Evaluating semantic evaluations: how RTE measures up

MACHINE LEARNING CHALLENGES: EVALUATING PREDICTIVE UNCERTAINTY VISUAL OBJECT CLASSIFICATION AND RECOGNIZING TEXTUAL ENTAILMENT(2005)

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摘要
In this paper, we discuss paradigms for evaluating open-domain semantic interpretation as they apply to the PASCAL Recognizing Textual Entailment (RTE) evaluation (Dagan et al. 2005). We focus on three aspects critical to a successful evaluation: creation of large quantities of reasonably good training data, analysis of inter-annotator agreement, and joint analysis of test item difficulty and test-taker proficiency (Rasch analysis). We found that although RTE does not correspond to a “real” or naturally occurring language processing task, it nonetheless provides clear and simple metrics, a tolerable cost of corpus development, good annotator reliability (with the potential to exploit the remaining variability), and the possibility of finding noisy but plentiful training material.
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关键词
language processing task,good annotator reliability,rasch analysis,pascal recognizing textual entailment,corpus development,successful evaluation,plentiful training material,joint analysis,good training data,semantic evaluation,inter-annotator agreement,semantic interpretation,data analysis
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