论文标题
具有辅助任务的功能增强机器阅读理解
Feature-augmented Machine Reading Comprehension with Auxiliary Tasks
论文作者
论文摘要
尽管大多数成功的机器阅读理解方法都取决于单个训练目标，但假定编码器层可以通过我们在预测层中定义的损耗函数来学习出色的表示，这在大多数时候是跨熵的，如果我们首先使用神经网络对问题和段落进行编码，然后将其直接融合，然后将其直接融合。但是，由于阅读理解中远距离传播的损失，编码器层无法有效学习并直接监督。因此，编码器层无法随时很好地学习表示形式。基于此，我们建议将多粒度信息注入编码层。实验证明了将多粒度信息添加到编码层的效果可以提高机器阅读理解系统的性能。最后，经验研究表明，我们的方法可以应用于许多现有的MRC模型。
While most successful approaches for machine reading comprehension rely on single training objective, it is assumed that the encoder layer can learn great representation through the loss function we define in the predict layer, which is cross entropy in most of time, in the case that we first use neural networks to encode the question and paragraph, then directly fuse the encoding result of them. However, due to the distantly loss backpropagating in reading comprehension, the encoder layer cannot learn effectively and be directly supervised. Thus, the encoder layer can not learn the representation well at any time. Base on this, we propose to inject multi granularity information to the encoding layer. Experiments demonstrate the effect of adding multi granularity information to the encoding layer can boost the performance of machine reading comprehension system. Finally, empirical study shows that our approach can be applied to many existing MRC models.
