Multiple Choice Question Answering Using Attention Based Ranking and Transfer Learning

The multiple choice question answering is still considered as an challenging task in Natural Language Processing. In this paper, we have tried to solve the problem of answering multiple choice questions where supporting documents corresponding to each question are not explicitly provided. Context retrieval is the strategy, which focuses on both reasoning and retrieving better supporting contexts. We present a improvised version of attention based deep neural network that eventually learns to order documents according to their relevance in relation to a given topic, all while achieving the goal of predicting the correct response. The top documents retrieved are considered more relevant context for given question answer pair. To achieve more accurate results transformer based pre-trained models are used in the implementation. We have used the concept of transfer learning which is related to learning and adapting knowledge by fine tuning model on other datasets. The reasoning challenge dataset by Allen institute is used to test the approach and SQuAD 2.0 and RACE datasets are used to fine tune the transformer based models. The accuracy of proposed model on ARC easy dataset is 89.51% and on ARC challenge dataset is 62.53%.