Abstract

This paper presents the Third PASCAL Recognising Textual Entailment Challenge (RTE-3), providing an overview of the dataset creating methodology and the submitted systems. In creating this year’s dataset, a number of longer texts were introduced to make the challenge more oriented to realistic scenarios. Additionally, a pool of resources was offered so that the participants could share common tools. A pilot task was also set up, aimed at differentiating unknown entailments from identified contradictions and providing justifications for overall system decisions. 26 participants submitted 44 runs, using different approaches and generally presenting new entailment models and achieving higher scores than in the previous challenges. 1.1 The RTE challenges The goal of the RTE challenges has been to create a benchmark task dedicated to textual entailment – recognizing that the meaning of one text is entailed, i.e. can be inferred, by another 1. In the recent years, this task has raised great interest since applied semantic inference concerns

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