Textual entailment using machine translation evaluation metrics

Abstract

In this paper we propose a novel approach to determine Textual Entailment (TE) relation between a pair of text expressions. Different machine translation (MT) along with summary evaluation metrics and polarity feature have been used as features for different machine learning classifiers to take the entailment decision in this study. We consider three machine translation evaluation metrics, namely BLEU, METEOR and TER and a summary evaluation metric namely ROUGE as similarity metrics for this task. We also used the negation polarity feature in combination with the similarity measure features. We performed experiments on the datasets released in the shared tasks on textual entailment organized in RTE-1, RTE-2, RTE-3, RTE-4 and RTE-5. The best classification accuracies obtained by our system on the RTE-1, RTE-2, RTE-3, RTE-4 and RTE-5 datasets are 54%, 55%, 60%, 52% and 51% respectively.