| Challenge: | Discourse parsing is a task of predicting relationships between utterances and their semantic content . lack of surface cues in discourse graphs forces parsers to rely on deep, semantic information . a large language model (LLM) can significantly improve discourse parser performance . |
| Approach: | They propose a large language model (LLM) that leverages discourse context to parse a discourse . this model provides local, context-sensitive representations of discourse units . |
| Outcome: | The proposed model can provide local, context-sensitive representations of discourse units . it can process discourse data incrementally, which is essential for later use of discourse information . |
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Can we obtain significant success in RST discourse parsing by using Large Language Models? (2024.eacl-long)
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| Challenge: | Experimental results show that LLMs with tens of billion parameters can perform discourse parsing tasks. |
| Approach: | They employ Llama 2 and fine-tune it with QLoRA to achieve similar results . they show that LLMs with tens of billion parameters can perform a wide range of NLP tasks . |
| Outcome: | The proposed model performs better than existing models on three benchmark datasets. |
Enhancing Discourse Parsing for Local Structures from Social Media with LLM-Generated Data (2025.coling-main)
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| 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. |
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. |
Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining (2020.coling-main)
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| 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. |
A Complete Shift-Reduce Chinese Discourse Parser with Robust Dynamic Oracle (2020.acl-main)
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| Challenge: | Existing work on hierarchical discourse parsing in English is based on the RST-style one. |
| Approach: | They propose a Chinese discourse parser that integrates pre-trained text encoder and employs novel training strategies to improve rhetorical relation recognition. |
| Outcome: | The proposed system achieves state-of-the-art performance in Chinese discourse parsing. |
Top-down Discourse Parsing via Sequence Labelling (2021.eacl-main)
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| Challenge: | Discourse analysis is a systematic way to understand how texts are segmented hierarchically into discourse units. |
| Approach: | They propose a top-down approach to discourse parsing that is conceptually simpler than its predecessors. |
| Outcome: | The proposed model eliminates the decoder and reduces the search space for splitting points. |
Are LLMs Good Annotators for Discourse-level Event Relation Extraction? (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated proficiency in a wide array of natural language processing tasks, but their effectiveness over discourse-level event relation extraction tasks remains unexplored. |
| Approach: | They evaluate LLMs' ability to address discourse-level event relation extraction tasks using an open-source model and a commercial model. |
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CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversations (2025.findings-acl)
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| Challenge: | Discourse parsing datasets based on conversations are restricted to a single domain . a lack of discourse structures in audio-based conversations is a challenge . |
| Approach: | They introduce CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse parsing in conversations. |
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A Language Model-based Generative Classifier for Sentence-level Discourse Parsing (2021.emnlp-main)
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| Challenge: | Existing methods to consider textual coherence are limited in labeled data. |
| Approach: | They propose a language model-based generative classifier that uses labels as input and embeds labels into their representations. |
| Outcome: | The proposed classifier achieves state-of-the-art in discourse segmentation and relation F1 scores with gold boundaries and automatically segmented boundaries. |
A Unified Linear-Time Framework for Sentence-Level Discourse Parsing (P19-1)
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| Challenge: | a new neural framework for sentence-level discourse analysis is proposed . a discourse segmenter and a parser are based on pointer networks and operate in linear time . |
| Approach: | They propose a neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory . they use a discourse segmenter and a parser to construct a discursive tree in a top-down fashion . |
| Outcome: | The proposed framework surpasses previous approaches on both tasks and human agreement on both. |