Learning to paraphrase: an unsupervised approach using multiple-sequence alignment
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Introduction to Information Retrieval
Introduction to Information Retrieval
Generating an entailment corpus from news headlines
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
"Ask not what textual entailment can do for you..."
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Generalizing sub-sentential paraphrase acquisition across original signal type of text pairs
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Entailment pairs are sentence pairs of a premise and a hypothesis, where the premise textually entails the hypothesis. Such sentence pairs are important for the development of Textual Entailment systems. In this paper, we take a closer look at a prominent strategy for their automatic acquisition from newspaper corpora, pairing first sentences of articles with their titles. We propose a simple logistic regression model that incorporates and extends this heuristic and investigate its robustness across three languages and three domains. We manage to identify two predictors which predict entailment pairs with a fairly high accuracy across all languages. However, we find that robustness across domains within a language is more difficult to achieve.