Chinese Paragraph-level Discourse Parsing with Global Backward and Local Reverse Reading (2020.coling-main)
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| Challenge: | Existing methods on discourse parsing in English suffer from long discourse units and fewer explicit connectives. |
| Approach: | They propose to use two reading modes to construct Chinese paragraph level discourse trees. |
| Outcome: | The proposed model outperforms baselines on Chinese discourse trees. |
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| Challenge: | Compared with sentence-level topic structure, paragraph-level topics can grasp and understand the context of a document from a higher level. |
| Approach: | They propose a hierarchical paragraph-level topic structure representation with three layers to guide corpus construction. |
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Chinese Discourse Parsing: Model and Evaluation (2020.lrec-1)
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| Challenge: | Chinese discourse parsing has not yet a consistent evaluation metric . micro vs. macro F1 scores, binary v. multiway ground truth, and left-heavy v . right-heaviness binarization are important for Chinese discourses . |
| Approach: | They propose a neural network model that unifies a pre-trained transformer and a CKY-like algorithm and compare it with previous models with different evaluation scenarios. |
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A Unified RvNN Framework for End-to-End Chinese Discourse Parsing (C18-2)
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| Challenge: | Existing work on Chinese discourse parser relies on external packages to extract linguistic features from free text. |
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MCDTB: A Macro-level Chinese Discourse TreeBank (C18-1)
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| Challenge: | Discourse analysis is becoming increasingly important in the field of natural language processing. |
| Approach: | They propose to annotate macro discourse information and additional discourse information to make annotation more objective and accurate. |
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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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On the Branching Bias of Syntax Extracted from Pre-trained Language Models (2020.findings-emnlp)
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| Challenge: | Existing methods for extracting constituency trees from language models suffer from branching bias. |
| Approach: | They propose to measure the branching bias by comparing the performance gap on a language and its reversed language. |
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Discourse Parsing Enhanced by Discourse Dependence Perception (2022.aacl-main)
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| Challenge: | Top-down neural models still suffer from the top-down error propagation issue . previous studies gradually switch from feature-based machine learning methods to deep neural models . |
| Approach: | They propose a top-down framework that learns from discourse dependency and constituency parsing through one shared encoder and two independent decoders. |
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Joint Modeling of Structure Identification and Nuclearity Recognition in Macro Chinese Discourse Treebank (C18-1)
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| Challenge: | Discourse parsing is a challenging task and plays a critical role in discourse analysis. |
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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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Input Representations for Parsing Discourse Representation Structures: Comparing English with Chinese (2021.acl-short)
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| Challenge: | Neural semantic parsers have obtained acceptable results in parsing DRSs . previous studies have focused on parse of DRS in English, but have focused only on a few languages . |
| Approach: | They propose to use character sequences as input to map meaning representations to string format. |
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