MLQA: Evaluating Cross-lingual Extractive Question Answering

@article{Lewis2020MLQAEC,
  title={MLQA: Evaluating Cross-lingual Extractive Question Answering},
  author={Patrick Lewis and Barlas Oğuz and Ruty Rinott and S. Riedel and Holger Schwenk},
  journal={ArXiv},
  year={2020},
  volume={abs/1910.07475}
}
Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. Such annotated datasets are difficult and costly to collect, and rarely exist in languages other than English, making building QA systems that work well in other languages challenging. In order to develop such systems, it is crucial to invest in high quality multilingual evaluation benchmarks to measure progress. We present MLQA, a multi-way aligned extractive QA… Expand
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