Which questions should I answer? Salience Prediction of Inquisitive
  Questions
Yating Wu Ritika Mangla Alexandros G. Dimakis Greg Durrett Junyi Jessy Li
Inquisitive questions -- open-ended, curiosity-driven questions people ask as
they read -- are an integral part of discourse processing (Kehler and Rohde,
2017; Onea, 2016) and comprehension (Prince, 2004). Recent work in NLP has
taken advantage of question generation capabilities of LLMs to enhance a wide
range of applications. But the space of inquisitive questions is vast: many
questions can be evoked from a given context. So which of those should be
prioritized to find answers? Linguistic theories, unfortunately, have not yet
provided an answer to this question. This paper presents QSALIENCE, a salience
predictor of inquisitive questions. QSALIENCE is instruction-tuned over our
dataset of linguist-annotated salience scores of 1,766 (context, question)
pairs. A question scores high on salience if answering it would greatly enhance
the understanding of the text (Van Rooy, 2003). We show that highly salient
questions are empirically more likely to be answered in the same article,
bridging potential questions (Onea, 2016) with Questions Under Discussion
(Roberts, 2012). We further validate our findings by showing that answering
salient questions is an indicator of summarization quality in news.