Papers with Discourse & Pragmatics

19 papers
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.
Si O No, Que Penses? Catalonian Independence and Linguistic Identity on Social Media (N18-2)

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Challenge: This study examines the use of Catalan on Twitter in discourse related to the 2017 independence referendum.
Approach: They use code-switching to determine the role of Catalan in political discourse . they corroborate prior findings that pro-independence tweets are more likely to include the local language than anti-independent tweets .
Outcome: The proposed method corroborates previous findings that pro-independence tweets are more likely to include the local language than anti-independent tweets.
Talking Point based Ideological Discourse Analysis in News Events (2025.findings-acl)

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Challenge: Existing models of ideological discourse analysis fail to capture the key elements that shape real-world narratives and lack the ability to integrate contextual information required for understanding abstract ideological views.
Approach: They propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events.
Outcome: The proposed framework can generate ideology-specific viewpoints (partisan perspectives) it can be used to generate event snapshots, a visual way of interpreting event discourse.
Identifying the Periodicity of Information in Natural Language (2026.acl-long)

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Challenge: Existing methods to detect periodicity of information in natural language are based on a canonical periodicity detection algorithm.
Approach: They propose a method to detect periods in surprisal sequences in natural language . they propose to use this method to identify periods outside the distributions of typical units .
Outcome: The proposed method can detect significant periods in a single document.
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.
EDTC: A Corpus for Discourse-Level Topic Chain Parsing (2021.findings-emnlp)

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Challenge: Discourse analysis is a fundamental part of natural language processing.
Approach: They propose a discourse-level topic chain parsing system which can be automated . they propose lexical cohesion modeling instead of lexically measuring topic structure .
Outcome: The proposed system is robust and reliable, and can provide high reliability and low confidence scores.
Toward Fast and Accurate Neural Discourse Segmentation (D18-1)

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Challenge: Existing discourse segmenters rely on complicated hand-crafted features and are not practical in actual use.
Approach: They propose an end-to-end neural segmenter based on BiLSTM-CRF framework that can segment texts fast and accurately using a large corpus.
Outcome: The proposed model is significantly faster than previous methods while achieving state-of-the-art performance on the RST-DT corpus.
Joint Learning of Syntactic Features Helps Discourse Segmentation (2020.lrec-1)

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Challenge: Discourse segmentation is a task of fragmenting text into minimal disjoint chunks of text called Elementary Discourse Units (EDUs).
Approach: They propose a framework for multi-lingual discourse segmentation with BERT . they cast the problem as a token classification problem and jointly learn syntactic features like part-of-speech tags and dependency relations.
Outcome: Experiments in English, Dutch, German, Portuguese Brazilian and Basque show that the proposed model performs better across languages.
Profiling News Discourse Structure Using Explicit Subtopic Structures Guided Critics (2021.findings-emnlp)

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Challenge: Experimental results show that the hierarchical model learns to segment a document into subtopics and improves performance on the news discourse profiling task.
Approach: They propose a hierarchical neural network that models multi-level interaction between sentences, subtopics, and the document.
Outcome: The proposed model outperforms the existing model on the news discourse profiling task.
CASIMIR: A Corpus of Scientific Articles Enhanced with Multiple Author-Integrated Revisions (2024.lrec-main)

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Challenge: CASIMIR dataset contains multiple revisions of 15,646 scientific articles . authors question the relevance of current evaluation methods for text revision .
Approach: They propose a textual resource on the revision step of the writing process of scientific articles.
Outcome: The proposed dataset contains the multiple revised versions of 15,646 scientific articles from OpenReview, along with their peer reviews.
Pártélet: A Hungarian Corpus of Propaganda Texts from the Hungarian Socialist Era (2020.lrec-1)

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Challenge: a digitized corpus of Communist propaganda texts is presented in this paper . it represents the direct political agitation and propaganda of the dictatorial system .
Approach: They present a digitized Hungarian corpus of Communist propaganda texts . they use a database to compile a large database of articles from the journal .
Outcome: The proposed dataset provides a unique opportunity for conducting research on Hungarian propaganda discourse . it also provides enables analysis of changes in the political discourse over a 35-year period .
A «Portrait» Approach to Multichannel Discourse (L18-1)

