QANet-based candidate answer rethink model for machine reading comprehension

Abstract

The current model applied to the span extraction reading comprehension task fuses the information of context and question, and outputs the index with the highest probability calculated in the context as the prediction span. In this process, the model discards all the remaining candidate answers, which results in a waste of the available information in the candidate answers. Our model is designed to simulate the behaviour of human beings choosing multiple candidate answers and comprehensively judging the final answer in reading comprehension tasks. We propose the QANet-based candidate answer rethink model. The model interacts and fuses multiple candidate answers with context and question, prompting the model to obtain a more accurate answer by synthesising these three aspects of information. Experiments show that our model has made new progress in performance.