| 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. |
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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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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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Multilingual Neural RST Discourse Parsing (2020.coling-main)
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| Challenge: | Existing studies on text discourse parsing for English are limited due to the lack of annotated data. |
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| Challenge: | Fig. 1 shows the video's story structure and event relationships in discourse parsing. |
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RST Parsing from Scratch (2021.naacl-main)
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| Challenge: | Fig. 1 shows a document level discourse parser that performs top-down end-to-end parsing without requiring segmentation . |
| Approach: | They propose a top-down end-to-end formulation of document level discourse parsing in the Rhetorical Structure Theory framework. |
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A Top-down Neural Architecture towards Text-level Parsing of Discourse Rhetorical Structure (2020.acl-main)
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| Challenge: | Text-level discourse parsing of discourse rhetorical structure (DRS) is a fundamental research topic in natural language processing. |
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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. |
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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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| Challenge: | Existing pre-trained language models (PLMs) are based on sentence-level pre-training, which is different from the basic processing unit, i.e. element discourse unit (EDU). |
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Modeling discourse cohesion for discourse parsing via memory network (P18-2)
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| Challenge: | Existing approaches to discourse parsing focus on studying the semantic and syntactic aspects of EDU pairs, but they do not address long span dependencies. |
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