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Challenge: a new study examines the multichannel discourse analysis of human communication . we examine the individual variation in multichannel behavior .
Approach: They propose to use a multichannel resource to study multichannel discourses . they propose to analyze verbal structure, prosody, gesticulation, facial expression, eye gaze .
Outcome: The proposed method is crucially important for fine-grained annotation procedures and statistical analyses of multichannel data.
DiscoGeM 2.0: A Parallel Corpus of English, German, French and Czech Implicit Discourse Relations (2024.lrec-main)

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Challenge: DiscoGeM 2.0 is a crowdsourced, parallel corpus of 12,834 implicit discourse relations . implicit discourse relationships are highly ambiguous and can have various interpretations .
Approach: They propose a crowdsourced annotation method that can be extended to other languages . they propose to annotate 12,834 implicit discourse relations in German, German, French and Czech data .
Outcome: The proposed method can be extended to other languages and reveals that implicit relations inferred in one language may differ from those inferted in the translation.
Extending AZee with Non-manual Gesture Rules for French Sign Language (2024.lrec-main)

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Challenge: Currently, Sign Languages (SLs) are under-resourced and are difficult to develop.
Approach: They propose to extend AZee to formally represent Sign Language discourses, but also to animate them with a virtual signer.
Outcome: The proposed model allows to formally represent Sign Language discourses, but also to animate them with a virtual signer.
Improving Crowdsourcing-Based Annotation of Japanese Discourse Relations (L18-1)

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Challenge: Discourse parsing is an important task in natural language processing, but few languages have corpora annotated with discourse relations . crowdsourcing-based annotations are of poor quality and require expensive and time-consuming . et al. (2009) evaluated the quality of annotations using expert annotations.
Approach: They construct a Japanese corpus with discourse annotations through crowdsourcing . they propose improvement techniques based on language tests .
Outcome: The proposed methods improve the quality of the annotations, and will make them publicly available.
Paying Attention to Deflections: Mining Pragmatic Nuances for Whataboutism Detection in Online Discourse (2024.findings-acl)

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Challenge: Existing studies on whataboutism have focused on tracking "what about" phrases, but they neglect the unique challenges to its detection.
Approach: They propose to use attention weights to distinguish the ‘what about’ lexical construct from whataboutism by using Twitter/X and YouTube datasets.
Outcome: The proposed method improves by 4% and 10% over previous state-of-the-art methods in Twitter and YouTube datasets.
IsraParlTweet: The Israeli Parliamentary and Twitter Resource (2024.lrec-main)

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Challenge: IsraParlTweet is a linked corpus of parliamentary discussions from the Knesset between 1992-2023 and Twitter posts made by Members of the Kneset between 2008-2023.
Approach: They propose a linked corpus of parliamentary discussions from the Knesset between 1992-2023 and Twitter posts made by Members of the Kneset between 2008-2023.
Outcome: IsraParlTweet can be used to conduct quantitative and qualitative analyses and provide valuable insights into political discourse in Israel.
Born Pragmatic, Trained to Hallucinate? Quantifying the Origins of Contextual Bias in LLMs via the PaCE Benchmark (2026.findings-acl)

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Challenge: Large language models excel at capturing communicative intent, but they have a side effect: pragmatic hallucination.
Approach: They propose a benchmark to quantify the impact of pragmatic hallucination on large language models . they propose RLHF and SFT to induce a strong tendency for pragmatic over-attribution .
Outcome: The proposed model outperforms existing models in predicting pragmatic hallucinations . the evaluations show that current alignment paradigms lack precise control over pragmatic boundaries .
Stories and Personal Experiences in the COVID-19 Discourse (2024.lrec-main)

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Challenge: 'storytelling' is a human strategy to use personal experiences to back-up one's position in debates about controversial topics.
Approach: They analyse the use of storytelling in the COVID-19 discourse by automatically annotating three publicly available Reddit datasets for a total of 367K comments.
Outcome: The proposed analysis on three publicly available Reddit datasets shows that storytelling is a powerful argumentative tool.

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