Machine Reading Comprehension (MRC) models tend to take advantage of spurious
correlations (also known as dataset bias or annotation artifacts in the
research community). Consequently, these models may perform the MRC task
without fully comprehending the given context and question, which is
undesirable since it may result in low robustness against distribution shift.
This paper delves into the concept of answer-position bias, where a significant
percentage of training questions have answers located solely in the first
sentence of the context. We propose a Single-Sentence Reader as a new approach
for addressing answer position bias in MRC. We implement this approach using
six different models and thoroughly analyze their performance. Remarkably, our
proposed Single-Sentence Readers achieve results that nearly match those of
models trained on conventional training sets, proving their effectiveness. Our
study also discusses several challenges our Single-Sentence Readers encounter
and proposes a potential solution.

这篇论文探讨了答案位置偏差的概念，其中有大部分训练问题的答案仅位于上下文的第一句。我们提出了一种新的方法来解决机器阅读理解中的答案位置偏差问题，即单句阅读器。我们使用六种不同的模型实现了这种方法，并对它们的性能进行了详细分析。值得注意的是，我们提出的单句阅读器的结果几乎与传统训练集上训练的模型相匹配，证明了其有效性。我们的研究还讨论了单句阅读器遇到的几个挑战，并提出了一个潜在的解决方案。