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.

Similar Papers

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.
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 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.
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.
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.
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 .
TIARA: A Tool for Annotating Discourse Relations and Sentence Reordering (2020.lrec-1)

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Challenge: Existing tools for discourse relations and sentence reordering are difficult to use and clutter the display.
Approach: They propose to use TIARA to simplify the annotation process by offering interactive visualisation, including coloured links, indentation, and dual-view.
Outcome: The proposed tool simplifies the annotation process and offers visualisations including coloured links, indentation, and dual-view.
A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd (N19-1)

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Challenge: Existing methods for analyzing discourse-level argument annotations require expensive labor and data.
Approach: They propose a method that breaks down a popular but complex discourse-level argument annotation scheme into a simple iterative procedure that can be applied even by untrained annotators.
Outcome: The proposed method can be applied even by untrained annotators.
An Environment for Relational Annotation of Political Debates (P19-3)

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Challenge: Scalable text analysis techniques can open corpora to new questions in computational social sciences and digital humanities.
Approach: They describe a tool that allows annotating newspaper text with rich information about claims (demands) raised by politicians and other actors.
Outcome: The MARDY tool realizes the complete workflow necessary for annotating a large newspaper text collection with rich information about claims (demands) raised by politicians and other actors.
A Multi-layer Annotated Corpus of Argumentative Text: From Argument Schemes to Discourse Relations (L18-1)

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Challenge: Recent interest in Argumentation Mining has brought to the fore the need for corpora annotated with argument information, which can be used as training data.
Approach: They propose a set of guidelines for the annotation of argument schemes and a new annotation tool for the 'inferential' argument schemes.
Outcome: The proposed corpus includes 112 argumentative microtexts and a new annotation tool.

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