Machine reading comprehension is a popular task to test whether a machine can understand natural lan-guage.Aiming at the choice reading comprehension items,we propose a multi-scale convolution based evidence sen-tence extraction model to extract more comprehensive features.Firstly,we utilize the pre-trained model to encode the semantic information for sentences,and use various features to assist the encoding to improve the performance of the model.Then,the multi-scale convolution is adopted to capture the text features at different scales,with the focal loss to alleviated the unbalanced sample issue.Finally,top-20 sentences are selected as the evidence sentences.Experimented on two datasets of reading comprehension,the proposed method improves the F1 values by 3.66％and 4.82％,respectively,compared with the optimal baseline models.
维普
万方数据