| 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. |
Similar Papers
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. |
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. |
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. |
Discourse Analysis and Its Applications (P19-4)
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| Challenge: | Discourse processing is a suite of NLP tasks to uncover linguistic structures from texts at several levels, which can support many downstream applications. |
| Approach: | They present a set of tasks to uncover linguistic structures from texts at several levels, which can support many downstream applications. |
| Outcome: | The tutorial covers the basic concepts of discourse analysis and linguistic structures in monologue vs. conversation, synchronous v. asynchronous conversation, and key linguistic structure in discourse analysis. |
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 . |
Evaluation and Facilitation of Online Discussions in the LLM Era: A Survey (2025.emnlp-main)
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Katerina Korre, Dimitris Tsirmpas, Nikos Gkoumas, Emma Cabalé, Danai Myrtzani, Theodoros Evgeniou, Ion Androutsopoulos, John Pavlopoulos
| Challenge: | Recent advances in LLMs enable artificial facilitation agents to not only moderate content, but also actively improve the quality of interactions. |
| Approach: | They propose a taxonomy on discussion quality evaluation and a new taxonomies for intervention and facilitation strategies. |
| Outcome: | The proposed methods synthesize ideas from Natural Language Processing (NLP) and Social Sciences to provide a taxonomy on discussion quality evaluation, and a roadmap of good practices and future research directions. |
Testing Focus and Non-at-issue Frameworks with a Question-under-Discussion-Annotated Corpus (2022.lrec-1)
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| Challenge: | Annotated German driving reports for question-under-discussion analysis are lacking in the literature on QUDs. |
| Approach: | They propose to annotate a German driving report corpus for QUD analysis . they show focus-related meaning aspects are essentially confirmed . |
| Outcome: | The annotated corpus of German driving reports shows that focus-related meaning aspects are essentially confirmed, indicating a sufficent accuracy of the annotations. |
Discourse Representation Parsing for Sentences and Documents (P19-1)
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| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
| Approach: | They propose a neural model which parses discourse structures of arbitrary length and granularity. |
| Outcome: | The proposed model outperforms baseline models on sentence- and document-level benchmarks. |
Towards Unification of Discourse Annotation Frameworks (2022.acl-srw)
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| Challenge: | Discourse information is difficult to represent and annotate, and corpora annotated under different frameworks vary considerably. |
| Approach: | They propose to use automatic means to unify discourse structures and relations . they will also explore the application of the unified framework in multi-task learning and graphical models . |
| Outcome: | The proposed method can be used in multi-task learning and graphical models. |