Malayalam Question Answering System Based on a Deep Learning Hybrid Model of CNN and Bi-LSTM Approach

The Question-Answering (QA) approach represents one of the most significant Natural Language Processing (NLP) tasks that depends on language input. In terms of morphology & adhesive structure, Malayalam is a resource-constrained indigenous language of India. These linguistic features make QA in Malayalam particularly difficult. This study uses a subset of 5 tasks from the Facebook bAbI dataset to present a subset of five assignments from the Facebook bAbI dataset; this study presents a Malayalam Question Answering Solution that utilizes a Deep Learning (DL) hybrid framework combining CNN and Bi-LSTM Methods. We believe this is the initial time a hybrid-based deep learning framework has been used for the Malayalam question-answering technology. In the first iteration of the method, high-level semantic characteristics are extracted utilizing a Convolutional Neural Network. The Bi-LSTM tier then extracts the contextual feature representation of the text using the feature extraction result. Finally, use the softmax activation function to predict correct answers for corresponding questions. The proposed model is both functional and systemized in terms of classification accuracy, precision, recall, and F1 scores. The simulation results show that the proposed hybrid CNN and Bi-LSTM model outperform the existing models in terms of classification with more than 91 % accuracy for all five tasks.