Recognising Textual Entailment Challenge

Recent years have seen a surge in research of text processing applications that perform semantic-oriented inference about concrete text meanings and their relationships. Even though many applications face similar underlying semantic problems, these problems are usually addressed in an application oriented manner. Consequently it is difficult to compare, under a generic evaluation framework, semantic methods that were developed within different applications. The PASCAL Challenge introduces textual entailment as a common task and evaluation framework for Natural Language Processing, Information Retrieval and Machine Learning researchers, covering a broad range of semantic-oriented inferences needed for practical applications. This task is therefore suitable for evaluating and comparing semantic-oriented models in a generic manner. Eventually, work on textual entailment may promote the development of generic semantic “engines”, which will play an analogous role to that of generic syntactic analyzers across multiple applications.