Evaluating Paraphrastic Robustness in Textual Entailment Models
Dhruv Verma Yash Kumar Lal Shreyashee Sinha Benjamin Van Durme Adam Poliak
We present PaRTE, a collection of 1,126 pairs of Recognizing Textual
Entailment (RTE) examples to evaluate whether models are robust to
paraphrasing. We posit that if RTE models understand language, their
predictions should be consistent across inputs that share the same meaning. We
use the evaluation set to determine if RTE models' predictions change when
examples are paraphrased. In our experiments, contemporary models change their
predictions on 8-16\% of paraphrased examples, indicating that there is still
room for improvement.