| Challenge: | Experimental results show that visual semantic role labeling is useful for text understanding . image-based role annotations are prohibitive, but the model induces frame-semantic visual representations . |
| Approach: | They propose to train a visual semantic role labeling model without prohibitive image annotations . they render candidate participants as image regions of objects and train vSRL model which learns to ground roles in the regions which depict the corresponding participant . |
| Outcome: | The proposed model trains without prohibitive image-based role annotations without prohibiting image-related annotations. |
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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. |
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. |
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. |
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. |
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. |
Label Definitions Improve Semantic Role Labeling (2022.naacl-main)
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| Challenge: | Existing work on semantic role labeling treats symbolic labels as symbolic . labeled data is costly and often lacking in many tasks, domains, and languages. |
| Approach: | They propose to retrieve and leverage semantic role labels from annotation guidelines . argument classification is at the core of Semantic Role Labeling . |
| Outcome: | The proposed model achieves state-of-the-art on a CoNLL09 dataset injected with label definitions given the predicate senses. |
What Does BERT with Vision Look At? (2020.acl-main)
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| Challenge: | Pre-trained visual grounded language models have improved performance on vision-and-language tasks but what they learn during pre-training remains unclear. |
| Approach: | They show that attention heads of visual grounded language models actively ground elements of language to image regions. |
| Outcome: | The attention heads of a visual grounded language model can ground elements to image regions, demonstrating their ability to detect syntactic relations between non-entity words and image regions. |
Incorporating Visual Semantics into Sentence Representations within a Grounded Space (D19-1)
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| Challenge: | Language grounding is an active field aiming at enriching textual representations with visual information. |
| Approach: | They propose to transfer visual information to textual representations by learning an intermediate representation space: the grounded space. |
| Outcome: | The proposed model outperforms the previous state-of-the-art on classification and semantic relatedness tasks. |