How Well Do You Know Your Audience? Toward Socially-aware Question Generation

Ian Stewart, Rada Mihalcea


Abstract
When writing, a person may need to anticipate questions from their audience, but different social groups may ask very different types of questions. If someone is writing about a problem they want to resolve, what kind of follow-up question will a domain expert ask, and could the writer better address the expert’s information needs by rewriting their original post? In this paper, we explore the task of socially-aware question generation. We collect a data set of questions and posts from social media, including background information about the question-askers’ social groups. We find that different social groups, such as experts and novices, consistently ask different types of questions. We train several text-generation models that incorporate social information, and we find that a discrete social-representation model outperforms the text-only model when different social groups ask highly different questions from one another. Our work provides a framework for developing text generation models that can help writers anticipate the information expectations of highly different social groups.
Anthology ID:
2022.sigdial-1.27
Volume:
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:
September
Year:
2022
Address:
Edinburgh, UK
Editors:
Oliver Lemon, Dilek Hakkani-Tur, Junyi Jessy Li, Arash Ashrafzadeh, Daniel Hernández Garcia, Malihe Alikhani, David Vandyke, Ondřej Dušek
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
255–269
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2022.sigdial-1.27/
DOI:
10.18653/v1/2022.sigdial-1.27
Bibkey:
Cite (ACL):
Ian Stewart and Rada Mihalcea. 2022. How Well Do You Know Your Audience? Toward Socially-aware Question Generation. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 255–269, Edinburgh, UK. Association for Computational Linguistics.
Cite (Informal):
How Well Do You Know Your Audience? Toward Socially-aware Question Generation (Stewart & Mihalcea, SIGDIAL 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2022.sigdial-1.27.pdf
Video:
 https://youtu.be/QMhSJXon4Z4