  Open-Domain Generative Question Answering has achieved impressive performance
in English by combining document-level retrieval with answer generation. These
approaches, which we refer to as GenQA, can generate complete sentences,
effectively answering both factoid and non-factoid questions. In this paper, we
extend GenQA to the multilingual and cross-lingual settings. For this purpose,
we first introduce GenTyDiQA, an extension of the TyDiQA dataset with
well-formed and complete answers for Arabic, Bengali, English, Japanese, and
Russian. Based on GenTyDiQA, we design a cross-lingual generative model that
produces full-sentence answers by exploiting passages written in multiple
languages, including languages different from the question. Our cross-lingual
generative system outperforms answer sentence selection baselines for all 5
languages and monolingual generative pipelines for three out of five languages
studied.
