| 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. |
| Outcome: | The proposed method performs well on DP benchmarks including PTB, UD2.2, SDP15 and SemEval16. |
Similar Papers
Dependency Graph Parsing as Sequence Labeling (2024.emnlp-main)
Copied to clipboard
| 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. |
Viable Dependency Parsing as Sequence Labeling (N19-1)
Copied to clipboard
| 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. |
Enhancing Discourse Dependency Parsing with Sentence Dependency Parsing: A Unified Generative Method Based on Code Representation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing annotation resources for Discourse Dependency Parsing tasks are limited due to their complexity and annotation schema differences. |
| Approach: | They propose a code-based unified dependency parsing method that uses code to model dependency parses under different annotation schemas. |
| Outcome: | The proposed method improves on two Chinese DDP tasks. |
Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing (2023.tacl-1)
Copied to clipboard
| Challenge: | Sequence-to-Sequence (S2S) models have been successful on text generation tasks . however, learning complex structures with S2S models remains challenging . |
| Approach: | They propose to use constrained decoding to model part-of-speech tagging, named entity recognition, constituency, and dependency parsing tasks with 3 lexically diverse linearization schemas and corresponding constrained coding methods. |
| Outcome: | The proposed methods outperform the state-of-the-art on four core tasks. |
Parsing as Tagging (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for dependency parsing treat parse as tagging, but they are not perfect. |
| Approach: | They propose a simple yet accurate method that treats parsing as tagging . they use a sequence model with a bidirectional LSTM over BERT embeddings . |
| Outcome: | The proposed method outperforms the state-of-the-art method on universal dependency (UD) by 1.76% unlabeled attachment score (UAS) for English, 1.98% UAS for French, and 1.16% UAS in German. |
Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks (2022.findings-naacl)
Copied to clipboard
| Challenge: | Code-switching dependency parsing is a challenging task due to the scarcity of necessary resources and structural difficulties embedded in code-switch languages. |
| Approach: | They propose to use sequence labeling models as auxiliary tasks for code-switched dependency parsing in a semi-supervised scheme and acquire state-of-the-art scores on all studied languages. |
| Outcome: | The proposed model outperforms the previous model by 7.4 LAS points on average on all of the studied languages. |
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing (P18-1)
Copied to clipboard
| Challenge: | Existing methods for semantic parsing are difficult to design and learn, especially in wideopen domains. |
| Approach: | They propose a neural semantic parsing approach which models semantic par- sing as an end-to-end semantic graph generation process. |
| Outcome: | The proposed model achieves state-of-the-art performance on Overnight dataset and gets competitive performance on Geo and Atis datasets. |
Semantics as a Foreign Language (D18-1)
Copied to clipboard
| Challenge: | (2017): Syntactic grammars capture propositions, but graph-based representations aim to capture a wider notion of propositions. |
| Approach: | They propose a neural sequence-to-sequence framework which can recover syntactic linearizations by a sequence-based approach. |
| Outcome: | The proposed framework performs almost on-par with previous state-of-the-art approaches while requiring less parallel training annotations. |
Sequence Labeling Parsing by Learning across Representations (P19-1)
Copied to clipboard
| Challenge: | Constituency and dependency parsing are the main abstractions for representing syntactic structure of sentences . constituency parsers are considered disjointed tasks, and their improvements have been obtained separately. |
| Approach: | They propose to add auxiliary loss to constituency parsing paradigms and explore a model that parses both paradigms at no cost. |
| Outcome: | The proposed model outperforms single-task models by 1.05 F1 points and 0.62 UAS points for constituency parsing and dependency parsers. |
A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing parsers that capture dependency graphs are lacking in capturing explicit dependencies . graph-based parsing is a popular choice for capturing dependency relationships between words . |
| Approach: | They propose a semi-autoregressive dependency parser that generates dependency graphs by adding nodes and edge groups autoregressively while pouring out all group elements in parallel. |
| Outcome: | The proposed method outperforms baselines on Enhanced Universal Dependencies of multiple languages. |