Calling Out Bluff: Attacking the Robustness of Automatic Scoring Systems with Simple Adversarial Testing
Table 6: Human Annotation Results. (↓ represents a decrease and ↑ represents an increase. Therefore, ‘% People ↓’ denotes the percentage of people who scored the adversarial response worse than the original response)
['[BOLD] #', '[BOLD] Perturbation', '[BOLD] Score ↓ %', '[BOLD] % People ↓', '[BOLD] % People ↑', '[BOLD] Common Reasons of ↓', '[BOLD] Common Reasons of ↑']
We divide the annotators into two groups. For the first group, we show them the original response and its corresponding score and then ask the annotators to score the adversarial response accordingly. For the second group, we ask them to score both the original and adversarial responses. If any of the annotators felt that the scores of the original and adversarial responses should not be the same, we ask them to list supporting reasons. For uniformity in responses, we derive a set of scoring rubrics extracted mentioned in our dataset and ask them to choose the most suitable ones. The main reasons for scoring adversarial responses lower by annotators are Relevance, Organization, Readability etc.. It can be observed that the % lowering in score was on an average of 30%.