Since late December 2019, the city of Wuhan in China has reported an outbreak of atypical pneumonia, now known as COVID-19 caused by the novel coronavirus. Cases have spread to other cities in China and more than 200 countries and regions internationally. World Health Organization (WHO) officially declares the coronavirus outbreak a pandemic and the public health emergency has caused world-wide impact to daily lives: people are advised to keep social distance, in- person events have been moved online, and some function facilitates have been locked-down. Alternatively, the Web becomes an active venue for people to share knowledge and information. With respect to the on-going topic, people continuously post questions online and seek for answers. Yet, sharing global information conveyed in different languages is challenging because the language barrier is intrinsically unfriendly to monolingual speakers. In this paper, we propose a multilingual COVID-QA model to answer people’s questions in their own languages while the model is able to absorb knowledge from other languages. Another challenge is that in most cases, the information to share does not have parallel data in multiple languages. To this end, we propose a novel framework which incorporates (unsupervised) translation alignment to learn as pseudo-parallel data. Then we train (cross-lingual) question-answering mapping and generation. We demonstrate the effectiveness of our proposed approach compared against a series of competitive baselines. In this way, we make it easier to share global information across the language barriers, and hopefully we contribute to the battle against COVID-19.

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