MLQA: Evaluating cross-lingual extractive question answering

https://ai.facebook.com/blog/mlqa-evaluating-cross-lingual-extractive-question-answering?__xts__%5B0%5D=68.ARAPmZIKpXDv733JFeRrrPTwAW7sPI8x6Rr5cnjswy_qpHRknmFeFqbPhMZWyvk7HIV34d_pGh5PE-3Zg-gdlRDd8JWGueS1uvb75i0o4sQ3WvT4N0MklDhpy2_XSrleOGSzBkhyjw6QuYlEDztm4SvIQR8CMfMfnfmO3aF8iIBo0WgIcTMTE-CPpTQZQRiNdLsV362f2AVkzYGMW82j1nbSA5Xd-oi8XJ86MPEk1fFYcL44k8-NwH0R44cKFOKS6JyNn1316LQ8w2_BJC7QiI2E73tkbLXVepU3&__tn__=K%2AF

MLQA: Evaluating cross-lingual extractive question answering
Facebook AI is sharing MLQA, an extractive question answering (QA) evaluation benchmark aligned across Arabic, German, Hindi, Spanish, Vietnamese, and Simplified Chinese. It will help the AI community improve and extend QA in more languages.
ai.facebook.com