A Survey on Recognizing Textual Entailment as an NLP Evaluation
It addresses the need for better evaluation methods in NLP, but is incremental as a survey paper.
The paper surveys Recognizing Textual Entailment (RTE) as a framework for evaluating semantic understanding in NLP systems, highlighting datasets that focus on specific linguistic phenomena for fine-grained assessment.
Recognizing Textual Entailment (RTE) was proposed as a unified evaluation framework to compare semantic understanding of different NLP systems. In this survey paper, we provide an overview of different approaches for evaluating and understanding the reasoning capabilities of NLP systems. We then focus our discussion on RTE by highlighting prominent RTE datasets as well as advances in RTE dataset that focus on specific linguistic phenomena that can be used to evaluate NLP systems on a fine-grained level. We conclude by arguing that when evaluating NLP systems, the community should utilize newly introduced RTE datasets that focus on specific linguistic phenomena.