Span extraction is an essential problem in machine reading comprehension.
Most of the existing algorithms predict the start and end positions of an
answer span in the given corresponding context by generating two probability
vectors. In this paper, we propose a novel approach that extends the
probability vector to a probability matrix. Such a matrix can cover more
start-end position pairs. Precisely, to each possible start index, the method
always generates an end probability vector. Besides, we propose a
sampling-based training strategy to address the computational cost and memory
issue in the matrix training phase. We evaluate our method on SQuAD 1.1 and
three other question answering benchmarks. Leveraging the most competitive
models BERT and BiDAF as the backbone, our proposed approach can get consistent
improvements in all datasets, demonstrating the effectiveness of the proposed
method.

本文提出了一种基于概率矩阵和基于采样训练策略的跨度抽取方法，该方法在四个问答基准数据集中得到了持续的改进，展示了该方法的有效性。