| Challenge: | Existing bracketing-based encodings only handle a mild extension of projective trees . encodes that encode arcs in a given plane provide almost total coverage of crossing arc . |
| Approach: | They propose a bracketing-based encoding that can be used to represent any 2-planar dependency tree over a sentence of length n as a sequence of n labels. |
| Outcome: | The proposed method improves over existing bracketing encodings in non-projective treebanks while achieving similar speed. |
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Hierarchical Bracketing Encodings for Dependency Parsing as Tagging (2025.acl-long)
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| Challenge: | Existing encodings for dependency parsing use suboptimal number of labels and a limited number of symbols. |
| Approach: | They propose a family of encodings for sequence labeling dependency parsing based on hierarchical bracketing . they propose an optimal hierarchically bracketing which minimizes the number of symbols used and encodes projective trees using only 12 distinct labels . |
| Outcome: | The proposed encodings yield competitive accuracy on a diverse set of treebanks. |
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. |
| Outcome: | The proposed approach significantly reduces label space while preserving structural information. |
4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees (2023.emnlp-main)
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| Challenge: | encodings that map trees into sequences of one discrete label per word have been proposed for constituency parsing and dependency parse. |
| Approach: | They propose an encoding that can represent any projective dependency tree as a sequence of 4-bit labels, one per word. |
| Outcome: | The proposed encoding achieves substantial accuracy gains over the previously best-performing sequence labeling encoders. |
Viable Dependency Parsing as Sequence Labeling (N19-1)
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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. |
Dependency Graph Parsing as Sequence Labeling (2024.emnlp-main)
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| 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. |
Constituent Parsing as Sequence Labeling (D18-1)
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| Challenge: | Constituent parsing is a core problem in NLP where the goal is to obtain the syntactic structure of sentences expressed as a phrase structure tree. |
| Approach: | They propose a method to reduce constituent parsing to sequence labeling by using a tree with unary branches. |
| Outcome: | The proposed method outperforms the Vinyals et al. (2015) sequence-to-sequence parser by 90% on the PTB and CTB treebanks. |
Non-Projective Dependency Parsing with Non-Local Transitions (N18-2)
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| Challenge: | Existing greedy transition-based parsers are prone to error propagation when creating arcs involving multiple transitions. |
| Approach: | They propose a greedy transition-based parser that introduces non-local transitions that create arcs involving nodes to the left of the current focus positions. |
| Outcome: | The proposed system outperforms the original version and achieves the best accuracy on the Stanford Dependencies conversion of the Penn Treebank among greedy transition-based parsers. |
A Unifying Theory of Transition-based and Sequence Labeling Parsing (2020.coling-main)
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| Challenge: | Existing parsers that read sentences from left to right are not learning to parse them. |
| Approach: | They propose a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees. |
| Outcome: | The proposed algorithms are learnable and comparable to existing encodings. |
A Dynamic Oracle for Linear-Time 2-Planar Dependency Parsing (N18-2)
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| Challenge: | Existing dynamic oracles for greedy parsers can handle non-projective syntax, but none are available for these types of training. |
| Approach: | They propose an efficient dynamic oracle for training the 2-Planar transition-based parser with over 99% coverage on non-projective syntactic corpora. |
| Outcome: | The proposed model outperforms the static training strategy in the vast majority of languages tested and scored better on most datasets than the arc-hybrid parser enhanced with the Swap transition. |
Hexatagging: Projective Dependency Parsing as Tagging (2023.acl-short)
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| Challenge: | Using a pretrained language model, we can train language models on increasingly large amounts of data. |
| Approach: | They propose a dependency parser that constructs dependency trees by tagging words with elements from a finite set of possible tags. |
| Outcome: | The proposed approach achieves state-of-the-art performance of 96.4 LAS and 97.4 UAS on the Penn Treebank test set. |