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Machine Reading Comprehension (MRC) models tend to take advantage of spuriouscorrelations (also known as dataset bias or annotation artifacts in theresearch community). Consequently, these models may perform the MRC taskwithout fully comprehending the given context and question, which isundesirable since it may result in low robustness against distribution shift.The main focus of this paper is answer-position bias, where a significantpercentage of training questions have answers located solely in the firstsentence of the context. We propose a Single-Sentence Reader as a new approachfor addressing answer position bias in MRC. Remarkably, in our experiments withsix different models, our proposed Single-Sentence Readers trained on biaseddataset achieve results that nearly match those of models trained on normaldataset, proving their effectiveness in addressing the answer position bias.Our study also discusses several challenges our Single-Sentence Readersencounter and proposes a potential solution.

Single-Sentence Reader: A Novel Approach for Addressing Answer Position Bias