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

arXiv: https://arxiv.org/abs/2110.08445 · HTML: https://mlsanity.com/paper/2110.08445/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 40 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts](https://mlsanity.com/paper/2105.03023/) (2021)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [TWEETQA: A Social Media Focused Question Answering Dataset](https://mlsanity.com/paper/1907.06292/) (2019)
- [Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia](https://mlsanity.com/paper/1805.05942/) (2018)
- [Answer-based Adversarial Training for Generating Clarification Questions](https://mlsanity.com/paper/1904.02281/) (2019)
- [Controllable Open-ended Question Generation with A New Question Type Ontology](https://mlsanity.com/paper/2107.00152/) (2021)
- [ClarQ: A large-scale and diverse dataset for Clarification Question Generation](https://mlsanity.com/paper/2006.05986/) (2020)
- [Compositional Demographic Word Embeddings](https://mlsanity.com/paper/2010.02986/) (2020)
- [Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations](https://mlsanity.com/paper/2004.14530/) (2020)
- [Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance](https://mlsanity.com/paper/1910.09180/) (2019)
- [Social Media-based User Embedding: A Literature Review](https://mlsanity.com/paper/1907.00725/) (2019)
- [You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP](https://mlsanity.com/paper/1909.00412/) (2019)

## Abstract (arXiv)

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.