| Challenge: | Existing studies on text discourse parsing for English are limited due to the lack of annotated data. |
| Approach: | They propose to use multilingual vector representations and segment-level translation to establish a neural, cross-lingual discourse parser. |
| Outcome: | The proposed model achieves state-of-the-art on cross-lingual, document-level discourse parsing on all sub-tasks. |
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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. |
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| Challenge: | Existing large RST corpora are inconsistent in annotation guidelines, genre representation, source selection, and relation definitions. |
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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 . |
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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. |
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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. |
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Exploring Discourse Structure in Document-level Machine Translation (2023.emnlp-main)
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| Challenge: | Existing methods for document-level machine translation (DocMT) are under-utilizing the context. |
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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. |
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Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing (2024.lrec-main)
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| Challenge: | Existing discourse parsing methods need a strong baseline for reporting reliable experimental results. |
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RST Discourse Parsing with Second-Stage EDU-Level Pre-training (2022.acl-long)
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