Second Recognising Textual Entailment Challenge

Textual Entailment Recognition has been proposed recently as a generic task that captures major semantic inference needs across many natural language processing applications, such as Question Answering (QA), Information Retrieval (IR), Information Extraction (IE), and (multi) document summarisation. This task requires to recognise, given two text fragments, whether the meaning of one text is entailed (can be inferred) from the other text. By introducing a second challenge we hope to keep the momentum going, and to further promote the formation of a research community around the applied entailment task. As in the previous challenge, the main task is judging whether a hypothesis (H) is entailed by a text (T). One of the main goals for the RTE-2 dataset is to provide more “realistic” text-hypothesis examples, based mostly on outputs of actual systems. We focus on the four application settings mentioned above: QA, IR, IE and multi-document summarisation. Each portion of the dataset includes typical T-H examples that correspond to success and failure cases of such applications. The examples represent different levels of entailment reasoning, such as lexical, syntactic, morphological and logical.