Ask what’s missing and what’s useful: Improving Clarification Question Generation using Global Knowledge
Bodhisattwa Prasad Majumder, Sudha Rao, Michel Galley, Julian McAuley
Abstract
The ability to generate clarification questions i.e., questions that identify useful missing information in a given context, is important in reducing ambiguity. Humans use previous experience with similar contexts to form a global view and compare it to the given context to ascertain what is missing and what is useful in the context. Inspired by this, we propose a model for clarification question generation where we first identify what is missing by taking a difference between the global and the local view and then train a model to identify what is useful and generate a question about it. Our model outperforms several baselines as judged by both automatic metrics and humans.
BibTeX
@inproceedings{majumder-etal-2021-ask,
title = "Ask what{'}s missing and what{'}s useful: Improving Clarification Question Generation using Global Knowledge",
author = "Majumder, Bodhisattwa Prasad and
Rao, Sudha and
Galley, Michel and
McAuley, Julian",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.340/",
doi = "10.18653/v1/2021.naacl-main.340",
pages = "4300--4312"
}