Ensemble approach for natural language question answering problem
This work addresses the problem of improving question answering accuracy for users of NLP systems, but it is incremental as it builds on existing models.
The authors tackled the natural language question answering problem by creating an ensemble model from three selected neural attention-based models, which outperformed the best existing Mnemonic Reader model on the SQUAD dataset.
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. 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. Based on these three model ensemble model was created and tested on SQUAD dataset. It outperforms the best Mnemonic Reader model.