XQuAD-ca

If you use this resource in your work, please cite our latest paper: @inproceedings{armengol-estape-etal-2021-multilingual,<br> title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",<br> author = "Armengol-Estap{\'e}, Jordi and<br> Carrino, Casimiro Pio and<br> Rodriguez-Penagos, Carlos and<br> de Gibert Bonet, Ona and<br> Armentano-Oller, Carme and<br> Gonzalez-Agirre, Aitor and<br> Melero, Maite and<br> Villegas, Marta",<br> booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",<br> month = aug,<br> year = "2021",<br> address = "Online",<br> publisher = "Association for Computational Linguistics",<br> url = "https://aclanthology.org/2021.findings-acl.437",<br> doi = "10.18653/v1/2021.findings-acl.437",<br> pages = "4933--4946",<br> } Professional translation into Catalan of XQuAD dataset (https://github.com/deepmind/xquad). XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Rumanian was added later. We added the 13th language to the corpus using also professional native catalan translators. XQuAD and XQuAD-Ca datasets are released under CC-by-sa licence.