text:   Cross-lingual Generalization",6,"[""I'm excited to announce XTREME, a new benchmark that covers 9 tasks and 40 typologically diverse languages.\n\nPaper: <LINK>\nBlog post: <LINK>\nCode: <LINK> <LINK>"", 'XTREME evaluates models on their capability to do zero-shot cross-lingual transfer when fine-tuned on English. In the paper, we conduct experiments with several state-of-the-art models that have been pre-trained on large multilingual corpora. https://t.co/9X59Ht8v7m', 'Overall, we find that there is a large gap to English and human performance and a lot of potential for improvement, particularly on syntactic tasks. See below for instance for the performance of XLM-R across tasks and languages. https://t.co/b8jUjW4ufF', 'This work would have not been possible without the contributions of many amazing people including @JunjieHu12 @orf_bnw @gneubig Melvin, Aditya @dhgarrette @JonClarkSeattle and others.', ""@gena_d Argh. The last letter of the website got lost during copy-pasting. 😅 Here's the correct link:\nhttps://t.co/S8RdKmhCsE"", '@boknilev Yes. For all but TyDiQA-GoldP, human performance is based on English. That comes with the obvious caveat that performance will differ across languages. IMO for datasets that are derived using translation (XNLI, PAWS-X, XQuAD), human perf should be very similar across languages.']",20,03,1227