  This study focuses on a reverse question answering (QA) procedure, in which
machines proactively raise questions and humans supply the answers. This
procedure exists in many real human-machine interaction applications. However,
a crucial problem in human-machine interaction is answer understanding. The
existing solutions have relied on mandatory option term selection to avoid
automatic answer understanding. However, these solutions have led to unnatural
human-computer interaction and negatively affected user experience. To this
end, the current study proposes a novel deep answer understanding network,
called AntNet, for reverse QA. The network consists of three new modules,
namely, skeleton attention for questions, relevance-aware representation of
answers, and multi-hop based fusion. As answer understanding for reverse QA has
not been explored, a new data corpus is compiled in this study. Experimental
results indicate that our proposed network is significantly better than
existing methods and those modified from classical natural language processing
deep models. The effectiveness of the three new modules is also verified.
