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We describe our two-stage system for the Multilingual Information Access(MIA) 2022 Shared Task on Cross-Lingual Open-Retrieval Question Answering. Thefirst stage consists of multilingual passage retrieval with a hybrid dense andsparse retrieval strategy. The second stage consists of a reader which outputsthe answer from the top passages returned by the first stage. We show theefficacy of using a multilingual language model with entity representations inpretraining, sparse retrieval signals to help dense retrieval, andFusion-in-Decoder. On the development set, we obtain 43.46 F1 on XOR-TyDi QAand 21.99 F1 on MKQA, for an average F1 score of 32.73. On the test set, weobtain 40.93 F1 on XOR-TyDi QA and 22.29 F1 on MKQA, for an average F1 score of31.61. We improve over the official baseline by over 4 F1 points on both thedevelopment and test sets.

MIA 2022 Shared Task Submission: Leveraging Entity Representations, Dense-Sparse Hybrids, and Fusion-in-Decoder for Cross-Lingual Question Answering