Back to Search
Start Over
Asking It All: Generating Contextualized Questions for any Semantic Role
- Source :
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Publication Year :
- 2021
- Publisher :
- arXiv, 2021.
-
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 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.<br />Comment: Accepted as a long paper to EMNLP 2021, Main Conference
- Subjects :
- 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
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Accession number :
- edsair.doi.dedup.....30f7ea8a8fee68169ba7d15be3076d71
- Full Text :
- https://doi.org/10.48550/arxiv.2109.04832