# A Survey of QUD Models for Discourse Processing

arXiv: https://arxiv.org/abs/2502.15573 · HTML: https://mlsanity.com/paper/2502.15573/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Conditional Generation with a Question-Answering Blueprint](https://mlsanity.com/paper/2207.00397/) (2022)
- [QADiscourse -- Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines](https://mlsanity.com/paper/2010.02815/) (2020)
- [Inquisitive Question Generation for High Level Text Comprehension](https://mlsanity.com/paper/2010.01657/) (2020)
- [Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections](https://mlsanity.com/paper/2111.00701/) (2021)
- [QUDEVAL: The Evaluation of Questions Under Discussion Discourse Parsing](https://mlsanity.com/paper/2310.14520/) (2023)
- [Elaborative Simplification as Implicit Questions Under Discussion](https://mlsanity.com/paper/2305.10387/) (2023)
- [Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion](https://mlsanity.com/paper/2210.05905/) (2022)
- [Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions](https://mlsanity.com/paper/2402.13551/) (2024)
- [Towards Unification of Discourse Annotation Frameworks](https://mlsanity.com/paper/2204.07781/) (2022)
- [Which questions should I answer? Salience Prediction of Inquisitive Questions](https://mlsanity.com/paper/2404.10917/) (2024)
- [QUDSELECT: Selective Decoding for Questions Under Discussion Parsing](https://mlsanity.com/paper/2408.01046/) (2024)

## Built on by (most cited first, 1 of 1)

- [Discursive Circuits: How Do Language Models Understand Discourse Relations?](https://mlsanity.com/paper/2510.11210/) (2025)

## Abstract (arXiv)

Question Under Discussion (QUD), which is originally a linguistic analytic framework, gains increasing attention in the community of natural language processing over the years. Various models have been proposed for implementing QUD for discourse processing. This survey summarizes these models, with a focus on application to written texts, and examines studies that explore the relationship between QUD and mainstream discourse frameworks, including RST, PDTB and SDRT. Some questions that may require further study are suggested.