| Challenge: | Using BiLSTMs, highway connections, and character-level CNNs, we propose a graph-based Tree Adjoining Grammar (TAG) parser. |
| Approach: | They propose a graph-based Tree Adjoining Grammar parser that uses BiLSTMs, highway connections, and character-level CNNs. |
| Outcome: | The proposed parser outperforms the previously reported best by more than 2.2 LAS and UAS points. |
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| Challenge: | In graph-based dependency parsers, learning representations is gaining in importance, and we use graph neural networks to learn the representations. |
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Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training (2020.aacl-main)
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| Challenge: | Existing approaches to dependency parsing use exact and approximate inference to find the best parse tree. |
| Approach: | They propose a second-order graph-based neural dependency parsing approach using message passing and end-to-end neural networks. |
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A Graph-Based Neural Model for End-to-End Frame Semantic Parsing (2021.emnlp-main)
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| Challenge: | Existing studies focus on frame semantic parsing as a graph construction problem. |
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Parsing linearizations appreciate PoS tags - but some are fussy about errors (2022.aacl-short)
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| Challenge: | Recent work on the impact of PoS tags on graph- and transition-based parsers suggests that they are only useful when tagging accuracy is prohibitively high or in low-resource scenarios. |
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Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing (2022.coling-1)
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| Challenge: | Using end-to-end span-based SRL, we propose a word-based graph parsing task for word-level representation of spans . compared with word-driven SRL, span-Based SRL is more complex due to difficulties in determining argument boundaries. |
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Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing - A Tale of Two Parsers Revisited (D19-1)
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| Challenge: | In recent years, dependency parsing has shifted from discrete features to neural networks and continuous representations. |
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Semantic Dependency Parsing with Edge GNNs (2022.findings-emnlp)
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| Challenge: | Existing semantic dependency parsers use factor graphs to generate a tree structure, but they are ill-suited for a more complex semantic relationship representation. |
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Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference (2020.acl-main)
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Generalized chart constraints for efficient PCFG and TAG parsing (P18-2)
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| Challenge: | Existing pruning techniques limit chart constraints to PCFGs and cannot be applied to more expressive grammars. |
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Stack-Pointer Networks for Dependency Parsing (P18-1)
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| Challenge: | Existing approaches to dependency parsing are local and greedy transitionbased . StackPtr parsers use the information of whole sentences and previously derived subtree structures . |
| Approach: | They propose a stack-pointer network-based dependency parser that reads whole sentence and builds dependency tree top-down in a depth-first fashion. |
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