A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling (D19-1)
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| Challenge: | Semantic role labeling (SRL) aims to identify the predicate-argument structure of a sentence. |
| Approach: | They propose to use a unified span-based model for Chinese SRL as a strong baseline. |
| Outcome: | The proposed framework achieves state-of-the-art 87.54 and 88.5 F1 scores on the Chinese Proposition Bank and CoNLL-2009 datasets. |
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| Challenge: | Syntactic information has been paid a great attention over the role of enhancing SRL . but the gap between syntax-aware and syntax-gnostic SRL is smaller . a new framework proposes syntax-based SRL for a wide range of NLP tasks . |
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| Challenge: | Using propBank-style semantic role labeling, we reduce the task to syntactic dependency parsing. |
| Approach: | They propose to convert SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format. |
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| Challenge: | Existing studies focus on auto-generated syntactic knowledge to enhance semantic role labeling . experimental results show that map memories can enhance SRL . |
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| Challenge: | Abstract: Syntax is the bridge to semantics, but recent studies have discussed the necessity of syntax in the context of SRL. |
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| Challenge: | Existing models for semantic role labeling fail to capture the relationship between syntax and semantics. |
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| Challenge: | Existing studies use only one singleton syntax for semantic role labeling (SRL). |
| Approach: | They propose a TreeLSTM-based integration that integrates phrasal boundaries and semantic relations from dependency into a labelaware GCN solution for simultaneously modeling syntactic edges and labels. |
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Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures inside Arguments (2022.coling-1)
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| Challenge: | Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. |
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Syntax for Semantic Role Labeling, To Be, Or Not To Be (P18-1)
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| Challenge: | Existing neural SRL models lack syntactic backbone for performance, limiting its use in deep learning. |
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Learning Semantic Role Labeling from Compatible Label Sequences (2023.findings-emnlp)
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| Challenge: | Prior work has shown that cross-task interaction helps, but only explored multitask learning so far. |
| Approach: | They propose a framework that jointly models VerbNet and PropBank labels as one sequence and enforcing Semlink constraints during decoding improves the overall F1 . |
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A Full End-to-End Semantic Role Labeler, Syntactic-agnostic Over Syntactic-aware? (C18-1)
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| Challenge: | Existing models for semantic role labeling are syntax-agnostic, but outperform them on benchmarks. |
| Approach: | They propose an end-to-end neural model which tackles the SRL problem in one shot . they augment the encoder with a non-linear transformation to distinguish the predicate and the argument . |
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