Inquisitive questions -open-ended, curiositydriven questions people ask as they read -are an integral part of discourse processing (Van Kuppevelt, 1995;Onea, 2016;Kehler and Rohde, 2017) 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 potential 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.This paper presents QSALIENCE, a salience predictor of inquisitive questions.QSALIENCE is instruction-tuned over our dataset of linguistannotated 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.

Questions Under Discussion (QUD) is a versatile linguistic framework in which discourse progresses as continuously asking questions and answering them.Automatic parsing of a discourse to produce a QUD structure thus entails a complex question generation task: given a document and an answer sentence, generate a question that satisfies linguistic constraints of QUD and can be grounded in an anchor sentence in prior context.These questions are known to be curiosity-driven and open-ended.This work introduces the first framework for the automatic evaluation of QUD parsing, instantiating the theoretical constraints of QUD in a concrete protocol.We present QUDE-VAL, a dataset of fine-grained evaluation of 2,190 QUD questions generated from both finetuned systems and LLMs.Using QUDEVAL, we show that satisfying all constraints of QUD is still challenging for modern LLMs, and that existing evaluation metrics poorly approximate parser quality.Encouragingly, human-authored QUDs are scored highly by our human evaluators, suggesting that there is headroom for further progress on language modeling to improve both QUD parsing and QUD evaluation.

Automated text simplification, a technique useful for making text more accessible to people such as children and emergent bilinguals, is often thought of as a monolingual translation task from complex to simplified text.This view fails to account for elaborative simplification, where new information is added into the simplified text.This paper proposes to view elaborative simplification through the lens of the Question Under Discussion (QUD) framework, providing a robust way to investigate what writers elaborate upon, how they elaborate, and how elaborations fit into the discourse context by viewing elaborations as explicit answers to implicit questions.We introduce ELABQUD, consisting of 1.3K elaborations accompanied with implicit QUDs, to study these phenomena.We show that explicitly modeling QUD (via question generation) not only provides essential understanding of elaborative simplification and how the elaborations connect with the rest of the discourse, but also substantially improves the quality of elaboration generation.