": "A",  "text": "headaches" },
      {"label": "B",  "text": "bigger brain" },
      {"label": "C",  "text": "education" },
      {"label": "D",  "text": "growth" },
      {"label": "E",  "text": "knowing more" }
    ] },
  "answerKey": "D"    # hidden for test data.
}

An example of the X-CODAH dataset:

{
  "id": "b8eeef4a823fcd4b",   # an id shared by all languages
  "lang": "en", # one of the 16 language codes.
  "question_tag": "o",  # one of 6 question types
  "question": {
    "stem": " ", # always a blank as a dummy question
    "choices": [
      {"label": "A",
        "text": "Jennifer loves her school very much, she plans to drop every courses."},
      {"label": "B",
        "text": "Jennifer loves her school very much, she is never absent even when she's sick."},
      {"label": "C",
        "text": "Jennifer loves her school very much, she wants to get a part-time job."},
      {"label": "D",
        "text": "Jennifer loves her school very much, she quits school happily."}
    ]
  },
  "answerKey": "B"  # hidden for test data.
}

Data Fields

  • id: an id shared by all languages
  • lang: one of the 16 language codes.
  • question_tag: one of 6 question types
  • stem: always a blank as a dummy question
  • choices: a list of answers, each answer has:
    • label: a string answer identifier for each answer
    • text: the answer text

Data Splits

  • X-CSQA: There are 8,888 examples for training in English, 1,000 for development in each language, and 1,074 examples for testing in each language.
  • X-CODAH: There are 8,476 examples for training in English, 300 for development in each language, and 1,000 examples for testing in each language.

Dataset Creation

Curation Rationale

To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and test in other languages, we create two benchmark datasets, namely X-CSQA and X-CODAH.

The details of the dataset construction, especially the translation procedures, can be found in section A of the appendix of the paper.

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

[Needs More Information]

Citation Information

# X-CSR
@inproceedings{lin-etal-2021-xcsr,
    title = "Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning",
    author = "Lin, Bill Yuchen and Lee, Seyeon and Qiao, Xiaoyang and Ren, Xiang",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL-IJCNLP 2021)",
    year = "2021",
    note={to appear}
}

# CSQA
@inproceedings{Talmor2019commonsenseqaaq,
    address = {Minneapolis, Minnesota},
    author = {Talmor, Alon  and Herzig, Jonathan  and Lourie, Nicholas and Berant, Jonathan},
    booktitle = {Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
    doi = {10.18653/v1/N19-1421},
    pages = {4149--4158},
    publisher = {Association for Computational Linguistics},
    title = {CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge},
    url = {https://www.aclweb.org/anthology/N19-1421},
    year = {2019}
}

# CODAH
@inproceedings{Chen2019CODAHAA,
    address = {Minneapolis, USA},
    author = {Chen, Michael  and D{'}Arcy, Mike  and Liu, Alisa  and Fernandez, Jared  and Downey, Doug},
    booktitle = {Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}},
    doi = {10.18653/v1/W19-2008},
    pages = {63--69},
    publisher = {Association for Computational Linguistics},
    title = {CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},
    url = {https://www.aclweb.org/anthology/W19-2008},
    year = {2019}
}

Contributions

Thanks to Bill Yuchen Lin, Seyeon Lee, Xiaoyang Qiao, Xiang Ren for adding this dataset.

Update on GitHub