  We present a study on the design of multilingual Answer Sentence Selection
(AS2) models, which are a core component of modern Question Answering (QA)
systems. The main idea is to transfer data, created from one resource rich
language, e.g., English, to other languages, less rich in terms of resources.
The main findings of this paper are: (i) the training data for AS2 translated
into a target language can be used to effectively fine-tune a Transformer-based
model for that language; (ii) one multilingual Transformer model it is enough
to rank answers in multiple languages; and (iii) mixed-language question/answer
pairs can be used to fine-tune models to select answers from any language,
where the input question is just in one language. This highly reduces the
complexity and technical requirement of a multilingual QA system. Our
experiments validate the findings above, showing a modest drop, at most 3%,
with respect to the state-of-the-art English model.
