多项选择阅读理解任务最近引起了人们的极大兴趣。该任务为每个问题提供了几个选项,并要求机器选择其中一个作为正确答案。当前的方法通常利用预先训练,然后进行微调的过程,该过程平等地对待数据,忽略了训练实例的难度。为了解决这个问题,课程学习(CL)已显示出其在提高模型性能方面的有效性。然而,以前的方法对课程学习有两个问题。首先,大多数方法是基于规则的,不够灵活,通常适用于特定的任务,比如机器翻译。第二,这些方法从容易到难或从难到易排列数据,忽视了人类通常从容易到困难学习的事实,当他们完成理解阅读任务时,从困难到容易。在这篇文章中,我们提出了一种新的自我意识周期课程学习(SACCL)方法,该方法可以从模型的角度评估数据难度,并使用周期训练策略对模型进行训练。实验表明,该方法在C3数据集上的性能优于基线,验证了SACCL的有效性。
Multiple-choice reading comprehension task has recently attracted significant interest. The task provides several options for each question and requires the machine to select one of them as the correct answer. Current approaches normally leverage a pre-training and then fine-tuning procedure that treats data equally, ignoring the difficulty of training examples. To solve this issue, curriculum learning (CL) has shown its effectiveness in improving the performance of models. However, previous methods have two problems with curriculum learning. First, most methods are rule-based, not flexible enough, and usually suitable for specific tasks, such as machine translation. Second, these methods arrange data from easy to hard or from hard to easy and overlook the fact that human beings usually learn from easy to difficult, and from difficult to easy when they make comprehension reading tasks. In this article, we propose a novel Self-Aware Cycle Curriculum Learning (SACCL) approach which can evaluate data difficulty from the model\'s perspective and train the model with cycle training strategy. The experiments show that the proposed approach achieves better performance on the C 3 dataset than the baseline, which verifies the effectiveness of SACCL.