A Bi-model MemN2N Network for Complex Question Answering Task
Question Answering (QA) system is a field of Natural language processing, in which the users can post query in their own languages. The system also gives precise answer instead of list of documents. A memory network has the ability to perform reasoning with inference components and long-term memory component. The two components are used efficiently to find the answers from the story context for a given query. In our earlier work  we evaluated the performance of MemN2N network with complex and easy question answering tasks and found that the MemN2N fail to produce good results with some complex QA tasks of bAbI dataset. This work intends to improve the performance with a state of the art Bi-Model end to end memory network (BiMemN2N_I) model for such complex QA tasks and compare its performance with the standard MemN2N model and MemNN models.
In this work, a Bi-model MemN2N Network based question answering system is implemented and its performance is evaluated with a complex question answering tasks from bAbI dataset. In addition, the performance of training and testing with suitable metrics are studied and identified the difference in the performance of two question answering tasks.