Ensemble Approach for Natural Language Question Answering Problem


Machine comprehension, answering a question depending on a given context paragraph is a typical task of Natural Language Understanding. It requires to model complex dependencies existing between the question and the context paragraph. There are many neural network models attempting to solve the problem of question answering. One of the best models have been selected, studied and compared with each other. All the selected models are based on the neural attention mechanism concept. Additionally, studies on a SQuAD dataset were performed. The subsets of queries were extracted and then each model was analyzed how it deals with specific group of queries. The ensemble model based on Mnemonic Reader, BiDAF and QANet was created and tested on SQuAD dataset. It outperforms the best Mnemonic Reader model.



Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL