TyDi QA is a question answering dataset covering 11 typologically diverse languages with 200K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology — the set of linguistic features that each language expresses — such that the authors expect models performing well on this set to generalize across a large number of the languages in the world.
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ToM-in-AMC is a novel NLP benchmark, Short for Theory-of-Mind meta-learning Assessment with Movie Characters. The benchmark consists of 1,000 parsed movie scripts for this purpose, each corresponding to a few-shot character understanding task.
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Chinese Literature NER RE is a Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text. It is constructed from hundreds of Chinese literature articles.
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