Experimental Comparison between Neural-Symbolic Question-Answering Methods

This paper presents an experimental comparison and analysis between two representative methods for neural-symbolic question-answering: the Complex Query Decomposition (CQD) method and the Graph Neural Network Question Embedding (GNN-QE) method. Starting with large and complex queries, CQD breaks down the large query into shorter and simpler sub-queries, thus decomposing the initial query into more manageable components. On the other hand, GNN-QE is a recent architecture for neural-symbolic question-answering that uses graph neural networks to encrypt question structures. GNN-QE portrays questions as graphs to capture the innate links between different question elements, allowing for more complex and comprehensive reasoning. This paper examined the main characteristics of CQD and GNN-QE methods and analyse their advantages and drawbacks towards question-answering problem through popular performance metrics, such as MRR and Hits@K. The results show how each method handles complex queries through the use of symbolic and neural representations, and how well it can produce precise and insightful responses.