  We present Cross-lingual Open-Retrieval Answer Generation (CORA), the first
unified many-to-many question answering (QA) model that can answer questions
across many languages, even for ones without language-specific annotated data
or knowledge sources. We introduce a new dense passage retrieval algorithm that
is trained to retrieve documents across languages for a question. Combined with
a multilingual autoregressive generation model, CORA answers directly in the
target language without any translation or in-language retrieval modules as
used in prior work. We propose an iterative training method that automatically
extends annotated data available only in high-resource languages to
low-resource ones. Our results show that CORA substantially outperforms the
previous state of the art on multilingual open QA benchmarks across 26
languages, 9 of which are unseen during training. Our analyses show the
significance of cross-lingual retrieval and generation in many languages,
particularly under low-resource settings.
