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

TL;DR

This paper investigates socially-aware question generation by analyzing social media data, demonstrating that incorporating social group information improves question generation accuracy for diverse audiences.

Contribution

It introduces a new dataset with social group annotations and develops models that effectively incorporate social information for question generation.

Findings

Social groups ask significantly different types of questions.

Socially-aware models outperform text-only models in diverse social contexts.

The discrete social-representation model shows the best performance.

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.