| Challenge: | Various linearizations have been proposed to cast syntactic dependency parsing as sequence labeling, but they cannot handle reentrancy or cycles. |
| Approach: | They propose unbounded linearizations that can be used to cast dependency parsing as sequence labeling. |
| Outcome: | The proposed linearizations can cast syntactic dependency parsing as a sequence labeling task. |
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| Challenge: | Existing work on dependency parsing by sequence labeling suggested that it was impractical. |
| Approach: | They propose to use dependency trees as sequence labels to obtain fast and accurate parsers using a conventional BILSTM-based model. |
| Outcome: | The proposed models are conceptually simple, not needing traditional parsing algorithms or auxiliary structures, and provide a good speed-accuracy tradeoff, with results competitive with more complex approaches. |
Hierarchical Bracketing Encodings Work for Dependency Graphs (2025.emnlp-main)
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| Challenge: | Sequence labeling (SL) is a simple yet effective paradigm for a wide range of natural language problems. |
| Approach: | They propose a new bracketing approach for dependency graph parsing that encodes graphs as sequences and n tagging actions. |
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| Challenge: | Existing paradigms for semantic parsing are sequence-to-sequence and AMR parsers. |
| Approach: | They propose to formulate parsing as a sequence-to-sequence task using graph-based decoding techniques developed for syntactic parsers. |
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Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations (2022.coling-1)
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| Challenge: | Existing semantic parsers are based on deep learning, but rule-based approaches offer advantages . a drawback of neural semantic parses is that their output lacks explainability . |
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Graph-based Dependency Parsing with Graph Neural Networks (P19-1)
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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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Simpler but More Accurate Semantic Dependency Parsing (P18-2)
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| Challenge: | Syntactic dependency parsing is the most popular method for automatically extracting low-level relationships between words in a sentence. |
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| Challenge: | Existing parsers that read sentences from left to right are not learning to parse them. |
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Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information. |
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Sequence Labeling Parsing by Learning across Representations (P19-1)
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Dependency Parsing via Sequence Generation (2022.findings-emnlp)
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| Challenge: | Existing methods for dependency parsing are transition-based, graph-based and sequence-to-sequence method. |
| Approach: | They propose to achieve dependency parsing (DP) via Sequence Generation (SG) by utilizing only the pre-trained language model without any auxiliary structures. |
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