  Existing methods for open-retrieval question answering in lower resource
languages (LRLs) lag significantly behind English. They not only suffer from
the shortcomings of non-English document retrieval, but are reliant on
language-specific supervision for either the task or translation. We formulate
a task setup more realistic to available resources, that circumvents document
retrieval to reliably transfer knowledge from English to lower resource
languages. Assuming a strong English question answering model or database, we
compare and analyze methods that pivot through English: to map foreign queries
to English and then English answers back to target language answers. Within
this task setup we propose Reranked Multilingual Maximal Inner Product Search
(RM-MIPS), akin to semantic similarity retrieval over the English training set
with reranking, which outperforms the strongest baselines by 2.7% on XQuAD and
6.2% on MKQA. Analysis demonstrates the particular efficacy of this strategy
over state-of-the-art alternatives in challenging settings: low-resource
languages, with extensive distractor data and query distribution misalignment.
Circumventing retrieval, our analysis shows this approach offers rapid answer
generation to almost any language off-the-shelf, without the need for any
additional training data in the target language.
