Abstract: Embodiments described herein provide a question answering approach that answers a question by generating an executable logical form. First, a ranking model is used to select a set of good logical forms from a pool of logical forms obtained by searching over a knowledge graph. The selected logical forms are good in the sense that they are close to (or exactly match, in some cases) the intents in the question and final desired logical form. Next, a generation model is adopted conditioned on the question as well as the selected logical forms to generate the target logical form and execute it to obtain the final answer. For example, at inference stage, when a question is received, a matching logical form is identified from the question, based on which the final answer can be generated based on the node that is associated with the matching logical form in the knowledge base.

Abstract: Embodiments described herein provide a question answering approach that answers a question by generating an executable logical form. First, a ranking model is used to select a set of good logical forms from a pool of logical forms obtained by searching over a knowledge graph. The selected logical forms are good in the sense that they are close to (or exactly match, in some cases) the intents in the question and final desired logical form. Next, a generation model is adopted conditioned on the question as well as the selected logical forms to generate the target logical form and execute it to obtain the final answer. For example, at inference stage, when a question is received, a matching logical form is identified from the question, based on which the final answer can be generated based on the node that is associated with the matching logical form in the knowledge base.