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Standard NLP tasks do not incorporate several common real-world scenariossuch as seeking clarifications about the question, taking advantage of clues,abstaining in order to avoid incorrect answers, etc. This difference in taskformulation hinders the adoption of NLP systems in real-world settings. In thiswork, we take a step towards bridging this gap and present a multi-stage taskthat simulates a typical human-human questioner-responder interaction such asan interview. Specifically, the system is provided with questionsimplifications, knowledge statements, examples, etc. at various stages toimprove its prediction when it is not sufficiently confident. We instantiatethe proposed task in Natural Language Inference setting where a system isevaluated on both in-domain and out-of-domain (OOD) inputs. We conductcomprehensive experiments and find that the multi-stage formulation of our taskleads to OOD generalization performance improvement up to 2.29% in Stage 1,1.91% in Stage 2, 54.88% in Stage 3, and 72.02% in Stage 4 over the standardunguided prediction. However, our task leaves a significant challenge for NLPresearchers to further improve OOD performance at each stage.

Interviewer-Candidate Role Play: Towards Developing Real-World NLP Systems