QUDSELECT: Selective Decoding for Questions Under Discussion Parsing (2024.emnlp-main)
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| Challenge: | Question Under Discussion (QUD) uses implicit questions to reveal discourse relationships between sentences. |
| Approach: | They propose a framework that selectively decodes the QUD dependency structures considering the QUC criteria. |
| Outcome: | The proposed framework outperforms the state-of-the-art baseline models by 9% in human evaluation and 4% in automatic evaluation. |
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QUDeval: The Evaluation of Questions Under Discussion Discourse Parsing (2023.emnlp-main)
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| Challenge: | Existing evaluation metrics poorly approximate parser quality, says a new study . questions under discussion is a linguistic framework that views discourse as asking questions and answering them . |
| Approach: | They propose a framework for automatic evaluation of QUD parsing . they use a dataset of fine-grained evaluation of 2,190 QUD questions . |
| Outcome: | The proposed framework shows that satisfying constraints of QUD is still challenging for modern LLMs. |
Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion (2023.findings-acl)
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| Challenge: | Existing discourse formalisms require large taxonomies of discourse relations to be accurate. |
| Approach: | They propose a linguistic framework for discourse analysis using questions under discussion . they propose qUD parser that derives a dependency structure of questions over full documents . |
| Outcome: | The proposed model is trained on a large, crowdsourced question-answering dataset. |
A Survey of QUD Models for Discourse Processing (2025.naacl-long)
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| Challenge: | Question Under Discussion (QUD) is a linguistic analytic framework for explaining pragmatic phenomena and information structural analysis. |
| Approach: | They propose to use Question Under Discussion (QUD) to model discourse units, such as sentences, as answers to some implicit or explicit questions. |
| Outcome: | The proposed model is compared with RST, PDTB and SDRT . questions that may require further study are suggested. |
Towards automatically generating Questions under Discussion to link information and discourse structure (2020.coling-main)
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| Challenge: | Questions under Discussion (QUD) are emerging as a useful approach to spelling out the connection between information structure of sentences and nature of discourse. |
| Approach: | They propose a framework for QUD annotation based on explicit pragmatic principles . they propose generating all potentially relevant questions for a given sentence . |
| Outcome: | The proposed framework supports more reliable discourse structure annotation based on explicit questions . but the proposed approach is not robust enough for authentic data . |
QuASE: Question-Answer Driven Sentence Encoding (2020.acl-main)
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| Challenge: | Question-answering (QA) data often encodes essential information in many facets . a growing interest of QA has led to many large-scale QA datasets available to the community . |
| Approach: | They propose a question-answer driven sentence encoding framework to learn representations from QA data. |
| Outcome: | The proposed framework learns representations from QA data, using BERT or other state-of-the-art contextual language models. |
QUD-Based Annotation of Discourse Structure and Information Structure: Tool and Evaluation (L18-1)
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| Challenge: | a new annotation scheme and discourse-analytic method is developed for information structure annotation. |
| Approach: | They propose a new annotation scheme and a discourse-analytic method based on Questions under Discussion . they introduce a tool which enables the analyst to semi-automatically segment texts and enhance them with QUDs . |
| Outcome: | The proposed method achieves good inter-annotator scores and good agreement with discourse annotations. |
Integrating Question Rewrites in Conversational Question Answering: A Reinforcement Learning Approach (2022.acl-srw)
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| Challenge: | Existing approaches to improve QR performance dependencies among dialogue history dependencies are limited. |
| Approach: | They propose a reinforcement learning approach that integrates QR and CQA tasks without corresponding labeled QR datasets. |
| Outcome: | The proposed approach improves existing pipeline approaches in conversational question answering (QA) existing methods depend on assumption of corresponding QR datasets for every CQA dataset, resulting in poor performance. |
Elaborative Simplification as Implicit Questions Under Discussion (2023.emnlp-main)
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| Challenge: | Automated text simplification is often thought of as a monolingual translation task . this view fails to account for elaborative simplification, where new information is added into the simplified text. |
| Approach: | They propose to view elaborative simplification through the lens of the Question Under Discussion framework . they propose to model 1.3K elongations accompanied by implicit QUDs to investigate what writers elaborate upon . |
| Outcome: | The proposed framework provides a robust way to investigate what writers elaborate upon, how they elaborate, and how elaborations fit into the discourse context. |
Q-STRUM Debate: Query-Driven Contrastive Summarization for Recommendation Comparison (2025.findings-acl)
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| Challenge: | Existing contrastive summarization methods such as STRUM-LLM fail to clarify differences between items . emergence of large language models (LLMs) has revolutionized QCS capabilities . |
| Approach: | They propose a new method that generates focused and contrastive summaries by using debate-style prompting. |
| Outcome: | Experiments show that Q-STRUM Debate performs better than existing methods on key contrastive summarization criteria. |
Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering (2021.acl-long)
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| Challenge: | Existing approaches do not explicitly train QA models on how to resolve conversational dependency, and thus these models are limited in understanding human dialogues. |
| Approach: | They propose a framework that generates self-contained questions that can be understood without the conversation history and then trains a QA model with the pairs of original and self-constructed questions using a consistency-based regularizer. |
| Outcome: | The proposed framework improves the models’ performance by up to 1.2 F1 on QuAC, and 5.2 F1 for CANARD, while addressing the limitations of the existing approaches. |