Pragmatic approach in natural language understanding

Natural languages that we use in daily conversation inevitably omit or insert various words. Incomplete sentences such as these may be grammatically correct, but they prevent machine translation systems from correctly interpreting such sentences as they assume meanings only from sentence-to-sentence context. This is largely due to the fact that context is a social product defined by the participants of a party, while translation machines literally translate the information provided by the speakers. The authors of this paper confirmed the effectiveness of syntactic constraint-lead approaches to Collaborative Fusion Machine Translation (CFMT) based on group Rorschach tests, in addition to semantic constraint-lead and syntactic constraint-lead machine translation processes. We believe the use of our pragmatic approach to distribute artificial intelligence will open a new frontier for CFMT.