Name: XOR-TyDi QA
Description: XOR-TyDi QA brings together for the first time information-seeking questions, open-retrieval QA, and multilingual QA to create a multilingual open-retrieval QA dataset that enables cross-lingual answer retrieval. It consists of questions written by information-seeking native speakers in 7 typologically diverse languages and answer annotations that are retrieved from multilingual document collections.
Provider: Multiple Institutions 
Paper Title: XOR QA: Cross-lingual Open-Retrieval Question Answering
Tasks: open-retrieval question answering
Venue Name: nan
Authors: Akari Asai, Jungo Kasai, Jonathan H. Clark,Kenton Lee, Eunsol Choi,  Hannaneh Hajishirzi
Abstract: Multilingual question answering tasks typically assume that answers exist in the same
  language as the question. Yet in practice, many languages face both information
  scarcity—where languages have few reference
  articles—and information asymmetry—where
  questions reference concepts from other cultures. This work extends open-retrieval question answering to a cross-lingual setting enabling questions from one language to be answered via answer content from another language. We construct a large-scale dataset
  built on 40K information-seeking questions
  across 7 diverse non-English languages that
  TYDI QA could not find same-language answers for. Based on this dataset, we introduce
  a task framework, called Cross-lingual OpenRetrieval Question Answering (XOR QA),
  that consists of three new tasks involving crosslingual document retrieval from multilingual
  and English resources. We establish baselines
  with state-of-the-art machine translation systems and cross-lingual pretrained models. Experimental results suggest that XOR QA is a
  challenging task that will facilitate the development of novel techniques for multilingual
  question answering. Our data and code are
  available at https://nlp.cs.washington.
  edu/xorqa/.