基于时间卷积网络的多项选择机器阅读理解

Multiple Choice Machine Reading Comprehension Based onTemporal Convolutional Network

机器阅读理解是自然语言处理领域中一项具有挑战性的任务,其旨在回答与文章相关的问题,且需要复杂的语义推理。针对现有机器阅读理解方法提取特征时存在一定程度的信息丢失,且无法捕获全局的语义关系等问题,在时间卷积网络(TCN)的基础上,构建一种多项选择机器阅读理解M-TCN模型。采用注意力机制对文章、问题和候选答案进行匹配,并建立三者之间的内在联系。同时,为提取高层特征以减少信息丢失,利用TCN对匹配表示进行聚合。通过在公开阅读理解RCAE数据集上验证模型的性能,实验结果表明,与现有机器阅读理解模型ElimiNet、MRU、HCM等相比,该模型对正确答案的预测精度达到了52.5%,且综合性能更优。

As a challenging task in the field of natural language processing,machine reading comprehension aims to answer questions related to articles and requires complex semantic reasoning.To solve the problem of information loss and inability to capture the global semantic relationship in feature extraction of existing machine reading comprehension methods,this paper constructs a multiple choice machine reading comprehension M-TCN model based on Temporal Convolutional Network(TCN).Attention mechanism is used to match articles,questions and candidate answers,and the internal relationship among them is established.At the same time,in order to extract high-level features to reduce information loss,TCN is used to aggregate the matching representation.The performance of the model is verified on the RCAE dataset of public reading comprehension.The experimental results show that compared with the existing machine reading comprehension models including ElimiNet,MRU and HCM,the proposed model increases the prediction accuracy to 52.5%,and its comprehensive performance is better.