Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks (D19-1)
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| Challenge: | State-of-the-art SRL models do not model non-local interaction between arguments . e.g., LSTMs do not allow for efficient inference . |
| Approach: | They propose a new approach to model interactions between arguments using capsule networks . they analyze errors in the refinement procedure by capturing intuition in a flexible way . |
| Outcome: | The proposed model outperforms the baseline model on all 7 languages and achieves state-of-the-art results on 5 languages including English. |
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Semantic Role Labeling with Iterative Structure Refinement (D19-1)
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| Challenge: | Modern state-of-the-art methods for semantic role labeling model only local interactions between individual labels . |
| Approach: | They propose to model local interactions between argument labeling decisions using a refinement network instead of arbitrary interactions between roles and words. |
| Outcome: | The proposed model outperforms baseline models on all 7 languages and achieves state-of-the-art results on 5 languages, including English. |
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. |
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. |
LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models (2025.findings-acl)
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| Challenge: | Semantic role labeling (SRL) is a crucial task of natural language processing (NLP). |
| Approach: | They propose to equip LLMs with retrieval-augmented generation and self-correction mechanisms to enable SRL to perform better in Chinese and English. |
| Outcome: | The proposed method achieves state-of-the-art in Chinese and English on three widely-used benchmarks. |
Semantic Role Labeling with Associated Memory Network (N19-1)
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| Challenge: | Existing work on semantic role labeling has been focused on using deep learning methods to solve the task. |
| Approach: | They propose a syntax-agnostic SRL model enhanced by the proposed associated memory network which makes use of inter-sentence attention of label-known associated sentences as a kind of memory to further enhance dependency-based SRL. |
| Outcome: | The proposed model achieves state-of-the-art on CoNLL-2009 benchmark datasets showing that it is not dependent on external resources. |
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. |
Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities (2023.findings-acl)
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| Challenge: | Existing systems for SRL are incapable of transferring knowledge across different predicate types. |
| Approach: | They propose a new PropBank dataset which boasts wide coverage of multiple predicate types and a manually-annotated challenge set which gives equal importance to verbal, nominal, and adjectival predicates. |
| Outcome: | The proposed dataset shows that standard benchmarks do not provide an accurate picture of the current situation in SRL and that state-of-the-art systems are still incapable of transferring knowledge across different predicate types. |
A Span Selection Model for Semantic Role Labeling (D18-1)
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| Challenge: | Existing models for semantic role labeling use BIO tags to predict argument spans . but performance of these approaches is weak . |
| Approach: | They propose a span-based model that takes into account all possible argument spans and scores them for each label. |
| Outcome: | The proposed model achieves state-of-the-art results on the CoNLL-2005 and 2012 datasets. |
Probing for Predicate Argument Structures in Pretrained Language Models (2022.acl-long)
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| Challenge: | Recent proposed approaches have achieved impressive results in dependency- and span-based, multilingual and cross-lingual Semantic Role Labeling (SRL) |
| Approach: | They propose to probe for predicate argument structures in pretrained language models . they show that PLMs encode semantic structures directly into contextualized representations . |
| Outcome: | The proposed models have achieved impressive results in dependency- and span-based, multilingual and cross-lingual Semantic Role Labeling (SRL) |
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. |
| Approach: | They propose to regard flat argument spans as latent subtrees, thus reducing SRL to a tree parsing task. |
| Outcome: | The proposed model performs better than previous syntax-agnostic models on CoNLL05 and CoNll12 benchmarks. |