EMNLP 2021main39 citations

Asking It All: Generating Contextualized Questions for any Semantic Role

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

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.

BibTeX
@inproceedings{pyatkin-etal-2021-asking,
    title = "Asking It All: Generating Contextualized Questions for any Semantic Role",
    author = "Pyatkin, Valentina  and
      Roit, Paul  and
      Michael, Julian  and
      Goldberg, Yoav  and
      Tsarfaty, Reut  and
      Dagan, Ido",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.108/",
    doi = "10.18653/v1/2021.emnlp-main.108",
    pages = "1429--1441"
}
Asking It All: Generating Contextualized Questions for any Semantic Role · EMNLP 2021