  The quality of training data is one of the crucial problems when a
learning-centered approach is employed. This paper proposes a new method to
investigate the quality of a large corpus designed for the recognizing textual
entailment (RTE) task. The proposed method, which is inspired by a statistical
hypothesis test, consists of two phases: the first phase is to introduce the
predictability of textual entailment labels as a null hypothesis which is
extremely unacceptable if a target corpus has no hidden bias, and the second
phase is to test the null hypothesis using a Naive Bayes model. The
experimental result of the Stanford Natural Language Inference (SNLI) corpus
does not reject the null hypothesis. Therefore, it indicates that the SNLI
corpus has a hidden bias which allows prediction of textual entailment labels
from hypothesis sentences even if no context information is given by a premise
sentence. This paper also presents the performance impact of NN models for RTE
caused by this hidden bias.
