| 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. |
Similar Papers
Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Study (2024.lrec-main)
Copied to clipboard
| Challenge: | Large language models have shown remarkable capability in many downstream tasks, yet their ability to understand discourse structures of dialogues remains less explored. |
| Approach: | They aim to systematically inspect ChatGPT’s performance in two discourse analysis tasks: topic segmentation and discourse parsing. |
| Outcome: | The proposed model can give more reasonable topic structures than human annotations but only linearly parses the hierarchical rhetorical structures. |
NLP for Conversations: Sentiment, Summarization, and Group Dynamics (C18-3)
Copied to clipboard
| Challenge: | a tutorial focuses on computational models for conversational structure, summarization and sentiment detection, and group dynamics. |
| Approach: | a tutorial will provide examples of specific NLP tasks for conversational structure, summarization and sentiment detection, and group dynamics. |
| Outcome: | The tutorial focuses on the three areas of conversational structure, summarization and sentiment detection, and group dynamics. |
Discourse Representation Parsing for Sentences and Documents (P19-1)
Copied to clipboard
| 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. |
A Survey of QUD Models for Discourse Processing (2025.naacl-long)
Copied to clipboard
| 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. |
Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining (2020.coling-main)
Copied to clipboard
| Challenge: | Discourse parsing is an important upstream task within the area of Natural Language Processing (NLP) . |
| Approach: | They propose a discourse parser that incorporates recent contextual language models to improve the performance of RST-based discourse parses. |
| Outcome: | The proposed parser outperforms existing models on two key RST datasets and on large-scale "silver-standard" discourse treebank MEGA-DT. |
WebDP: Understanding Discourse Structures in Semi-Structured Web Documents (2023.findings-acl)
Copied to clipboard
| Challenge: | Web documents are one of the most primary and biggest data resources in current era, and understanding their discourse structure will benefit various downstream document processing applications. |
| Approach: | They propose a web document discourse structure representation schema by extending classical discourse theories and adding special features to well represent discourse characteristics of web documents. |
| Outcome: | The proposed task is feasible but challenging for current models. |
Summarization of Dialogues and Conversations At Scale (2023.eacl-tutorials)
Copied to clipboard
| Challenge: | Conversations are the natural communication format for people. |
| Approach: | This tutorial will survey the cutting-edge methods for summarizing written and spoken conversation. |
| Outcome: | This tutorial will examine the cutting-edge methods for summarizing written and spoken conversations, covering key sub-areas whose combination is needed for a successful solution. |
Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion (2023.findings-acl)
Copied to clipboard
| 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. |
Enhancing Discourse Parsing for Local Structures from Social Media with LLM-Generated Data (2025.coling-main)
Copied to clipboard
| Challenge: | Existing discourse parsers do not generalize well across genres and text types. |
| Approach: | They propose to integrate large language models into RST discourse parsers to improve parser performance in a social media context. |
| Outcome: | The proposed model improves parser performance in a social media context without pre-identified discourse units. |
Evaluating Discourse in Structured Text Representations (P19-1)
Copied to clipboard
| Challenge: | Discourse structure is integral to understanding a text and is useful in many NLP tasks. |
| Approach: | They propose a structured attention mechanism for text classification that derives a tree over a text, akin to an RST discourse tree. |
| Outcome: | The proposed model improves performance on multiple discourse-relevant tasks and datasets and ablation studies show it does little to capture discourse structure. |