To address this issue, we take inspiration from the FraCaS dataset (Cooper et al., 1996) and construct a suite of targeted datasets that separately test a system's ability to perform individual bits of interpretation such as paraphrasing, semantic role labeling, and coreference.
to the original FraCaS data set, which is relatively small and which could not support the training of purely lexical neural RTE classifiers, we pursue the strategy of automatically converting semantic classificationsi.e., human judgments about semantic propertiesinto labeled examples for textual entailment.