# Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion

arXiv: https://arxiv.org/abs/2210.05905 · HTML: https://mlsanity.com/paper/2210.05905/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 83 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Evaluation of Text Generation: A Survey](https://mlsanity.com/paper/2006.14799/) (2020)
- [Discourse-Aware Neural Extractive Text Summarization](https://mlsanity.com/paper/1910.14142/) (2019)
- [Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints](https://mlsanity.com/paper/1603.08887/) (2016)
- [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)
- [Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification](https://mlsanity.com/paper/2010.10035/) (2020)
- [Neural Discourse Structure for Text Categorization](https://mlsanity.com/paper/1702.01829/) (2017)
- [Better Document-level Sentiment Analysis from RST Discourse Parsing](https://mlsanity.com/paper/1509.01599/) (2015)
- [Discourse Level Factors for Sentence Deletion in Text Simplification](https://mlsanity.com/paper/1911.10384/) (2019)
- [Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections](https://mlsanity.com/paper/2111.00701/) (2021)
- [Evaluating Discourse in Structured Text Representations](https://mlsanity.com/paper/1906.01472/) (2019)

## Built on by (most cited first, 13 of 13)

- [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)
- [QUDsim: Quantifying Discourse Similarities in LLM-Generated Text](https://mlsanity.com/paper/2504.09373/) (2025)
- [Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions](https://mlsanity.com/paper/2402.13551/) (2024)
- [QUDSELECT: Selective Decoding for Questions Under Discussion Parsing](https://mlsanity.com/paper/2408.01046/) (2024)
- [Explanatory Summarization with Discourse-Driven Planning](https://mlsanity.com/paper/2504.19339/) (2025)
- [Discursive Circuits: How Do Language Models Understand Discourse Relations?](https://mlsanity.com/paper/2510.11210/) (2025)
- [Hey, wait a minute: on at-issue sensitivity in Language Models](https://mlsanity.com/paper/2510.12740/) (2025)
- [A Survey of QUD Models for Discourse Processing](https://mlsanity.com/paper/2502.15573/) (2025)
- [Identifying & Interactively Refining Ambiguous User Goals for Data Visualization Code Generation](https://mlsanity.com/paper/2510.09390/) (2025)
- [DiscoTrace: Representing and Comparing Answering Strategies of Humans and LLMs in Information-Seeking Question Answering](https://mlsanity.com/paper/2604.15140/) (2026)
- [Multimodal QUD: Inquisitive Questions from Scientific Figures](https://mlsanity.com/paper/2604.23733/) (2026)
- [HKJudge: A Legal Discourse-Annotated Corpus for Interpreting What Courts Find, How They Reason, and What They Rule](https://mlsanity.com/paper/2606.06679/) (2026)

## Abstract (arXiv)

Automatic discourse processing is bottlenecked by data: current discourse formalisms pose highly demanding annotation tasks involving large taxonomies of discourse relations, making them inaccessible to lay annotators. This work instead adopts the linguistic framework of Questions Under Discussion (QUD) for discourse analysis and seeks to derive QUD structures automatically. QUD views each sentence as an answer to a question triggered in prior context; thus, we characterize relationships between sentences as free-form questions, in contrast to exhaustive fine-grained taxonomies. We develop the first-of-its-kind QUD parser that derives a dependency structure of questions over full documents, trained using a large, crowdsourced question-answering dataset DCQA (Ko et al., 2022). Human evaluation results show that QUD dependency parsing is possible for language models trained with this crowdsourced, generalizable annotation scheme. We illustrate how our QUD structure is distinct from RST trees, and demonstrate the utility of QUD analysis in the context of document simplification. Our findings show that QUD parsing is an appealing alternative for automatic discourse processing.