| 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. |
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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. |
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. |
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. |
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. |
Iterative Span Selection: Self-Emergence of Resolving Orders in Semantic Role Labeling (2022.coling-1)
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| Challenge: | Semantic role labeling is the task of labeling semantic arguments for marked semantic predicates. |
| Approach: | They propose a model which combines global decoding and iterative identification for the semantic arguments to consider their roles and relations in the labeling order. |
| Outcome: | The proposed model outperforms existing models in the benchmark datasets of span-based SRL: CoNLL-2005 and CoNll-2012. |
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. |
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. |
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. |
Span-based Semantic Role Labeling as Lexicalized Constituency Tree Parsing (2025.findings-acl)
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| Challenge: | Existing models for semantic role labeling fail to capture the relationship between syntax and semantics. |
| Approach: | They propose a lexicalized tree representation for span-based SRL that integrates constituency and dependency parsing to explicitly model predicate-argument structures. |
| Outcome: | The proposed model achieves competitive performance on standard English benchmarks. |
Alignment-free Cross-lingual Semantic Role Labeling (2020.emnlp-main)
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| Challenge: | Existing approaches to semantic role labeling rely on word alignments, translation engines or preprocessing tools. |
| Approach: | They propose a cross-lingual semantic role labeling model which only requires annotations in a source language and access to raw text in . |
| Outcome: | The proposed model minimizes the effort required to construct annotations or models for a new target language. |