Answering complex factual questions has drawn a lot of attention. Researchers leverage various data sources to support complex QA, such as unstructured texts, structured knowledge graphs and relational databases, semi-structured web tables, or even hybrid data sources. However, although the ideas behind these approaches show similarity to some extent, there is not yet a consistent strategy to deal with various data sources. In this survey, we carefully examine how complex factual question answering has evolved across various data sources. We list the similarities among these approaches and group them into the analysis–extend–reason framework, despite the various question types and data sources that they focus on. We also address future directions for difficult factual question answering as well as the relevant benchmarks.

Lingxi Zhang

Jing Zhang

Xirui Ke

Haoyang Li

Xinmei Huang

Zhonghui Shao

Shulin Cao

Xin Lv

AI Open

A survey on complex factual question answering

复杂事实问答调查

A survey on complex factual question answering

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