| Challenge: | Existing neural SRL models lack syntactic backbone for performance, limiting its use in deep learning. |
| Approach: | They propose an enhanced argument labeling model with extended korder argument pruning algorithm for effectively exploiting syntactic information. |
| Outcome: | The proposed model achieves state-of-the-art on the CoNLL-2008 and 2009 benchmarks in English and Chinese. |
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A Unified Syntax-aware Framework for Semantic Role Labeling (D18-1)
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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 . |
| Approach: | They propose to extend existing models to investigate more effective ways of incorporating syntax into sequential neural networks. |
| Outcome: | The proposed framework outperforms existing models on CoNLL-2009 benchmarks in English and Chinese. |
Syntax-driven Approach for Semantic Role Labeling (2022.lrec-1)
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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 . |
| Approach: | They propose to map memories to enhance semantic role labeling by encoding auto-generated syntactic knowledge from off-the-shelf toolkits. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on two English benchmark datasets. |
Syntax-Enhanced Self-Attention-Based Semantic Role Labeling (D19-1)
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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. |
| Approach: | They propose a syntax-enhanced self-attention model that incorporates syntactic knowledge into the SRL task effectively. |
| Outcome: | The proposed model achieves state-of-the-art for the Chinese SRL task on the CoNLL-2009 dataset. |
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 . |
| Outcome: | The proposed model outperforms state-of-the-art syntax-aware SRL systems on CoNLL-2008 and 2009 benchmarks for English and Chinese. |
Syntax-aware Multilingual Semantic Role Labeling (D19-1)
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| Challenge: | Existing work on semantic role labeling (SRL) on English has focused on syntactic integration and enhanced word representation. |
| Approach: | They propose a method guided by syntactic rule to prune arguments to integrate syntax into multilingual SRL model simply and effectively. |
| Outcome: | The proposed model achieves state-of-the-art results on the CoNLL-2009 benchmarks of all seven languages. |
Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures (2022.findings-emnlp)
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| Challenge: | Existing approaches to Semantic Role Labeling rely on discrete labels to classify predicate senses and their arguments. |
| Approach: | They propose a generalized formulation of Semantic Role Labeling that leverages Definition Modeling to describe predicate-argument structures using natural language definitions instead of discrete labels. |
| Outcome: | The proposed model can describe predicate-argument structures using natural language definitions instead of discrete labels. |
Semantic Role Labeling as Syntactic Dependency Parsing (2020.emnlp-main)
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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. |
| Outcome: | The proposed scheme reduces the task of (span-based) PropBank-style semantic role labeling to syntactic dependency parsing. |
Syntax-aware Neural Semantic Role Labeling with Supertags (N19-1)
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| Challenge: | a new syntax-aware model for dependency-based semantic role labeling outperforms syntax-based models for English and Spanish. |
| Approach: | They propose a syntax-aware model for dependency-based semantic role labeling that outperforms syntax-based models for English and Spanish. |
| Outcome: | The proposed model outperforms syntax-agnostic models for English and Spanish. |
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. |
How to Best Use Syntax in Semantic Role Labelling (P19-1)
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| Challenge: | Existing studies on integrating external information into NLP tasks focus on word-level shallow features such as POS or chunk tags. |
| Approach: | They propose to integrate syntactic information into a neural ELMo-based SRL sequence labelling model by using a constituency representation as input features. |
| Outcome: | The proposed approach improves performance on the in-domain CoNLL’05 and CoNll’12 benchmarks. |