@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",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Ask what’s missing and what’s useful: Improving Clarification Question Generation using Global Knowledge
%A Majumder, Bodhisattwa Prasad
%A Rao, Sudha
%A Galley, Michel
%A McAuley, Julian
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F majumder-etal-2021-ask
%X 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.
%R 10.18653/v1/2021.naacl-main.340
%U https://aclanthology.org/2021.naacl-main.340
%U https://doi.org/10.18653/v1/2021.naacl-main.340
%P 4300-4312
Markdown (Informal)
[Ask what’s missing and what’s useful: Improving Clarification Question Generation using Global Knowledge](https://aclanthology.org/2021.naacl-main.340) (Majumder et al., NAACL 2021)
ACL