Retrieval based open-domain QA systems use retrieved documents and
answer-span selection over retrieved documents to find best-answer candidates.
We hypothesize that multilingual Question Answering (QA) systems are prone to
information inconsistency when it comes to documents written in different
languages, because these documents tend to provide a model with varying
information about the same topic. To understand the effects of the biased
availability of information and cultural influence, we analyze the behavior of
multilingual open-domain question answering models with a focus on retrieval
bias. We analyze if different retriever models present different passages given
the same question in different languages on TyDi QA and XOR-TyDi QA, two
multilingualQA datasets. We speculate that the content differences in documents
across languages might reflect cultural divergences and/or social biases.

本研究针对基于检索的开放领域问答系统，探究多语言环境下文献信息不一致的问题，分析多语言开放领域问答模型的检索偏差，探讨文化差异和信息不一致性对模型的影响。