In order to generate natural answers, typical products need lots of Natural Language Processing (NLP) tools and pattern engineering (McTear et al., 2016), which not only suffers from high costs of manual annotations for training data and patterns, but also have low coverage that cannot flexibly deal with variable linguistic phenomena in different domains.
this paper devotes to develop an end-to-end paradigm that generates natural answers without any NLP tools (e.g.