Asking It All: Generating Contextualized Questions for any Semantic Role
| Autor: | Valentina Pyatkin, Paul Roit, Julian Michael, Yoav Goldberg, Reut Tsarfaty, Ido Dagan |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology 010501 environmental sciences 01 natural sciences Computation and Language (cs.CL) 0105 earth and related environmental sciences |
| Zdroj: | Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing |
| DOI: | 10.48550/arxiv.2109.04832 |
| Popis: | 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 context-independent 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, broad-coverage ontology of predicates and roles. Comment: Accepted as a long paper to EMNLP 2021, Main Conference |
| Databáze: | OpenAIRE |
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