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Asking It All: Generating Contextualized Questions for any Semantic Role

Valentina Pyatkin, Paul Roit, Julian Michael, Yoav Goldberg, Reut Tsarfaty, Ido Dagan

2021Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing23 citationsDOIOpen Access PDF

Abstract

Asking questions about a situation is an inherent step towards understanding it. To this end, we introduce the task of role question generation, which, given a predicate mention and a passage, requires producing a set of questions asking about all possible semantic roles of the predicate. We develop a two-stage model for this task, which first produces a contextindependent question prototype for each role and then revises it to be contextually appropriate for the passage. Unlike most existing approaches to question generation, our approach does not require conditioning on existing answers in the text. Instead, we condition on the type of information to inquire about, regardless of whether the answer appears explicitly in the text, could be inferred from it, or should be sought elsewhere. Our evaluation demonstrates that we generate diverse and well-formed questions for a large, broadcoverage ontology of predicates and roles.

Topics & Concepts

Computer sciencePredicate (mathematical logic)Task (project management)Set (abstract data type)Natural language processingOntologyContext (archaeology)Artificial intelligenceEpistemologyProgramming languageEconomicsManagementPaleontologyBiologyPhilosophyTopic ModelingNatural Language Processing TechniquesSpeech and dialogue systems
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