Open-retrieval question answering systems are generally trained and tested on
large datasets in well-established domains. However, low-resource settings such
as new and emerging domains would especially benefit from reliable question
answering systems. Furthermore, multilingual and cross-lingual resources in
emergent domains are scarce, leading to few or no such systems. In this paper,
we demonstrate a cross-lingual open-retrieval question answering system for the
emergent domain of COVID-19. Our system adopts a corpus of scientific articles
to ensure that retrieved documents are reliable. To address the scarcity of
cross-lingual training data in emergent domains, we present a method utilizing
automatic translation, alignment, and filtering to produce English-to-all
datasets. We show that a deep semantic retriever greatly benefits from training
on our English-to-all data and significantly outperforms a BM25 baseline in the
cross-lingual setting. We illustrate the capabilities of our system with
examples and release all code necessary to train and deploy such a system.

本文提出了一种跨语言的开放检索问答系统，使用科学文章语料库保证检索到的文档的可靠性；通过利用自动翻译、对齐和过滤来产生英文 - 全语言数据集，缓解新兴语境下跨语言训练数据的稀缺性。结果表明，在使用我们的英文 - 全语言数据进行训练后，深度语义检索器在跨语言设置中明显优于 BM25 基线。