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.
翻译:写作时, 一个人可能需要预知观众的问题, 但不同的社会团体可能会问非常不同的问题。 如果有人正在写出他们想要解决的问题, 那么一个域专家会问什么样的后续问题, 作者能否通过重写其原始文章更好地解决专家的信息需求? 在本文中, 我们探索社会意识问题生成的任务。 我们从社交媒体收集一系列问题和文章的数据, 包括有关提问者社会群体的背景资料。 我们发现, 不同的社会群体, 如专家和新手, 总是会问不同种类的问题。 我们训练了几种包含社会信息的文本生成模型, 我们发现, 当不同的社会群体提出截然不同的问题时, 一个独立的社会代表性模型比文本型的模型优于文本型。 我们的工作为开发文本生成模型提供了一个框架, 帮助作者预测高度不同的社会群体的信息期望。