AntNet: Deep Answer Understanding Network for Natural Reverse QA

--This study refers to a reverse question answering (reverse QA) procedure, in which machines proactively raise questions and humans supply answers. This procedure exists in many real human-machine interaction applications. A crucial problem in human-machine interaction is answer understanding. Existing solutions rely on mandatory option term selection to avoid automatic answer understanding. However, these solutions lead to unnatural human-computer interaction and harm user experience. T o this end, this study proposed a novel deep answer understanding network, called AntNet, for reverse QA. The network consists of three new modules, namely, skeleton extraction 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 (NLP) deep models. The effectiveness of the three new modules is also verified. UTOMA TIC question answering (QA) is a crucial component in many human-machine interaction systems, such as intelligent customer service, as it can provide a natural way for humans to acquire information [1]. Therefore, QA has received increasing attention in academic research and industry communities in recent years [2]. Questions are solely raised by humans, and answers are then returned by machines in the conventional QA scenario. How to select the best matched answer is the key problem in this setting [3].