Question answering model based on machine reading comprehension with knowledge enhancement and answer verification

Abstract

Summary Deep learning has led to important breakthroughs in natural language processing and obtained the state‐of‐the‐art results on machine reading comprehension. However, it is essential to consider the entity recognition and the detection of unanswerable questions for accuracy improvement. A novel question answering model is proposed with knowledge enhancement and answer verification to promote the performance of reading comprehension. With knowledge enhancement, the proposed model is able to recognize entities from the passage and detect word boundary precisely. To deal with unanswerable questions, the answerability of questions is evaluated based on the textual entailment. Empirical studies suggest that the proposed model has better ability of reading comprehension than others, with improvement on question answering tasks.