Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse (C18-1)
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| Challenge: | Experimental results show that nuclearity recognition is a challenging task in Chinese discourse parsing due to the need for more deep semantic information. |
| Approach: | They propose a text matching network that encodes discourse units and paragraphs by combining Bi-LSTM and CNN to capture global dependency information and local n-gram information. |
| Outcome: | The proposed model outperforms baselines on the Chinese Discourse TreeBank . the proposed model is based on a novel text matching network . |
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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. |
| Approach: | They propose a macro discourse structure presentation schema to present the macro level discourse structure analysis. |
| Outcome: | The proposed corpus is based on two tasks of macro discourse structure analysis, including structure identification and nuclearity recognition. |
Neural Graph Matching Networks for Chinese Short Text Matching (2020.acl-main)
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| Challenge: | Chinese word segmentation can be erroneous, ambiguous or inconsistent, causing performance problems. |
| Approach: | They propose a sentence matching framework that uses paired word lattices as input instead of a character sequence. |
| Outcome: | The proposed framework outperforms the state-of-the-art short text matching models on two Chinese datasets. |
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. |
| Approach: | They propose an end-to-end Chinese discourse parser based on recursive neural network to jointly model the subtasks including elementary discourse unit segmentation, tree structure construction, center labeling, and sense labeling. |
| Outcome: | The proposed framework achieves state-of-the-art in the Chinese Discourse Treebank dataset. |
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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Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese (P19-1)
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| Challenge: | Currently, most studies on implicit discourse relation recognition use sentence-level representations . Chinese is a paratactic language that tends to pro-drop clause connectives . |
| Approach: | They propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. |
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State-of-the-art Chinese Word Segmentation with Bi-LSTMs (D18-1)
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| Challenge: | A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. |
| Approach: | They propose a bidirectional LSTM model with standard deep learning techniques and best practices for the task of Chinese word segmentation. |
| Outcome: | The proposed model outperforms models based on standard deep learning techniques and best practices on Chinese word segmentation datasets. |
CNM: An Interpretable Complex-valued Network for Matching (N19-1)
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| Challenge: | Existing work on quantum physics models language understanding using quantum probability . |
| Approach: | They propose a quantum-theoretic framework that unifies different linguistic units in a single complex-valued vector space and a complex-valuable network for semantic matching. |
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Extractive Summarization as Text Matching (2020.acl-main)
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| Challenge: | Currently, most of the neural extractive summarization systems score and extract sentences individually and model the relationship between sentences. |
| Approach: | They propose to instantiate a neural extractive summarization task as a semantic text matching problem and use it to match a source document and candidate summaries in a semantic space. |
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A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing (D19-1)
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| Challenge: | Existing approaches to discourse parsing use commonsense knowledge and linguistic constraints to integrate them into neural network models. |
| Approach: | They propose a knowledge regularization approach that integrates linguistic constraints with contexts for deriving word representations. |
| Outcome: | The proposed approach outperforms previous systems on the benchmark dataset PDTB for discourse parsing. |