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MLQA

Paper

Title: MLQA: Evaluating Cross-lingual Extractive Question Answering

Abstract: https://arxiv.org/abs/1910.07475

MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between 4 different languages on average

Homepage: https://github.com/facebookresearch/MLQA

Citation

@misc{lewis2020mlqaevaluatingcrosslingualextractive,
      title={MLQA: Evaluating Cross-lingual Extractive Question Answering},
      author={Patrick Lewis and Barlas Oğuz and Ruty Rinott and Sebastian Riedel and Holger Schwenk},
      year={2020},
      eprint={1910.07475},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/1910.07475},
}

Groups, Tags, and Tasks

Groups

  • Not part of a group yet

Tasks

Tasks of the form mlqa_context-lang_question-lang.yaml

  • mlqa_ar_ar.yaml
  • mlqa_ar_de.yaml
  • mlqa_ar_vi.yaml
  • mlqa_ar_zh.yaml
  • mlqa_ar_en.yaml
  • mlqa_ar_es.yaml
  • mlqa_ar_hi.yaml
  • mlqa_de_ar.yaml
  • mlqa_de_de.yaml
  • mlqa_de_vi.yaml
  • mlqa_de_zh.yaml
  • mlqa_de_en.yaml
  • mlqa_de_es.yaml
  • mlqa_de_hi.yaml
  • mlqa_vi_ar.yaml
  • mlqa_vi_de.yaml
  • mlqa_vi_vi.yaml
  • mlqa_vi_zh.yaml
  • mlqa_vi_en.yaml
  • mlqa_vi_es.yaml
  • mlqa_vi_hi.yaml
  • mlqa_zh_ar.yaml
  • mlqa_zh_de.yaml
  • mlqa_zh_vi.yaml
  • mlqa_zh_zh.yaml
  • mlqa_zh_en.yaml
  • mlqa_zh_es.yaml
  • mlqa_zh_hi.yaml
  • mlqa_en_ar.yaml
  • mlqa_en_de.yaml
  • mlqa_en_vi.yaml
  • mlqa_en_zh.yaml
  • mlqa_en_en.yaml
  • mlqa_en_es.yaml
  • mlqa_en_hi.yaml
  • mlqa_es_ar.yaml
  • mlqa_es_de.yaml
  • mlqa_es_vi.yaml
  • mlqa_es_zh.yaml
  • mlqa_es_en.yaml
  • mlqa_es_es.yaml
  • mlqa_es_hi.yaml
  • mlqa_hi_ar.yaml
  • mlqa_hi_de.yaml
  • mlqa_hi_vi.yaml
  • mlqa_hi_zh.yaml
  • mlqa_hi_en.yaml
  • mlqa_hi_es.yaml
  • mlqa_hi_hi.yaml

Checklist

For adding novel benchmarks/datasets to the library:

  • Is the task an existing benchmark in the literature?
    • Have you referenced the original paper that introduced the task?
    • If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?

If other tasks on this dataset are already supported:

  • Is the "Main" variant of this task clearly denoted?
  • Have you provided a short sentence in a README on what each new variant adds / evaluates?
  • Have you noted which, if any, published evaluation setups are matched by this variant?