{"id":"W2889787757","doi":"10.18653/v1/d18-1259","title":"HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering","year":2018,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":1582,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Office of Naval Research; Defense Advanced Research Projects Agency; Université de Montréal; Nvidia; National Science Foundation","keywords":"Zhàng; Question answering; Computer science; Artificial intelligence; Natural language processing; Information retrieval; History; China; Archaeology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06108907148323083,"score_gpt":0.3121959901327288,"score_spread":0.2511069186494979,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}