| Challenge: | Existing work on semantic role labels ignores the semantic connection between the two tasks . et al. (2010) defined two types of semantic roles: core roles and non-core roles. |
| Approach: | They propose to use machine reading comprehension to bridge the gap between these two tasks . they formalize predicate disambiguation as multiple-choice machine reading understanding . |
| Outcome: | The proposed framework achieves state-of-the-art or comparable results to previous work . it uses the descriptions of candidate senses of a given predicate as options to select the correct sense . |
Similar Papers
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. |
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. |
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. |
Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling (N19-1)
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| Challenge: | Existing work on opinion role labeling (ORL) is highly correlative with semantic role labeled (SRL) SRL is used to identify opinion holders and holder expressions for a given predicate. |
| Approach: | They propose a method to enhance opinion role labeling by presenting semantic-aware word representations which are learned from SRL. |
| Outcome: | The proposed method outperforms two other methods on a benchmark MPQA corpus and achieves higher F scores. |
SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling (N18-1)
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| Challenge: | Recent neural approaches do not outperform the state-of-the-art feature-based models for Opinion Role Labeling (ORL). |
| Approach: | They propose to use multi-task learning to improve Opinion Role Labeling by using a related task which has substantially more data. |
| Outcome: | The proposed model outperforms the state-of-the-art model for Opinion Role Labeling (ORL) with more data. |
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. |
| 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. |
Using Semantic Role Labeling to Improve Neural Machine Translation (2022.lrec-1)
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| Challenge: | despite progress in machine translation, some form of language understanding may be desirable . current systems rely on pattern recognition, but some form may be useful . |
| Approach: | They use semantic role labeling to annotate a standard parallel corpus with semantic roles . they then train a neural machine translation system using the annotated corpus and original unannotated text . |
| Outcome: | The proposed system improves BLEU scores for English, French, German, Greek and Spanish. |
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. |
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. |