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Single-Sentence Reader: A Novel Approach for Addressing Answer Position Bias. (arXiv:2308.04566v1 [cs.CL])
cs.CL updates on arXiv.org arxiv.org
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. …
annotation arxiv bias community context correlations dataset machine novel reading research research community