DQI: Measuring Data Quality in NLP
Table 12: Sample counts for Splits across Labels
['[BOLD] Split/Label', '[BOLD] Entailment', '[BOLD] Neutral', '[BOLD] Contradiction']
S6SS6SSS0Px2 Contradiction samples are seen to be more prone to spurious bias In order to compute the terms, the good and bad splits of data were further divided into three subsets each, corresponding to the gold labels of samples. On creating these subsets, we note that the ratio of contradiction samples in the good and bad categories is much higher than that seen in the case of entailment and neutral labels.