| # 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: | |
| * [x] Is the task an existing benchmark in the literature? | |
| * [x] Have you referenced the original paper that introduced the task? | |
| * [x] 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? | |