It can be challenging to build effective open question answering (open QA)
systems for languages other than English, mainly due to a lack of labeled data
for training. We present a data efficient method to bootstrap such a system for
languages other than English. Our approach requires only limited QA resources
in the given language, along with machine-translated data, and at least a
bilingual language model. To evaluate our approach, we build such a system for
the Icelandic language and evaluate performance over trivia style datasets. The
corpora used for training are English in origin but machine translated into
Icelandic. We train a bilingual Icelandic/English language model to embed
English context and Icelandic questions following methodology introduced with
DensePhrases (Lee et al., 2021). The resulting system is an open domain
cross-lingual QA system between Icelandic and English. Finally, the system is
adapted for Icelandic only open QA, demonstrating how it is possible to
efficiently create an open QA system with limited access to curated datasets in
the language of interest.

该研究提出了一种数据节约的方法，通过有限的语言资源和机器翻译的数据以及双语语言模型，针对英语以外的其他语言，构建了开放领域跨语言 QA 系统，并使用冰岛语对该系统进行了测试。